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Problem‑Solving Using Evidence and Critical Thinking Cornell Course

Select start date, problem-solving using evidence and critical thinking, course overview.

Have you ever known a very intelligent person who made a very bad decision? If so, you know that having a high IQ does not guarantee that you automatically make critically thoughtful decisions. Critically thoughtful problem-solving is a discipline and a skill—one that allows you to make decisions that are the product of careful thought, and the results of those decisions help your team and organization thrive.

In this course you will practice a disciplined, systematic approach to problem solving that helps ensure that your analysis of a problem is comprehensive, is based on quality, credible evidence, and takes full and fair account of the most probable counterarguments and risks. The result of this technique is a thoroughly defensible assessment of what the problem is, what is causing it, and the most effective plan of action to address it. Finally, you will identify and frame a problem by assessing its context and develop a well-reasoned and implementable solution that addresses the underlying causes.

Key Course Takeaways

• Assess the context of the problem
• Determine the current and desired states and confirm this with decision makers
• Identify and articulate the questions that must be answered to bridge the gap between current state and desired future state
• Determine root causes and distinguish symptoms from problems
• Brainstorm a range of possible solutions to address each significant underlying cause
• Assess each option for the extent to which it bridges the gap between current and future state, whether it is implementable, and if it is ethical

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Risa Mish is professor of practice of management at the Johnson Graduate School of Management. She designed and teaches the MBA Core course in Critical and Strategic Thinking, in addition to teaching courses in leadership and serving as faculty co-director of the Johnson Leadership Fellows program.

She has been the recipient of the MBA Core Faculty Teaching Award, selected by the residential program MBA class to honor the teacher who “best fosters learning through lecture, discussion and course work in the required core curriculum”; the Apple Award for Teaching Excellence, selected by the MBA graduating classes to honor a faculty member who “exemplifies outstanding leadership and enduring educational influence”; the “Best Teacher Award”, selected by the graduating class of the Cornell-Tsinghua dual degree MBA/FMBA program offered by Johnson at Cornell and the PBC School of Finance at Tsinghua University; the Stephen Russell Distinguished Teaching Award, selected by the five-year MBA reunion class to honor a faculty member whose “teaching and example have continued to influence graduates five years into their post-MBA careers”; and the Globe Award for Teaching Excellence, selected by the Executive MBA graduating class to honor a faculty member who “demonstrates a command of subject matter and also possesses the creativity, dedication, and enthusiasm essential to meet the unique challenges of an EMBA education.”

Mish serves as a keynote speaker and workshop leader at global, national, and regional conferences for corporations and trade associations in the consumer products, financial services, health care, high tech, media, and manufacturing industries, on a variety of topics, including critical thinking and problem solving, persuasion and influence, and motivating optimal employee performance. Before returning to Cornell, Mish was a partner in the New York City law firm of Collazo Carling & Mish LLP (now Collazo Florentino & Keil LLP), where she represented management clients on a wide range of labor and employment law matters, including defense of employment discrimination claims in federal and state courts and administrative agencies, and in labor arbitrations and negotiations under collective bargaining agreements. Prior to CC&M, Mish was a labor and employment law associate with Simpson Thacher & Bartlett in New York City, where she represented Fortune 500 clients in the financial services, consumer products, and manufacturing industries. She is admitted to practice before the U.S. Supreme Court and state and federal courts in New York and Massachusetts.

Mish is a member of the board of directors of SmithBucklin Corporation, the world’s largest trade association management company, headquartered in Chicago and TheraCare Corporation, headquartered in New York City. She formerly served as a Trustee of the Tompkins County Public Library, Vice Chair of the board of directors of the Community Foundation of Tompkins County, and member of the board of directors of the United Way of Tompkins County.

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How to Apply Evidence-Based Problem Solving to Improve the Outcomes of Your Projects

I’ve said it before: I’m not a fan of the expression “data-driven organization. While we often hear that data-driven corporations outperform their peers , it’s a mistake to think that simply investing in data infrastructure and data acquisition will improve the bottom line. As I noted in a previous article, many data-driven companies end up data-rich but insight-poor .

And the same is true in business analysis. A substantial investment in acquiring and crunching data will not ensure that the business problem will be fully understood, the constraints involved will be properly identified, and we’ll be able to build a solution that is desirable, viable, and usable.

The reason why being data-driven can be problematic is simple:

Data is often presented in ways that fool us into thinking we know things that aren't true.

Consider this illustrative example from Twitter. (I decided to redact the name after realizing this wasn’t a joke, but rather one of repeated instances of the same account misinterpreting data.)

Why no interest in long COVID in Japan or Sweden or Italy or Brazil? Anyone capable of thinking analytically will arrive at a logical explanation for this unsurprising “trend”. First, even the author was able to notice that those countries are not part of the “Anglosphere.” If we wanted to analyze the online search interest for “long COVID” in non-English speaking countries, we’d have to combine the searches in English with searches using their local languages (for example, “COVID longa” in Brazilian Portuguese).

Second, what evidence do we have that Google search volume is a good proxy for interest in a topic in every country? For instance, many of my friends in Brazil use Ecosia instead of Google so that their online searches can contribute to planting trees in the country to protect its endangered plants and animals. And it’s quite possible that there, and in many other countries, people prefer to rely on newsletters like Your Local Epidemiologist to receive science-backed information about the pandemic among other public health topics, rather than risking being misinformed by the results of a search engine.

The reality is that data without analytical thinking can be as bad or worse than relying only on our intuition. Measuring things and gathering data is an excellent approach to reduce uncertainty and find patterns that result in new opportunities and better solutions to existing problems—but only if we understand the components of the evidence-based approach to problem solving.

The core elements of evidence-based problem solving

1. first principles thinking.

First principles reasoning , the topic of my previous article, is where it all starts. What are we absolutely sure is true? What has been treated as truth without being proven? Only when we follow this line of questioning do we avoid the risk of making incorrect decisions based on ambiguous data or biased opinions.

2. Hypothesis testing

When we’re trying to solve a problem, some assumptions will represent core beliefs that must be true for our solution to succeed. For example, in a software development project, a core assumption is that the stakeholders have used objective evidence to reach the conclusion that building a custom solution is better than trying to mold an off-the-shelf application to the organization’s needs.

But not all project assumptions should be treated as a given. The biggest enemy of successful problem solving is confirmation bias: our tendency to pay attention to information that confirms our beliefs and ignore or downplay facts that contradict our ideas. To avoid missing key pieces of information, or interpreting subjective information in a way that favors what we want to believe, we must treat our unproven assumptions as hypotheses to be tested–and, when possible, falsified.

Imagine that you work for a B2B company that sells a content management solution. The sales team has been asking for a collaborative editing feature to allow users other than the document creator to make edits. If you only look for supporting evidence, you’re likely to find “proof” that the feature is going to be popular with the user base.

But if the reason for building the feature is to help the business retain customers, collecting more data may help you falsify this hypothesis. Maybe customers are only interested in the capability if it can log the editor's name, and due to technical constraints, the proposed solution won’t allow that. Or, none of the customers threatening to leave for a competitor are interested in the collaborative edit feature, and delivering the capability will not increase the retention rate.

Hypothesis testing doesn’t need to be a costly or lengthy process. In some cases, the information required to prove or disprove a hypothesis is already available (for example, data from a customer survey). In other cases, the process may consist of simply writing down your hypothesis, finding people to talk to, figuring out what you need to learn, devising questions to get you there, and scheduling your interviews.

If, while looking for evidence that validates or invalidates your hypothesis, you end up falsifying it, that’s a reason to celebrate. By analyzing the merit of proposed ideas and rejecting the ones that can’t produce real value, the business can free scarce resources to work on more promising initiatives.

3. Information value analysis

In every project, there will always be a degree of uncertainty. When building a new feature for a customer-facing product, how can we be sure that offering what the users are asking for will prevent them from leaving if a competitor creates a better alternative at a lower price? When replacing the company’s CRM tool, how can we know if the new vendor’s promises to close some capability gaps will actually fulfill the stated business needs?

In reality, if we try to test every possible hypothesis, gather evidence for even the most basic assumption, and weigh every possible outcome, we risk wasting time or getting into analysis paralysis. 

That's why we need to pay attention to the expected value of more information when looking for evidence to support project decisions. If we're fairly certain that the new CRM tool is the best alternative despite some capability gaps, the cost of more information is unlikely to justify seeking additional evidence to support the choice. On the other hand, if being wrong about a feature's ability to increase customer retention could have a sizable impact on the bottom line, the risk reduction benefits of more information may very well justify the extra time and effort to investigate further.

Being “data-driven” doesn’t help create project success; being evidence-based does.

Evidence-based problem solving reduces the risk of blind spots and confirmation bias and increases the chances of achieving the desired outcomes. In high-stakes projects, risks can be dramatically reduced when a business analyst is willing to apply first principles thinking, hypothesis testing, and information value analysis to integrate the best evidence into the decision-making process.

Author:  Adriana Beal  

Adriana Beal has been working as a data scientist since 2016. Her educational background includes graduate degrees in Electrical Engineering and Strategic Management of Information obtained from top schools in her native country, Brazil and certificates in Big Data and Data Analytics from the University of Texas and Machine Learning Specialty from AWS. Over the past five years, she has developed predictive models to improve outcomes in healthcare, mobility, IoT, customer science, human services, and agriculture. Prior to that she worked for more than a decade in business analysis and product management helping U.S. Fortune 500 companies and high tech startups make better software decisions. Adriana has two IT strategy books published in Brazil and work internationally published by IEEE and IGI Global. You can find more of her useful advice for business analysts at bealprojects.com

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Adopting the right problem-solving approach

May 4, 2023 You’ve defined your problem, ensured stakeholders are aligned, and are ready to bring the right problem-solving approach and focus to the situation to find an optimal solution. But what is the right problem-solving approach? And what if there is no single ideal course of action? In our 2013 classic  from the Quarterly , senior partner Olivier Leclerc  highlights the value of taking a number of different approaches simultaneously to solve difficult problems. Read on to discover the five flexons, or problem-solving languages, that can be applied to the same problem to generate richer insights and more innovative solutions. Then check out more insights on problem-solving approaches, and dive into examples of pressing challenges organizations are contending with now.

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• Published: 11 January 2023

The effectiveness of collaborative problem solving in promoting students’ critical thinking: A meta-analysis based on empirical literature

• Enwei Xu   ORCID: orcid.org/0000-0001-6424-8169 1 ,
• Wei Wang 1 &
• Qingxia Wang 1  

Humanities and Social Sciences Communications volume  10 , Article number:  16 ( 2023 ) Cite this article

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Collaborative problem-solving has been widely embraced in the classroom instruction of critical thinking, which is regarded as the core of curriculum reform based on key competencies in the field of education as well as a key competence for learners in the 21st century. However, the effectiveness of collaborative problem-solving in promoting students’ critical thinking remains uncertain. This current research presents the major findings of a meta-analysis of 36 pieces of the literature revealed in worldwide educational periodicals during the 21st century to identify the effectiveness of collaborative problem-solving in promoting students’ critical thinking and to determine, based on evidence, whether and to what extent collaborative problem solving can result in a rise or decrease in critical thinking. The findings show that (1) collaborative problem solving is an effective teaching approach to foster students’ critical thinking, with a significant overall effect size (ES = 0.82, z  = 12.78, P  < 0.01, 95% CI [0.69, 0.95]); (2) in respect to the dimensions of critical thinking, collaborative problem solving can significantly and successfully enhance students’ attitudinal tendencies (ES = 1.17, z  = 7.62, P  < 0.01, 95% CI[0.87, 1.47]); nevertheless, it falls short in terms of improving students’ cognitive skills, having only an upper-middle impact (ES = 0.70, z  = 11.55, P  < 0.01, 95% CI[0.58, 0.82]); and (3) the teaching type (chi 2  = 7.20, P  < 0.05), intervention duration (chi 2  = 12.18, P  < 0.01), subject area (chi 2  = 13.36, P  < 0.05), group size (chi 2  = 8.77, P  < 0.05), and learning scaffold (chi 2  = 9.03, P  < 0.01) all have an impact on critical thinking, and they can be viewed as important moderating factors that affect how critical thinking develops. On the basis of these results, recommendations are made for further study and instruction to better support students’ critical thinking in the context of collaborative problem-solving.

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Introduction

Although critical thinking has a long history in research, the concept of critical thinking, which is regarded as an essential competence for learners in the 21st century, has recently attracted more attention from researchers and teaching practitioners (National Research Council, 2012 ). Critical thinking should be the core of curriculum reform based on key competencies in the field of education (Peng and Deng, 2017 ) because students with critical thinking can not only understand the meaning of knowledge but also effectively solve practical problems in real life even after knowledge is forgotten (Kek and Huijser, 2011 ). The definition of critical thinking is not universal (Ennis, 1989 ; Castle, 2009 ; Niu et al., 2013 ). In general, the definition of critical thinking is a self-aware and self-regulated thought process (Facione, 1990 ; Niu et al., 2013 ). It refers to the cognitive skills needed to interpret, analyze, synthesize, reason, and evaluate information as well as the attitudinal tendency to apply these abilities (Halpern, 2001 ). The view that critical thinking can be taught and learned through curriculum teaching has been widely supported by many researchers (e.g., Kuncel, 2011 ; Leng and Lu, 2020 ), leading to educators’ efforts to foster it among students. In the field of teaching practice, there are three types of courses for teaching critical thinking (Ennis, 1989 ). The first is an independent curriculum in which critical thinking is taught and cultivated without involving the knowledge of specific disciplines; the second is an integrated curriculum in which critical thinking is integrated into the teaching of other disciplines as a clear teaching goal; and the third is a mixed curriculum in which critical thinking is taught in parallel to the teaching of other disciplines for mixed teaching training. Furthermore, numerous measuring tools have been developed by researchers and educators to measure critical thinking in the context of teaching practice. These include standardized measurement tools, such as WGCTA, CCTST, CCTT, and CCTDI, which have been verified by repeated experiments and are considered effective and reliable by international scholars (Facione and Facione, 1992 ). In short, descriptions of critical thinking, including its two dimensions of attitudinal tendency and cognitive skills, different types of teaching courses, and standardized measurement tools provide a complex normative framework for understanding, teaching, and evaluating critical thinking.

Cultivating critical thinking in curriculum teaching can start with a problem, and one of the most popular critical thinking instructional approaches is problem-based learning (Liu et al., 2020 ). Duch et al. ( 2001 ) noted that problem-based learning in group collaboration is progressive active learning, which can improve students’ critical thinking and problem-solving skills. Collaborative problem-solving is the organic integration of collaborative learning and problem-based learning, which takes learners as the center of the learning process and uses problems with poor structure in real-world situations as the starting point for the learning process (Liang et al., 2017 ). Students learn the knowledge needed to solve problems in a collaborative group, reach a consensus on problems in the field, and form solutions through social cooperation methods, such as dialogue, interpretation, questioning, debate, negotiation, and reflection, thus promoting the development of learners’ domain knowledge and critical thinking (Cindy, 2004 ; Liang et al., 2017 ).

Collaborative problem-solving has been widely used in the teaching practice of critical thinking, and several studies have attempted to conduct a systematic review and meta-analysis of the empirical literature on critical thinking from various perspectives. However, little attention has been paid to the impact of collaborative problem-solving on critical thinking. Therefore, the best approach for developing and enhancing critical thinking throughout collaborative problem-solving is to examine how to implement critical thinking instruction; however, this issue is still unexplored, which means that many teachers are incapable of better instructing critical thinking (Leng and Lu, 2020 ; Niu et al., 2013 ). For example, Huber ( 2016 ) provided the meta-analysis findings of 71 publications on gaining critical thinking over various time frames in college with the aim of determining whether critical thinking was truly teachable. These authors found that learners significantly improve their critical thinking while in college and that critical thinking differs with factors such as teaching strategies, intervention duration, subject area, and teaching type. The usefulness of collaborative problem-solving in fostering students’ critical thinking, however, was not determined by this study, nor did it reveal whether there existed significant variations among the different elements. A meta-analysis of 31 pieces of educational literature was conducted by Liu et al. ( 2020 ) to assess the impact of problem-solving on college students’ critical thinking. These authors found that problem-solving could promote the development of critical thinking among college students and proposed establishing a reasonable group structure for problem-solving in a follow-up study to improve students’ critical thinking. Additionally, previous empirical studies have reached inconclusive and even contradictory conclusions about whether and to what extent collaborative problem-solving increases or decreases critical thinking levels. As an illustration, Yang et al. ( 2008 ) carried out an experiment on the integrated curriculum teaching of college students based on a web bulletin board with the goal of fostering participants’ critical thinking in the context of collaborative problem-solving. These authors’ research revealed that through sharing, debating, examining, and reflecting on various experiences and ideas, collaborative problem-solving can considerably enhance students’ critical thinking in real-life problem situations. In contrast, collaborative problem-solving had a positive impact on learners’ interaction and could improve learning interest and motivation but could not significantly improve students’ critical thinking when compared to traditional classroom teaching, according to research by Naber and Wyatt ( 2014 ) and Sendag and Odabasi ( 2009 ) on undergraduate and high school students, respectively.

The above studies show that there is inconsistency regarding the effectiveness of collaborative problem-solving in promoting students’ critical thinking. Therefore, it is essential to conduct a thorough and trustworthy review to detect and decide whether and to what degree collaborative problem-solving can result in a rise or decrease in critical thinking. Meta-analysis is a quantitative analysis approach that is utilized to examine quantitative data from various separate studies that are all focused on the same research topic. This approach characterizes the effectiveness of its impact by averaging the effect sizes of numerous qualitative studies in an effort to reduce the uncertainty brought on by independent research and produce more conclusive findings (Lipsey and Wilson, 2001 ).

This paper used a meta-analytic approach and carried out a meta-analysis to examine the effectiveness of collaborative problem-solving in promoting students’ critical thinking in order to make a contribution to both research and practice. The following research questions were addressed by this meta-analysis:

What is the overall effect size of collaborative problem-solving in promoting students’ critical thinking and its impact on the two dimensions of critical thinking (i.e., attitudinal tendency and cognitive skills)?

How are the disparities between the study conclusions impacted by various moderating variables if the impacts of various experimental designs in the included studies are heterogeneous?

This research followed the strict procedures (e.g., database searching, identification, screening, eligibility, merging, duplicate removal, and analysis of included studies) of Cooper’s ( 2010 ) proposed meta-analysis approach for examining quantitative data from various separate studies that are all focused on the same research topic. The relevant empirical research that appeared in worldwide educational periodicals within the 21st century was subjected to this meta-analysis using Rev-Man 5.4. The consistency of the data extracted separately by two researchers was tested using Cohen’s kappa coefficient, and a publication bias test and a heterogeneity test were run on the sample data to ascertain the quality of this meta-analysis.

Data sources and search strategies

There were three stages to the data collection process for this meta-analysis, as shown in Fig. 1 , which shows the number of articles included and eliminated during the selection process based on the statement and study eligibility criteria.

This flowchart shows the number of records identified, included and excluded in the article.

First, the databases used to systematically search for relevant articles were the journal papers of the Web of Science Core Collection and the Chinese Core source journal, as well as the Chinese Social Science Citation Index (CSSCI) source journal papers included in CNKI. These databases were selected because they are credible platforms that are sources of scholarly and peer-reviewed information with advanced search tools and contain literature relevant to the subject of our topic from reliable researchers and experts. The search string with the Boolean operator used in the Web of Science was “TS = (((“critical thinking” or “ct” and “pretest” or “posttest”) or (“critical thinking” or “ct” and “control group” or “quasi experiment” or “experiment”)) and (“collaboration” or “collaborative learning” or “CSCL”) and (“problem solving” or “problem-based learning” or “PBL”))”. The research area was “Education Educational Research”, and the search period was “January 1, 2000, to December 30, 2021”. A total of 412 papers were obtained. The search string with the Boolean operator used in the CNKI was “SU = (‘critical thinking’*‘collaboration’ + ‘critical thinking’*‘collaborative learning’ + ‘critical thinking’*‘CSCL’ + ‘critical thinking’*‘problem solving’ + ‘critical thinking’*‘problem-based learning’ + ‘critical thinking’*‘PBL’ + ‘critical thinking’*‘problem oriented’) AND FT = (‘experiment’ + ‘quasi experiment’ + ‘pretest’ + ‘posttest’ + ‘empirical study’)” (translated into Chinese when searching). A total of 56 studies were found throughout the search period of “January 2000 to December 2021”. From the databases, all duplicates and retractions were eliminated before exporting the references into Endnote, a program for managing bibliographic references. In all, 466 studies were found.

Second, the studies that matched the inclusion and exclusion criteria for the meta-analysis were chosen by two researchers after they had reviewed the abstracts and titles of the gathered articles, yielding a total of 126 studies.

Third, two researchers thoroughly reviewed each included article’s whole text in accordance with the inclusion and exclusion criteria. Meanwhile, a snowball search was performed using the references and citations of the included articles to ensure complete coverage of the articles. Ultimately, 36 articles were kept.

Two researchers worked together to carry out this entire process, and a consensus rate of almost 94.7% was reached after discussion and negotiation to clarify any emerging differences.

Eligibility criteria

Since not all the retrieved studies matched the criteria for this meta-analysis, eligibility criteria for both inclusion and exclusion were developed as follows:

The publication language of the included studies was limited to English and Chinese, and the full text could be obtained. Articles that did not meet the publication language and articles not published between 2000 and 2021 were excluded.

The research design of the included studies must be empirical and quantitative studies that can assess the effect of collaborative problem-solving on the development of critical thinking. Articles that could not identify the causal mechanisms by which collaborative problem-solving affects critical thinking, such as review articles and theoretical articles, were excluded.

The research method of the included studies must feature a randomized control experiment or a quasi-experiment, or a natural experiment, which have a higher degree of internal validity with strong experimental designs and can all plausibly provide evidence that critical thinking and collaborative problem-solving are causally related. Articles with non-experimental research methods, such as purely correlational or observational studies, were excluded.

The participants of the included studies were only students in school, including K-12 students and college students. Articles in which the participants were non-school students, such as social workers or adult learners, were excluded.

The research results of the included studies must mention definite signs that may be utilized to gauge critical thinking’s impact (e.g., sample size, mean value, or standard deviation). Articles that lacked specific measurement indicators for critical thinking and could not calculate the effect size were excluded.

Data coding design

In order to perform a meta-analysis, it is necessary to collect the most important information from the articles, codify that information’s properties, and convert descriptive data into quantitative data. Therefore, this study designed a data coding template (see Table 1 ). Ultimately, 16 coding fields were retained.

The designed data-coding template consisted of three pieces of information. Basic information about the papers was included in the descriptive information: the publishing year, author, serial number, and title of the paper.

The variable information for the experimental design had three variables: the independent variable (instruction method), the dependent variable (critical thinking), and the moderating variable (learning stage, teaching type, intervention duration, learning scaffold, group size, measuring tool, and subject area). Depending on the topic of this study, the intervention strategy, as the independent variable, was coded into collaborative and non-collaborative problem-solving. The dependent variable, critical thinking, was coded as a cognitive skill and an attitudinal tendency. And seven moderating variables were created by grouping and combining the experimental design variables discovered within the 36 studies (see Table 1 ), where learning stages were encoded as higher education, high school, middle school, and primary school or lower; teaching types were encoded as mixed courses, integrated courses, and independent courses; intervention durations were encoded as 0–1 weeks, 1–4 weeks, 4–12 weeks, and more than 12 weeks; group sizes were encoded as 2–3 persons, 4–6 persons, 7–10 persons, and more than 10 persons; learning scaffolds were encoded as teacher-supported learning scaffold, technique-supported learning scaffold, and resource-supported learning scaffold; measuring tools were encoded as standardized measurement tools (e.g., WGCTA, CCTT, CCTST, and CCTDI) and self-adapting measurement tools (e.g., modified or made by researchers); and subject areas were encoded according to the specific subjects used in the 36 included studies.

The data information contained three metrics for measuring critical thinking: sample size, average value, and standard deviation. It is vital to remember that studies with various experimental designs frequently adopt various formulas to determine the effect size. And this paper used Morris’ proposed standardized mean difference (SMD) calculation formula ( 2008 , p. 369; see Supplementary Table S3 ).

Procedure for extracting and coding data

According to the data coding template (see Table 1 ), the 36 papers’ information was retrieved by two researchers, who then entered them into Excel (see Supplementary Table S1 ). The results of each study were extracted separately in the data extraction procedure if an article contained numerous studies on critical thinking, or if a study assessed different critical thinking dimensions. For instance, Tiwari et al. ( 2010 ) used four time points, which were viewed as numerous different studies, to examine the outcomes of critical thinking, and Chen ( 2013 ) included the two outcome variables of attitudinal tendency and cognitive skills, which were regarded as two studies. After discussion and negotiation during data extraction, the two researchers’ consistency test coefficients were roughly 93.27%. Supplementary Table S2 details the key characteristics of the 36 included articles with 79 effect quantities, including descriptive information (e.g., the publishing year, author, serial number, and title of the paper), variable information (e.g., independent variables, dependent variables, and moderating variables), and data information (e.g., mean values, standard deviations, and sample size). Following that, testing for publication bias and heterogeneity was done on the sample data using the Rev-Man 5.4 software, and then the test results were used to conduct a meta-analysis.

Publication bias test

When the sample of studies included in a meta-analysis does not accurately reflect the general status of research on the relevant subject, publication bias is said to be exhibited in this research. The reliability and accuracy of the meta-analysis may be impacted by publication bias. Due to this, the meta-analysis needs to check the sample data for publication bias (Stewart et al., 2006 ). A popular method to check for publication bias is the funnel plot; and it is unlikely that there will be publishing bias when the data are equally dispersed on either side of the average effect size and targeted within the higher region. The data are equally dispersed within the higher portion of the efficient zone, consistent with the funnel plot connected with this analysis (see Fig. 2 ), indicating that publication bias is unlikely in this situation.

This funnel plot shows the result of publication bias of 79 effect quantities across 36 studies.

Heterogeneity test

To select the appropriate effect models for the meta-analysis, one might use the results of a heterogeneity test on the data effect sizes. In a meta-analysis, it is common practice to gauge the degree of data heterogeneity using the I 2 value, and I 2  ≥ 50% is typically understood to denote medium-high heterogeneity, which calls for the adoption of a random effect model; if not, a fixed effect model ought to be applied (Lipsey and Wilson, 2001 ). The findings of the heterogeneity test in this paper (see Table 2 ) revealed that I 2 was 86% and displayed significant heterogeneity ( P  < 0.01). To ensure accuracy and reliability, the overall effect size ought to be calculated utilizing the random effect model.

The analysis of the overall effect size

This meta-analysis utilized a random effect model to examine 79 effect quantities from 36 studies after eliminating heterogeneity. In accordance with Cohen’s criterion (Cohen, 1992 ), it is abundantly clear from the analysis results, which are shown in the forest plot of the overall effect (see Fig. 3 ), that the cumulative impact size of cooperative problem-solving is 0.82, which is statistically significant ( z  = 12.78, P  < 0.01, 95% CI [0.69, 0.95]), and can encourage learners to practice critical thinking.

This forest plot shows the analysis result of the overall effect size across 36 studies.

In addition, this study examined two distinct dimensions of critical thinking to better understand the precise contributions that collaborative problem-solving makes to the growth of critical thinking. The findings (see Table 3 ) indicate that collaborative problem-solving improves cognitive skills (ES = 0.70) and attitudinal tendency (ES = 1.17), with significant intergroup differences (chi 2  = 7.95, P  < 0.01). Although collaborative problem-solving improves both dimensions of critical thinking, it is essential to point out that the improvements in students’ attitudinal tendency are much more pronounced and have a significant comprehensive effect (ES = 1.17, z  = 7.62, P  < 0.01, 95% CI [0.87, 1.47]), whereas gains in learners’ cognitive skill are slightly improved and are just above average. (ES = 0.70, z  = 11.55, P  < 0.01, 95% CI [0.58, 0.82]).

The analysis of moderator effect size

The whole forest plot’s 79 effect quantities underwent a two-tailed test, which revealed significant heterogeneity ( I 2  = 86%, z  = 12.78, P  < 0.01), indicating differences between various effect sizes that may have been influenced by moderating factors other than sampling error. Therefore, exploring possible moderating factors that might produce considerable heterogeneity was done using subgroup analysis, such as the learning stage, learning scaffold, teaching type, group size, duration of the intervention, measuring tool, and the subject area included in the 36 experimental designs, in order to further explore the key factors that influence critical thinking. The findings (see Table 4 ) indicate that various moderating factors have advantageous effects on critical thinking. In this situation, the subject area (chi 2  = 13.36, P  < 0.05), group size (chi 2  = 8.77, P  < 0.05), intervention duration (chi 2  = 12.18, P  < 0.01), learning scaffold (chi 2  = 9.03, P  < 0.01), and teaching type (chi 2  = 7.20, P  < 0.05) are all significant moderators that can be applied to support the cultivation of critical thinking. However, since the learning stage and the measuring tools did not significantly differ among intergroup (chi 2  = 3.15, P  = 0.21 > 0.05, and chi 2  = 0.08, P  = 0.78 > 0.05), we are unable to explain why these two factors are crucial in supporting the cultivation of critical thinking in the context of collaborative problem-solving. These are the precise outcomes, as follows:

Various learning stages influenced critical thinking positively, without significant intergroup differences (chi 2  = 3.15, P  = 0.21 > 0.05). High school was first on the list of effect sizes (ES = 1.36, P  < 0.01), then higher education (ES = 0.78, P  < 0.01), and middle school (ES = 0.73, P  < 0.01). These results show that, despite the learning stage’s beneficial influence on cultivating learners’ critical thinking, we are unable to explain why it is essential for cultivating critical thinking in the context of collaborative problem-solving.

Different teaching types had varying degrees of positive impact on critical thinking, with significant intergroup differences (chi 2  = 7.20, P  < 0.05). The effect size was ranked as follows: mixed courses (ES = 1.34, P  < 0.01), integrated courses (ES = 0.81, P  < 0.01), and independent courses (ES = 0.27, P  < 0.01). These results indicate that the most effective approach to cultivate critical thinking utilizing collaborative problem solving is through the teaching type of mixed courses.

Various intervention durations significantly improved critical thinking, and there were significant intergroup differences (chi 2  = 12.18, P  < 0.01). The effect sizes related to this variable showed a tendency to increase with longer intervention durations. The improvement in critical thinking reached a significant level (ES = 0.85, P  < 0.01) after more than 12 weeks of training. These findings indicate that the intervention duration and critical thinking’s impact are positively correlated, with a longer intervention duration having a greater effect.

Different learning scaffolds influenced critical thinking positively, with significant intergroup differences (chi 2  = 9.03, P  < 0.01). The resource-supported learning scaffold (ES = 0.69, P  < 0.01) acquired a medium-to-higher level of impact, the technique-supported learning scaffold (ES = 0.63, P  < 0.01) also attained a medium-to-higher level of impact, and the teacher-supported learning scaffold (ES = 0.92, P  < 0.01) displayed a high level of significant impact. These results show that the learning scaffold with teacher support has the greatest impact on cultivating critical thinking.

Various group sizes influenced critical thinking positively, and the intergroup differences were statistically significant (chi 2  = 8.77, P  < 0.05). Critical thinking showed a general declining trend with increasing group size. The overall effect size of 2–3 people in this situation was the biggest (ES = 0.99, P  < 0.01), and when the group size was greater than 7 people, the improvement in critical thinking was at the lower-middle level (ES < 0.5, P  < 0.01). These results show that the impact on critical thinking is positively connected with group size, and as group size grows, so does the overall impact.

Various measuring tools influenced critical thinking positively, with significant intergroup differences (chi 2  = 0.08, P  = 0.78 > 0.05). In this situation, the self-adapting measurement tools obtained an upper-medium level of effect (ES = 0.78), whereas the complete effect size of the standardized measurement tools was the largest, achieving a significant level of effect (ES = 0.84, P  < 0.01). These results show that, despite the beneficial influence of the measuring tool on cultivating critical thinking, we are unable to explain why it is crucial in fostering the growth of critical thinking by utilizing the approach of collaborative problem-solving.

Different subject areas had a greater impact on critical thinking, and the intergroup differences were statistically significant (chi 2  = 13.36, P  < 0.05). Mathematics had the greatest overall impact, achieving a significant level of effect (ES = 1.68, P  < 0.01), followed by science (ES = 1.25, P  < 0.01) and medical science (ES = 0.87, P  < 0.01), both of which also achieved a significant level of effect. Programming technology was the least effective (ES = 0.39, P  < 0.01), only having a medium-low degree of effect compared to education (ES = 0.72, P  < 0.01) and other fields (such as language, art, and social sciences) (ES = 0.58, P  < 0.01). These results suggest that scientific fields (e.g., mathematics, science) may be the most effective subject areas for cultivating critical thinking utilizing the approach of collaborative problem-solving.

The effectiveness of collaborative problem solving with regard to teaching critical thinking

According to this meta-analysis, using collaborative problem-solving as an intervention strategy in critical thinking teaching has a considerable amount of impact on cultivating learners’ critical thinking as a whole and has a favorable promotional effect on the two dimensions of critical thinking. According to certain studies, collaborative problem solving, the most frequently used critical thinking teaching strategy in curriculum instruction can considerably enhance students’ critical thinking (e.g., Liang et al., 2017 ; Liu et al., 2020 ; Cindy, 2004 ). This meta-analysis provides convergent data support for the above research views. Thus, the findings of this meta-analysis not only effectively address the first research query regarding the overall effect of cultivating critical thinking and its impact on the two dimensions of critical thinking (i.e., attitudinal tendency and cognitive skills) utilizing the approach of collaborative problem-solving, but also enhance our confidence in cultivating critical thinking by using collaborative problem-solving intervention approach in the context of classroom teaching.

Furthermore, the associated improvements in attitudinal tendency are much stronger, but the corresponding improvements in cognitive skill are only marginally better. According to certain studies, cognitive skill differs from the attitudinal tendency in classroom instruction; the cultivation and development of the former as a key ability is a process of gradual accumulation, while the latter as an attitude is affected by the context of the teaching situation (e.g., a novel and exciting teaching approach, challenging and rewarding tasks) (Halpern, 2001 ; Wei and Hong, 2022 ). Collaborative problem-solving as a teaching approach is exciting and interesting, as well as rewarding and challenging; because it takes the learners as the focus and examines problems with poor structure in real situations, and it can inspire students to fully realize their potential for problem-solving, which will significantly improve their attitudinal tendency toward solving problems (Liu et al., 2020 ). Similar to how collaborative problem-solving influences attitudinal tendency, attitudinal tendency impacts cognitive skill when attempting to solve a problem (Liu et al., 2020 ; Zhang et al., 2022 ), and stronger attitudinal tendencies are associated with improved learning achievement and cognitive ability in students (Sison, 2008 ; Zhang et al., 2022 ). It can be seen that the two specific dimensions of critical thinking as well as critical thinking as a whole are affected by collaborative problem-solving, and this study illuminates the nuanced links between cognitive skills and attitudinal tendencies with regard to these two dimensions of critical thinking. To fully develop students’ capacity for critical thinking, future empirical research should pay closer attention to cognitive skills.

The moderating effects of collaborative problem solving with regard to teaching critical thinking

In order to further explore the key factors that influence critical thinking, exploring possible moderating effects that might produce considerable heterogeneity was done using subgroup analysis. The findings show that the moderating factors, such as the teaching type, learning stage, group size, learning scaffold, duration of the intervention, measuring tool, and the subject area included in the 36 experimental designs, could all support the cultivation of collaborative problem-solving in critical thinking. Among them, the effect size differences between the learning stage and measuring tool are not significant, which does not explain why these two factors are crucial in supporting the cultivation of critical thinking utilizing the approach of collaborative problem-solving.

In terms of the learning stage, various learning stages influenced critical thinking positively without significant intergroup differences, indicating that we are unable to explain why it is crucial in fostering the growth of critical thinking.

Although high education accounts for 70.89% of all empirical studies performed by researchers, high school may be the appropriate learning stage to foster students’ critical thinking by utilizing the approach of collaborative problem-solving since it has the largest overall effect size. This phenomenon may be related to student’s cognitive development, which needs to be further studied in follow-up research.

With regard to teaching type, mixed course teaching may be the best teaching method to cultivate students’ critical thinking. Relevant studies have shown that in the actual teaching process if students are trained in thinking methods alone, the methods they learn are isolated and divorced from subject knowledge, which is not conducive to their transfer of thinking methods; therefore, if students’ thinking is trained only in subject teaching without systematic method training, it is challenging to apply to real-world circumstances (Ruggiero, 2012 ; Hu and Liu, 2015 ). Teaching critical thinking as mixed course teaching in parallel to other subject teachings can achieve the best effect on learners’ critical thinking, and explicit critical thinking instruction is more effective than less explicit critical thinking instruction (Bensley and Spero, 2014 ).

In terms of the intervention duration, with longer intervention times, the overall effect size shows an upward tendency. Thus, the intervention duration and critical thinking’s impact are positively correlated. Critical thinking, as a key competency for students in the 21st century, is difficult to get a meaningful improvement in a brief intervention duration. Instead, it could be developed over a lengthy period of time through consistent teaching and the progressive accumulation of knowledge (Halpern, 2001 ; Hu and Liu, 2015 ). Therefore, future empirical studies ought to take these restrictions into account throughout a longer period of critical thinking instruction.

With regard to group size, a group size of 2–3 persons has the highest effect size, and the comprehensive effect size decreases with increasing group size in general. This outcome is in line with some research findings; as an example, a group composed of two to four members is most appropriate for collaborative learning (Schellens and Valcke, 2006 ). However, the meta-analysis results also indicate that once the group size exceeds 7 people, small groups cannot produce better interaction and performance than large groups. This may be because the learning scaffolds of technique support, resource support, and teacher support improve the frequency and effectiveness of interaction among group members, and a collaborative group with more members may increase the diversity of views, which is helpful to cultivate critical thinking utilizing the approach of collaborative problem-solving.

With regard to the learning scaffold, the three different kinds of learning scaffolds can all enhance critical thinking. Among them, the teacher-supported learning scaffold has the largest overall effect size, demonstrating the interdependence of effective learning scaffolds and collaborative problem-solving. This outcome is in line with some research findings; as an example, a successful strategy is to encourage learners to collaborate, come up with solutions, and develop critical thinking skills by using learning scaffolds (Reiser, 2004 ; Xu et al., 2022 ); learning scaffolds can lower task complexity and unpleasant feelings while also enticing students to engage in learning activities (Wood et al., 2006 ); learning scaffolds are designed to assist students in using learning approaches more successfully to adapt the collaborative problem-solving process, and the teacher-supported learning scaffolds have the greatest influence on critical thinking in this process because they are more targeted, informative, and timely (Xu et al., 2022 ).

With respect to the measuring tool, despite the fact that standardized measurement tools (such as the WGCTA, CCTT, and CCTST) have been acknowledged as trustworthy and effective by worldwide experts, only 54.43% of the research included in this meta-analysis adopted them for assessment, and the results indicated no intergroup differences. These results suggest that not all teaching circumstances are appropriate for measuring critical thinking using standardized measurement tools. “The measuring tools for measuring thinking ability have limits in assessing learners in educational situations and should be adapted appropriately to accurately assess the changes in learners’ critical thinking.”, according to Simpson and Courtney ( 2002 , p. 91). As a result, in order to more fully and precisely gauge how learners’ critical thinking has evolved, we must properly modify standardized measuring tools based on collaborative problem-solving learning contexts.

With regard to the subject area, the comprehensive effect size of science departments (e.g., mathematics, science, medical science) is larger than that of language arts and social sciences. Some recent international education reforms have noted that critical thinking is a basic part of scientific literacy. Students with scientific literacy can prove the rationality of their judgment according to accurate evidence and reasonable standards when they face challenges or poorly structured problems (Kyndt et al., 2013 ), which makes critical thinking crucial for developing scientific understanding and applying this understanding to practical problem solving for problems related to science, technology, and society (Yore et al., 2007 ).

Suggestions for critical thinking teaching

Other than those stated in the discussion above, the following suggestions are offered for critical thinking instruction utilizing the approach of collaborative problem-solving.

First, teachers should put a special emphasis on the two core elements, which are collaboration and problem-solving, to design real problems based on collaborative situations. This meta-analysis provides evidence to support the view that collaborative problem-solving has a strong synergistic effect on promoting students’ critical thinking. Asking questions about real situations and allowing learners to take part in critical discussions on real problems during class instruction are key ways to teach critical thinking rather than simply reading speculative articles without practice (Mulnix, 2012 ). Furthermore, the improvement of students’ critical thinking is realized through cognitive conflict with other learners in the problem situation (Yang et al., 2008 ). Consequently, it is essential for teachers to put a special emphasis on the two core elements, which are collaboration and problem-solving, and design real problems and encourage students to discuss, negotiate, and argue based on collaborative problem-solving situations.

Second, teachers should design and implement mixed courses to cultivate learners’ critical thinking, utilizing the approach of collaborative problem-solving. Critical thinking can be taught through curriculum instruction (Kuncel, 2011 ; Leng and Lu, 2020 ), with the goal of cultivating learners’ critical thinking for flexible transfer and application in real problem-solving situations. This meta-analysis shows that mixed course teaching has a highly substantial impact on the cultivation and promotion of learners’ critical thinking. Therefore, teachers should design and implement mixed course teaching with real collaborative problem-solving situations in combination with the knowledge content of specific disciplines in conventional teaching, teach methods and strategies of critical thinking based on poorly structured problems to help students master critical thinking, and provide practical activities in which students can interact with each other to develop knowledge construction and critical thinking utilizing the approach of collaborative problem-solving.

Third, teachers should be more trained in critical thinking, particularly preservice teachers, and they also should be conscious of the ways in which teachers’ support for learning scaffolds can promote critical thinking. The learning scaffold supported by teachers had the greatest impact on learners’ critical thinking, in addition to being more directive, targeted, and timely (Wood et al., 2006 ). Critical thinking can only be effectively taught when teachers recognize the significance of critical thinking for students’ growth and use the proper approaches while designing instructional activities (Forawi, 2016 ). Therefore, with the intention of enabling teachers to create learning scaffolds to cultivate learners’ critical thinking utilizing the approach of collaborative problem solving, it is essential to concentrate on the teacher-supported learning scaffolds and enhance the instruction for teaching critical thinking to teachers, especially preservice teachers.

Implications and limitations

There are certain limitations in this meta-analysis, but future research can correct them. First, the search languages were restricted to English and Chinese, so it is possible that pertinent studies that were written in other languages were overlooked, resulting in an inadequate number of articles for review. Second, these data provided by the included studies are partially missing, such as whether teachers were trained in the theory and practice of critical thinking, the average age and gender of learners, and the differences in critical thinking among learners of various ages and genders. Third, as is typical for review articles, more studies were released while this meta-analysis was being done; therefore, it had a time limit. With the development of relevant research, future studies focusing on these issues are highly relevant and needed.

Conclusions

The subject of the magnitude of collaborative problem-solving’s impact on fostering students’ critical thinking, which received scant attention from other studies, was successfully addressed by this study. The question of the effectiveness of collaborative problem-solving in promoting students’ critical thinking was addressed in this study, which addressed a topic that had gotten little attention in earlier research. The following conclusions can be made:

Regarding the results obtained, collaborative problem solving is an effective teaching approach to foster learners’ critical thinking, with a significant overall effect size (ES = 0.82, z  = 12.78, P  < 0.01, 95% CI [0.69, 0.95]). With respect to the dimensions of critical thinking, collaborative problem-solving can significantly and effectively improve students’ attitudinal tendency, and the comprehensive effect is significant (ES = 1.17, z  = 7.62, P  < 0.01, 95% CI [0.87, 1.47]); nevertheless, it falls short in terms of improving students’ cognitive skills, having only an upper-middle impact (ES = 0.70, z  = 11.55, P  < 0.01, 95% CI [0.58, 0.82]).

As demonstrated by both the results and the discussion, there are varying degrees of beneficial effects on students’ critical thinking from all seven moderating factors, which were found across 36 studies. In this context, the teaching type (chi 2  = 7.20, P  < 0.05), intervention duration (chi 2  = 12.18, P  < 0.01), subject area (chi 2  = 13.36, P  < 0.05), group size (chi 2  = 8.77, P  < 0.05), and learning scaffold (chi 2  = 9.03, P  < 0.01) all have a positive impact on critical thinking, and they can be viewed as important moderating factors that affect how critical thinking develops. Since the learning stage (chi 2  = 3.15, P  = 0.21 > 0.05) and measuring tools (chi 2  = 0.08, P  = 0.78 > 0.05) did not demonstrate any significant intergroup differences, we are unable to explain why these two factors are crucial in supporting the cultivation of critical thinking in the context of collaborative problem-solving.

Data availability

All data generated or analyzed during this study are included within the article and its supplementary information files, and the supplementary information files are available in the Dataverse repository: https://doi.org/10.7910/DVN/IPFJO6 .

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Acknowledgements

This research was supported by the graduate scientific research and innovation project of Xinjiang Uygur Autonomous Region named “Research on in-depth learning of high school information technology courses for the cultivation of computing thinking” (No. XJ2022G190) and the independent innovation fund project for doctoral students of the College of Educational Science of Xinjiang Normal University named “Research on project-based teaching of high school information technology courses from the perspective of discipline core literacy” (No. XJNUJKYA2003).

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Xu, E., Wang, W. & Wang, Q. The effectiveness of collaborative problem solving in promoting students’ critical thinking: A meta-analysis based on empirical literature. Humanit Soc Sci Commun 10 , 16 (2023). https://doi.org/10.1057/s41599-023-01508-1

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Home > Online Programs > EVIDENCE-BASED PRACTICE (EBP): THE PROBLEM-SOLVING APPROACH

EVIDENCE-BASED PRACTICE (EBP): THE PROBLEM-SOLVING APPROACH

• Published On: January 10, 2012

As the nursing profession continues to evolve, the educational focus is also changing. One of the most significant emerging trends in healthcare today is the focus on evidence-based practice, also known as EBP.

Evidenced-based practice is often described as an approach to patient care that involves considering the best available research and practice guidelines associated with a specific clinical situation. Key elements in the successful implementation of evidence-based practice in nursing include:

• Reviewing research and studies that examine the best practices in clinical nursing.
• Interactive decision-making regarding care and treatment planning which integrates care team members, as well as the opinion of the patient and his or her family.
• Ongoing professional development education of nurses, including pursuit of advanced degree programs when available.
• Addressing clinical issues and critically examining possible practice changes.
• Strong emphasis on problem-solving skills, clinical judgment and the use of sound evidence to support clinical decisions based on research, experience and the environment.

Challenges to Evidence-Based Practice

Some of the impediments to evidence-based practice include a resistance to change practice and habits within the nursing community, the lack of ongoing education programs and poor administrative support. Although barriers exist, the successful patient outcomes from evidence-based practice have helped win support for this model of care among the medical profession as a whole.

Increased Responsibilities for Nurses Today

Because evidence-based practice places an emphasis on the knowledge, skills and experience of nurses, today’s nurses are being given more responsibility and respect than ever before. EBP focuses on specific nursing skills including critical decision-making founded in evidence and research, with a move away from traditional treatment regimes and habits that had been the hallmark of nursing for generations. Registered nurses now need strong analytic and academic research skills to complement clinic skills and hands-on patient care.

Options for Evidence-Based Practice Education

Nurses who are seeking to improve their clinical skills and expand both their knowledge base and career options should consider obtaining additional nursing education in programs that focus on EBP. Professional nursing today demands that nurses have a solid understanding of how to conduct research, critically review studies and medical reviews, and an EBP-focused education program will teach nurses these vital skills.

Some of the most accessible educational programs that include an emphasis on EBP the knowledge are the online nursing programs offered at the University of Texas at Arlington, including an RN to BSN and a Master of Science in Nursing Administration . The University of Texas at Arlington’s College of Nursing and Health Innovation, named one of the “Best of the West” by Princeton Review , offers a specialized program to allow RNs to obtain their BSN in just over a year. By pursuing an advanced nursing degree with a focus on evidence-based practice, working nurses will have access to a variety of career options in both clinical and administrative roles.

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METHODS article

Core processes: how to use evidence, theories, and research in planning behavior change interventions.

• 1 Department of Work and Social Psychology, Maastricht University, Maastricht, Netherlands
• 2 Department of Health Promotion, Maastricht University, Maastricht, Netherlands

Psychology is not only a basic behavioral science but also an applied discipline that is used to solve societal problems. In a problem-driven context, the search for existing literature, the correct application of appropriate theories, and the collection of additional research data are basic tools essential for the systematic development of any theory- and evidence-based behavior change intervention. The processes of brainstorming, literature review, theory selection and application, and data collection are “Core Processes” that can be used in different phases/steps of intervention planning—from needs assessment to intervention design to program implementation and evaluation—and within different planning frameworks. In this paper, we illustrate how the use of these “Core Processes” provides expert, empirical and theoretical guidance to planners from problem definition to problem solution. Specific emphasis is put on finding theories that are potentially useful in providing answers to planning questions using a combination of approaches to access and select theories (i.e., the topic, concept, and general theories approaches). Furthermore, emphasis is put on the logic of answering planning questions in a specific order by first brainstorming before consulting the literature, then applying theories, and finally collecting additional data.

Within social and health psychology teaching programs at institutes of higher education, we train students to become experts in the understanding and promotion of behaviors that contribute to better population health, public safety, and sustainable environments. Graduates of such programs are seen as experts on behavior change. They are expected to make informed decisions when it comes to identifying targets for behavior change interventions, selecting appropriate change methods to reach these targets, and translating these methods into practical applications, while making sure these measures can be implemented and their effectiveness can be assessed. Expertise in intervention planning implies that planners not only know about information sources that could help them in finding answers to the above questions, but also are able to translate the information gained from these sources in such ways that the final answers are indeed informed by expert opinion, empirical research, and theory, thus increasing the likelihood of selecting relevant intervention goals and effective and feasible intervention content [cf. ( 1 – 3 ); for empirical evidence, see for example ( 4 )].

In the Netherlands, and many other countries, most of the psychology programs include a practical training on applying psychological theory. In such skills training, students select theory- and evidence-based explanations for practically relevant problems in which behavior plays a prominent role, such as in the prevention of infectious diseases (e.g., HIV infection) and the promotion of healthy lifestyles (e.g., sufficient exercise), the early detection of life-threatening diseases (e.g., cancer, diabetes), promoting adherence to therapy and medical regimes to prevent disease episodes (e.g., asthma) or even death (e.g., AIDS), or problems in the domains of sustainability (e.g., energy conservation) or safety (e.g., fire prevention). These explanations are found through a systematic process of asking a question (e.g., why do people perform behavior X?), brainstorming possible answers, looking for empirical evidence and theoretical support, conducting new research, and coming to a final list of answers to the question. This working method is originally described by Veen ( 5 ) and in later years has been transformed into the PATH protocol ( 6 , 7 ). However, this systematic process to finding answers to questions—here referred to as Core Processes—is not limited to the understanding of problematic behaviors, but extends to the full process of intervention planning from analyzing the problem and risk behavior at hand, to selecting methods of change, to designing implementable and evaluable interventions ( 8 ).

In intervention planning, there are different frameworks available [see O'Cathain et al. ( 2 ) for a taxonomy of planning frameworks], such as PRECEDE-PROCEED ( 9 ), the Behavior Change Wheel ( 10 ), and Intervention Mapping ( 11 ), that provide guidance to planners from problem definition to problem solution. Across all these planning frameworks, applied psychologists may encounter the difficulty of using expert knowledge, empirical evidence and theory in order to analyze the problem and inform behavior change interventions. Brainstroming, reviewing existing literature, applying appropriate theories, and collecting additional research data are basic tools (Core Processes) in different phases/steps of planning frameworks, but often it is unclear exactly how and when these processes should be used in problem analysis and solving ( 6 – 8 ).

Here, Core Processes are presented as a helpful and systematic way to answer questions during intervention design. We would like to stress that although these Core Processes are described within Intervention Mapping ( 11 ), they can be applied in any planning framework and in each step of program planning from problem analysis, to intervention design, to program implementation and evaluation. Therefore, Core Processes are not a planning framework on their own, but a helpful and systematic approach to answer questions relevant to problem definition and solution using expert knowledge, empirical evidence and theory, and collecting additional data. The use of Core Processes is essential within problem-driven applied psychology because too often intervention planners claim to have reviewed empirical literature, applied theories, and collected additional data, but in fact have done these tasks incompletely and selectively. For example, when not making explicit links between determinants and methods of change or making incorrect translations from change methods to their practical applications ( 12 ). Also, in our teaching and consulting activities planners indicate finding it difficult in practice to apply these Core Processes correctly and sufficiently.

Theory-Driven and Problem-Driven Applied Psychology

Within applied (health and social) psychology, a distinction can be made between two approaches: theory-driven and problem-driven applied psychology ( 13 ). Theory-driven applied psychology involves testing a theory in an applied setting, primarily in order to gain insight into the external validity of the theory. Problem-driven applied psychology refers to scientific activities that focus on changing or reducing a practical problem. In problem-driven applied psychology, theories are used, but problem solving is the primary focus of this approach, and the criteria for success are formulated in terms of problem reduction, with contributions to theory as a useful by-product. Problem-driven applied psychology is an important field, because it provides an ultimate test for the usefulness of psychology both as a discipline and as a profession.

Core Processes for Using Theory and Evidence

Processes involved in answering a question using empirical data and theory can be complex and time-consuming; sometimes planners do not persevere in working through these difficulties. Consequently, the understanding of a problem is often incomplete, and attempts to solve the problem may be based on faulty premises/assumptions. Also, the problems that are addressed are often complex and require a multidisciplinary approach. For example, the Focus on Strength project combined existing ideas, evidence and theory from biological and psychological perspectives and introduced strength exercises to counter the negative health consequences associated with obesity and ensure high participation motivation in overweight youth ( 14 ). Furthermore, behavior change is difficult by definition: If it was easy, we would not need experts in change. So, although the required expertise within multidisciplinary planning groups may vary based on the problem that is addressed, expertise in behavior change (e.g., an applied psychologist) is always required.

Using Core Processes minimizes the likelihood of incomplete understanding and selecting ineffective solutions. As depicted in Figure 1 , Core Processes includes six steps that are described below. This is followed by an example of how to apply Core Processes in intervention planning. However, before describing Core Processes, it is important to stress that unlike planning frameworks for intervention development that generally are meant to be iterative and flexible in the order of steps (e.g., intervention planning may start from an already existing intervention implemented in a different intervention population and context), Core Processes has a fixed order of six steps, starting with asking a question, followed by first consulting with experts, then reviewing the existing empirical evidence and finding theoretical support, and if then still needed collect additional data. By keeping this order of steps, it is guaranteed that the knowledge that is available to answer planning questions is indeed accessed and new research is both relevant and informative.

Figure 1 . Core Processes for Using Evidence, Theories, and Research.

Step 1. Pose Questions

The first step when following the Core Processes is to pose (the right) questions. The first questions are often asked as a means of analyzing possible causes of the health problem (e.g., what are important risk behaviors?). Later questions are used to identify determinants of behavior and environmental conditions, and help to both develop interventions and plan intervention implementation. It is crucial that the planning group is on the same page regarding which question needs to be answered at what moment (e.g., problem analysis, identifying determinants, selecting change methods, designing implementation strategies), before continuing with the second step of the Core Processes. Lack of clarity about the questions that have to be answered might lead to a feeling of being lost in translation during subsequent steps.

Step 2. Brainstorm Possible Answers

The second step concerns “brainstorming” about possible answers and using “free association.” This is a creative process that includes consulting with experts and primarily involves free association with the aim of generating as many explanations as possible in response to a question. The planners can later disregard explanations that are poorly supported in the literature. In formulating these provisional explanations, applied behavioral scientists typically draw on theoretical and empirical knowledge, whether consciously or not. Doing so is unavoidable at this stage, but the brainstorming should be as open as possible and should not be limited to data- or theory-informed. Only in the next steps, empirical findings (of existing research in step 3 and new research in step 5) and theoretical support (step 4) are incorporated to avoid haphazard decisions based on a brainstorm only. Also, the planning group should then bear in mind that: (1) an explanation should describe a process (an explanation of causation), and (2) an explanation should be plausible. For example, socioeconomic status may be an important contextual factor—or even a root cause—of certain behaviors, but it may need to be explored further in order to better describe a process that explains behavior and thus identify factors that are part of the causal process but are more proximal to the behavior and also more easy to change [e.g., attitudinal beliefs; ( 15 )]. It may be useful to represent the explanation in a process model that shows causation ( 7 ).

Step 3. Review Empirical Findings From Published Research

The next step is to support or refute provisional answers to the questions that the planning group has asked with empirical and theoretical evidence, starting with reviewing findings from published research. The idea behind this is to disregard explanations that are poorly supported in the literature. We suggest to start searching for reviews that have already been conducted. There are many sources available in the burgeoning field of systematic reviews and evidence-based public health that are worthwhile to consult before looking for individual studies. When appraising available reviews, or conducting a new one [see ( 15 ) for basic how-to guidance], it is warranted to at least understand the nature of the numerator (what studies are used in the evidence summary) in terms of the denominator (what studies were conducted or reported), and to be aware of the variation that exists in the quality of evidence. Of course, the latter also applies when assessing individual studies. We would like to reiterate that Core Processes should be followed in this order. For example, it is unwise to use general theories aimed at explaining behavior if there is ample evidence available on determinants regarding the specific behavior of interest.

Step 4. Find Theoretical Support Using the Topic, Concepts, and General Theories Approaches

The search of the literature is focused, for example, on a specific behavior, or target group or culture. However, it might be that there is limited literature available (e.g., regarding a certain behavior or target group) or that the literature is limited in scope (e.g., focusing on a limited number of explanations). The next step, therefore, is to find theoretical support for the provisional explanations and to make the provisional list of answers as extensive as possible before conducting new research (i.e., step 5) and making decisions (i.e., step 6).

Theories can be defined as formal and abstract statements about a selected aspect of reality ( 16 ). As a consequence of their very nature, theories are always a reduction of reality. This is not a shortcoming, but rather a definition, which is important to keep in mind when using theory in addressing problem-driven problems. Real-life problems are—by definition—complex; otherwise, they would already have been solved without the need to involve researchers. It follows, then, that a multi-theory approach is required [( 11 ), p. 25] in order to further understand and solve real-life problems. This is also why intervention studies do not necessarily lead to improvements of a single theory ( 17 ). From this perspective, applying theory to real life problems can be likened to completing a jigsaw puzzle with various theories fitting together to provide an explanation or answer to a planning question ( 18 ). The argument that one theory—for example, the Reasoned Action Approach—cannot explain all the possible variances in behavior or behavior change is therefore no reason to discard the theory altogether ( 16 ). Not being able to explain all variance in behavior could only be held against a “Theory of Everything,” and there are good reasons why such a theory is undesirable ( 18 ).

In a problem-driven context, all theories, theoretical models, and concepts are potentially useful within the parameters that the theory describes ( 7 ). Moreover, there are common and unique elements regarding each theory ( 19 , 20 ). There are three approaches to finding theories: the topic, concept and general theories approaches; these should be utilized in combination but also in that order. Limiting the pool of candidate theories too soon may lead to inadequate answers or, worse, it may lead to conclusions being drawn that are counterproductive.

The Topic Approach

Going back to the literature review, the planning group needs to look specifically for theoretical concepts and frameworks that have been used to design the reported empirical studies and/or explain the findings. They then assess these theories in terms of how useful they are for providing additional answers to the formulated question.

The Concept Approach

A second approach to find theory-informed answers to the question being asked is to examine concepts that are generated during brainstorming sessions in the second step. It is likely that the ideas resulting from these brainstorming sessions are initially stated in lay terms, but there may be advantages to relabeling them with their theoretical labels. The information that can be garnered about a theoretical construct can be more precise than that related to a simple lay concept (e.g., lack of confidence could also be labeled as the theoretical construct self-efficacy). One person cannot be familiar with all potentially useful theories. This is why it is advisable to include individuals from various disciplines in the planning group and it stresses once again that expertise in behavior change (e.g., an applied psychologist) is always required. It is also worth noting that reading comprehensive overviews of theories may aid this process [( 11 ), Chapters 2 and 3; ( 21 – 24 )].

The General Theories Approach

After the topic and concept approaches, a general theories approach involves exploring a theory that may offer additional insight into the question at hand. At this stage, it may be fruitful to consider alternative frameworks that have not been accessed through the other two approaches but that could provide valuable information for further extending and refining the list of explanations. For example, dual process models of human behavior that differentiate between impulsive or automatic decision making and more reasoned routes of planning [e.g., ( 25 )], or theories of self-regulation and self-management [e.g., ( 26 )] may be informative. Referring back to the earlier statement about the strict order in the three approaches to find theories, the general theories approach should be seen as a last resort to prevent falling back in a theory-driven rather than problem-driven approach in tackling societal problems. When there is tension between generalizability and utility of theories, utility should be given preference given the applied nature of the problem-driven approach ( 27 ).

Step 5. Identify and Address the Need for New Research

It is important that the planning group completes the previously described steps instead of jumping straight into research. A very practical reason is that conducting new research requires a lot of resources (in terms of time, expertise, and money). More important, all evidence and insights that are available should be used before conducting new research: it should be clear what omissions and knowledge gaps to address in the research. For example, the planning group may want to know whether certain theoretical constructs that look promising are actually explanatory in relation to their population of interest.

Step 6. Complete and Assess the List of Possible Answers

At this point, the planning group is ready to summarize and complete the provisional list of answers into a working list of items for which the theoretical and empirical evidence is evaluated as sufficient. The planners will consider the criteria relevance and changeability of the evidence- and theory-based answers.

Example: Applying Core Processes

The following example nicely illustrates the use of the Core Processes [( 11 ), p. 21–8]. In this example, a group of students in a health education class designed a project to prevent the transmission of HIV and other sexually transmitted infections (STIs) and pregnancy among urban adolescents.

Over the course of the project, they asked a number of questions, including: (1) Health problem . What are the health problems associated with HIV, STIs, and pregnancy in adolescents (ages 13–18) in the USA? (2) Behaviors . What are important risk behaviors for the transmission of HIV and STIs, and for pregnancy among adolescents? How do these risk behaviors vary, for example, between boys and girls? (3) Determinants . About the risk behavior: Why don't adolescent males use condoms when having sex with steady girlfriends? Why do girls have sex with boys who do not use condoms? About the health-promoting behavior: Why would girls carry condoms? Why would adolescents discuss condom use with their partners? (4) Change methods . What change methods relate to what determinants? How can change methods be translated into appropriate practical applications? (5) Implementation . How could such an intervention be implemented?

Using “free association,” planning group members generate as many explanations as possible that can later be dropped when poorly supported ( 8 ). Trained behavioral scientists already know a lot about determinants of behavior and barriers for change and this knowledge should be used. In Table 1 , the first column represents the outcome of the brainstorm regarding determinants of condom use.

Table 1 . List of answers regarding condom use among adolescents [( 11 ), p. 21–8].

The second column in Table 1 presents the outcomes of the review on the evidence supporting the results of the brainstorm. The intervention planners identified empirical evidence for some issues related to unprotected sex that were not already brainstormed, for example not perceiving condoms as a means of pregnancy prevention ( 28 ) or perceiving condoms as embarrassing ( 29 , 30 ). The planning group also identified a number of studies that reported the relationship between unsafe sex and various theoretical constructs (listed in the third column): intention to use condoms and perceived norms ( 28 , 31 ) and self-efficacy in terms of negotiating and discussing condom use with partners ( 29 , 32 ). Ideally, those concepts (as depicted in Table 1 ) should be specified at the level of beliefs, for example the specific beliefs that underlie an attitude or self-efficacy ( 33 ). The planning group also became interested in information on the wider social context. For example, community characteristics—such as a high proportion of families living below the poverty line, a low level of education, and high unemployment—were found to be strongly related to teenage pregnancies ( 34 ).

Topic Approach

The literature review identified a meta-analysis study on the psycho-social determinants of condom use in heterosexual populations by Sheeran et al. ( 35 ). In the introduction and discussion sections, these authors refer to different psychosocial theories of (health) behavior such as the Health Belief Model ( 36 ), the Theory of Planned Behavior ( 37 ), and the Aids Risk Reduction Model ( 38 ). By studying these theories in detail, additional answers can be added to the list of potential explanations that are supported by theories of human behavior ( Table 1 , third column).

Concept Approach

Lack of confidence appeared on the original list. This concept could also be labeled as the theoretical construct self-efficacy. By further exploring the construct of self-efficacy in the literature ( 39 , 40 ), the planning group may then also discover that self-efficacy is closely related to skills, perceived norms, and outcome expectations. As a result, they could add perceived norms and skills for negotiating condom use and applying a condom to the list ( Table 1 , third column). In this additional exploration of the theoretical literature, the group may encounter methods for influencing self-efficacy and think ahead in terms of how to apply this in the intervention. None of this useful information would have been available if the group had not related confidence to the concept of self-efficacy and studied the underlying theoretical framework.

General Theories Approach

The planning group could have used the general theories approach to access Social Cognitive Theory ( 41 ), but of course the topic and concept approaches would most likely also have led the planning group to this theory.

In the next step, the planning group needed more information from their priority population about the items on the provisional list in order to determine whether these proposed factors were relevant to their particular population. To this end, the group conducted focus groups with seventh- and eighth-grade students from the priority population. The new data called into question the notion of a lack of knowledge about HIV or STIs in the adolescent population. Interestingly, the adolescents also felt that the argument “condoms don't work” is more of an excuse and less of a belief about their effectiveness. The adolescents who had tried condoms expressed some embarrassment with the process of using condoms and a need for a greater level of skills and self-efficacy. With this new information ( Table 1 , fourth column), the planning group was able to proceed to the final step.

In the final step, the planning group completes the provisional list of answers and summarizes it into a working list for which the evidence is sufficient. The provisional list of answers from the brainstorm is thus followed up by a list of answers for which theoretical and empirical support has been sought. In step 6, the planning group then decides whether the evidence is sufficient by assessing the answers in terms of relevance and changeability. Relevance refers to the strength of the evidence for the association between the determinant and the behavior. Crutzen et al. ( 42 ) provide a practical approach to select determinants based on visualization of confidence intervals for the means and correlation coefficients for all determinants simultaneously. Changeability refers to the strength of the evidence suggesting that the proposed change can be realized by an intervention. Whenever possible, judgments regarding changeability should be based on evidence from the research literature ( 43 ). However, when data regarding changeability are scarce, such judgments have to rely on a theoretical or conceptual basis. Behavior change expertise is then needed to make judgments regarding changeability. See Figure 2 for a logic model concerning the overarching project from which this example was derived.

Figure 2 . Logic Model with Relevant and Changeable Determinants [adapted from ( 11 ), p. 259].

Core Processes for Selecting Change Methods

For brevity and consistency reasons, the example used above to illustrate the Core Processes in answering questions with empirical and theoretical support mainly concern selection of determinants (i.e., addressing “why” questions). We would like to stress that Core Processes also need to be used to select change methods for behavior change or to systematically plan implementation and evaluation of interventions ( 11 ). In other words, to also address “how” questions. The focus of the questions then shifts to potential solutions or theory- and evidence-based change methods, for example: How can we encourage specific subgroups of adolescents to use condoms? How can change methods be translated into appropriate practical applications? In relation to a solutions or methods question, answers that remain on the list after engaging in all Core Processes will be methods that have been shown to produce significant change in similar situations. Kok et al. ( 44 ), for example, provides tables with theoretical methods (and their limiting conditions) for every major determinant and for all higher environmental levels, i.e., interpersonal, organizational, community, and policy levels. It is important to bear in mind that theory-based methods are only effective under certain limiting conditions, i.e., the parameters for effectiveness ( 12 ). When these parameters are ignored—or lost in translation from behavior change method to practical application—effective behavior change is undermined and the intervention may even result in unintended or counterproductive effects ( 45 ). Parameters for effectiveness are another example stressing that although the required expertise within multidisciplinary planning groups may vary based on the problem that is addressed, expertise in behavior change (e.g., an applied psychologist) is always required.

Applied psychology is a scientific discipline in which different kinds of societal problems and issues are addressed. The garnering of expert knowledge, the search for existing literature, the selection and correct application of appropriate theories, and the collection of additional research data are essential for the systematic development of any intervention. It is, however, often unclear exactly how and when these processes should be used in problem analysis and solving. Core Processes are presented as a helpful and systematic way to answer questions raised in different phases/steps of planning frameworks. So, Core Processes are not a planning framework on their own, but a way to address questions relevant to problem definition and solution using evidence, theories, and research.

Author's Note

We have presented this work at the 2019 conference of the European Health Psychology Society and an abstract similar to this paper was included in the proceedings of that conference ( 46 ).

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: Core Processes, applying theories, applied psychology, behavior change, problem-driven approach

Citation: Ruiter RAC and Crutzen R (2020) Core Processes: How to Use Evidence, Theories, and Research in Planning Behavior Change Interventions. Front. Public Health 8:247. doi: 10.3389/fpubh.2020.00247

Received: 26 November 2019; Accepted: 20 May 2020; Published: 24 June 2020.

Reviewed by:

Copyright © 2020 Ruiter and Crutzen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Robert A. C. Ruiter, r.ruiter@maastrichtuniversity.nl ; Rik Crutzen, rik.crutzen@maastrichtuniversity.nl

† These authors share first authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

STEM Problem Solving: Inquiry, Concepts, and Reasoning

• Published: 29 January 2022
• Volume 32 , pages 381–397, ( 2023 )

Cite this article

• Aik-Ling Tan   ORCID: orcid.org/0000-0002-4627-4977 1 ,
• Yann Shiou Ong   ORCID: orcid.org/0000-0002-6092-2803 1 ,
• Yong Sim Ng   ORCID: orcid.org/0000-0002-8400-2040 1 &
• Jared Hong Jie Tan 1  

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Balancing disciplinary knowledge and practical reasoning in problem solving is needed for meaningful learning. In STEM problem solving, science subject matter with associated practices often appears distant to learners due to its abstract nature. Consequently, learners experience difficulties making meaningful connections between science and their daily experiences. Applying Dewey’s idea of practical and science inquiry and Bereiter’s idea of referent-centred and problem-centred knowledge, we examine how integrated STEM problem solving offers opportunities for learners to shuttle between practical and science inquiry and the kinds of knowledge that result from each form of inquiry. We hypothesize that connecting science inquiry with practical inquiry narrows the gap between science and everyday experiences to overcome isolation and fragmentation of science learning. In this study, we examine classroom talk as students engage in problem solving to increase crop yield. Qualitative content analysis of the utterances of six classes of 113 eighth graders and their teachers were conducted for 3 hours of video recordings. Analysis showed an almost equal amount of science and practical inquiry talk. Teachers and students applied their everyday experiences to generate solutions. Science talk was at the basic level of facts and was used to explain reasons for specific design considerations. There was little evidence of higher-level scientific conceptual knowledge being applied. Our observations suggest opportunities for more intentional connections of science to practical problem solving, if we intend to apply higher-order scientific knowledge in problem solving. Deliberate application and reference to scientific knowledge could improve the quality of solutions generated.

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1 Introduction

As we enter to second quarter of the twenty-first century, it is timely to take stock of both the changes and demands that continue to weigh on our education system. A recent report by World Economic Forum highlighted the need to continuously re-position and re-invent education to meet the challenges presented by the disruptions brought upon by the fourth industrial revolution (World Economic Forum, 2020 ). There is increasing pressure for education to equip children with the necessary, relevant, and meaningful knowledge, skills, and attitudes to create a “more inclusive, cohesive and productive world” (World Economic Forum, 2020 , p. 4). Further, the shift in emphasis towards twenty-first century competencies over mere acquisition of disciplinary content knowledge is more urgent since we are preparing students for “jobs that do not yet exist, technology that has not yet been invented, and problems that has yet exist” (OECD, 2018 , p. 2). Tan ( 2020 ) concurred with the urgent need to extend the focus of education, particularly in science education, such that learners can learn to think differently about possibilities in this world. Amidst this rhetoric for change, the questions that remained to be answered include how can science education transform itself to be more relevant; what is the role that science education play in integrated STEM learning; how can scientific knowledge, skills and epistemic practices of science be infused in integrated STEM learning; what kinds of STEM problems should we expose students to for them to learn disciplinary knowledge and skills; and what is the relationship between learning disciplinary content knowledge and problem solving skills?

In seeking to understand the extent of science learning that took place within integrated STEM learning, we dissected the STEM problems that were presented to students and examined in detail the sense making processes that students utilized when they worked on the problems. We adopted Dewey’s ( 1938 ) theoretical idea of scientific and practical/common-sense inquiry and Bereiter’s ideas of referent-centred and problem-centred knowledge building process to interpret teacher-students’ interactions during problem solving. There are two primary reasons for choosing these two theoretical frameworks. Firstly, Dewey’s ideas about the relationship between science inquiry and every day practical problem-solving is important in helping us understand the role of science subject matter knowledge and science inquiry in solving practical real-world problems that are commonly used in STEM learning. Secondly, Bereiter’s ideas of referent-centred and problem-centred knowledge augment our understanding of the types of knowledge that students can learn when they engage in solving practical real-world problems.

Taken together, Dewey’s and Bereiter’s ideas enable us to better understand the types of problems used in STEM learning and their corresponding knowledge that is privileged during the problem-solving process. As such, the two theoretical lenses offered an alternative and convincing way to understand the actual types of knowledge that are used within the context of integrated STEM and help to move our understanding of STEM learning beyond current focus on examining how engineering can be used as an integrative mechanism (Bryan et al., 2016 ) or applying the argument of the strengths of trans-, multi-, or inter-disciplinary activities (Bybee, 2013 ; Park et al., 2020 ) or mapping problems by the content and context as pure STEM problems, STEM-related problems or non-STEM problems (Pleasants, 2020 ). Further, existing research (for example, Gale et al., 2000 ) around STEM education focussed largely on description of students’ learning experiences with insufficient attention given to the connections between disciplinary conceptual knowledge and inquiry processes that students use to arrive at solutions to problems. Clarity in the role of disciplinary knowledge and the related inquiry will allow for more intentional design of STEM problems for students to learn higher-order knowledge. Applying Dewey’s idea of practical and scientific inquiry and Bereiter’s ideas of referent-centred and problem-centred knowledge, we analysed six lessons where students engaged with integrated STEM problem solving to propose answers to the following research questions: What is the extent of practical and scientific inquiry in integrated STEM problem solving? and What conceptual knowledge and problem-solving skills are learnt through practical and science inquiry during integrated STEM problem solving?

2 Inquiry in Problem Solving

Inquiry, according to Dewey ( 1938 ), involves the direct control of unknown situations to change them into a coherent and unified one. Inquiry usually encompasses two interrelated activities—(1) thinking about ideas related to conceptual subject-matter and (2) engaging in activities involving our senses or using specific observational techniques. The National Science Education Standards released by the National Research Council in the US in 1996 defined inquiry as “…a multifaceted activity that involves making observations; posing questions; examining books and other sources of information to see what is already known; planning investigations; reviewing what is already known in light of experimental evidence; using tools to gather, analyze, and interpret data; proposing answers, explanations, and predictions; and communicating the results. Inquiry requires identification of assumptions, use of critical and logical thinking, and consideration of alternative explanations” (p. 23). Planning investigation; collecting empirical evidence; using tools to gather, analyse and interpret data; and reasoning are common processes shared in the field of science and engineering and hence are highly relevant to apply to integrated STEM education.

In STEM education, establishing the connection between general inquiry and its application helps to link disciplinary understanding to epistemic knowledge. For instance, methods of science inquiry are popular in STEM education due to the familiarity that teachers have with scientific methods. Science inquiry, a specific form of inquiry, has appeared in many science curriculum (e.g. NRC, 2000 ) since Dewey proposed in 1910 that learning of science should be perceived as both subject-matter and a method of learning science (Dewey, 1910a , 1910b ). Science inquiry which involved ways of doing science should also encompass the ways in which students learn the scientific knowledge and investigative methods that enable scientific knowledge to be constructed. Asking scientifically orientated questions, collecting empirical evidence, crafting explanations, proposing models and reasoning based on available evidence are affordances of scientific inquiry. As such, science should be pursued as a way of knowing rather than merely acquisition of scientific knowledge.

Building on these affordances of science inquiry, Duschl and Bybee ( 2014 ) advocated the 5D model that focused on the practice of planning and carrying out investigations in science and engineering, representing two of the four disciplines in STEM. The 5D model includes science inquiry aspects such as (1) deciding on what and how to measure, observe and sample; (2) developing and selecting appropriate tools to measure and collect data; (3) recording the results and observations in a systematic manner; (4) creating ways to represent the data and patterns that are observed; and (5) determining the validity and the representativeness of the data collected. The focus on planning and carrying out investigations in the 5D model is used to help teachers bridge the gap between the practices of building and refining models and explanation in science and engineering. Indeed, a common approach to incorporating science inquiry in integrated STEM curriculum involves student planning and carrying out scientific investigations and making sense of the data collected to inform engineering design solution (Cunningham & Lachapelle, 2016 ; Roehrig et al., 2021 ). Duschl and Bybee ( 2014 ) argued that it is needful to design experiences for learners to appreciate that struggles are part of problem solving in science and engineering. They argued that “when the struggles of doing science is eliminated or simplified, learners get the wrong perceptions of what is involved when obtaining scientific knowledge and evidence” (Duschl & Bybee, 2014 , p. 2). While we concur with Duschl and Bybee about the need for struggles, in STEM learning, these struggles must be purposeful and grade appropriate so that students will also be able to experience success amidst failure.

The peculiar nature of science inquiry was scrutinized by Dewey ( 1938 ) when he cross-examined the relationship between science inquiry and other forms of inquiry, particularly common-sense inquiry. He positioned science inquiry along a continuum with general or common-sense inquiry that he termed as “logic”. Dewey argued that common-sense inquiry serves a practical purpose and exhibits features of science inquiry such as asking questions and a reliance on evidence although the focus of common-sense inquiry tends to be different. Common-sense inquiry deals with issues or problems that are in the immediate environment where people live, whereas the objects of science inquiry are more likely to be distant (e.g. spintronics) from familiar experiences in people’s daily lives. While we acknowledge the fundamental differences (such as novel discovery compared with re-discovering science, ‘messy’ science compared with ‘sanitised’ science) between school science and science that is practiced by scientists, the subject of interest in science (understanding the world around us) remains the same.

The unfamiliarity between the functionality and purpose of science inquiry to improve the daily lives of learners does little to motivate learners to learn science (Aikenhead, 2006 ; Lee & Luykx, 2006 ) since learners may not appreciate the connections of science inquiry in their day-to-day needs and wants. Bereiter ( 1992 ) has also distinguished knowledge into two forms—referent-centred and problem-centred. Referent-centred knowledge refers to subject-matter that is organised around topics such as that in textbooks. Problem-centred knowledge is knowledge that is organised around problems, whether they are transient problems, practical problems or problems of explanations. Bereiter argued that referent-centred knowledge that is commonly taught in schools is limited in their applications and meaningfulness to the lives of students. This lack of familiarity and affinity to referent-centred knowledge is likened to the science subject-matter knowledge that was mentioned by Dewey. Rather, it is problem-centred knowledge that would be useful when students encounter problems. Learning problem-centred knowledge will allow learners to readily harness the relevant knowledge base that is useful to understand and solve specific problems. This suggests a need to help learners make the meaningful connections between science and their daily lives.

Further, Dewey opined that while the contexts in which scientific knowledge arise could be different from our daily common-sense world, careful consideration of scientific activities and applying the resultant knowledge to daily situations for use and enjoyment is possible. Similarly, in arguing for problem-centred knowledge, Bereiter ( 1992 ) questioned the value of inert knowledge that plays no role in helping us understand or deal with the world around us. Referent-centred knowledge has a higher tendency to be inert due to the way that the knowledge is organised and the way that the knowledge is encountered by learners. For instance, learning about the equation and conditions for photosynthesis is not going to help learners appreciate how plants are adapted for photosynthesis and how these adaptations can allow plants to survive changes in climate and for farmers to grow plants better by creating the best growing conditions. Rather, students could be exposed to problems of explanations where they are asked to unravel the possible reasons for low crop yield and suggest possible ways to overcome the problem. Hence, we argue here that the value of the referent knowledge is that they form the basis and foundation for the students to be able to discuss or suggest ways to overcome real life problems. Referent-centred knowledge serves as part of the relevant knowledge base that can be harnessed to solve specific problems or as foundational knowledge students need to progress to learn higher-order conceptual knowledge that typically forms the foundations or pillars within a discipline. This notion of referent-centred knowledge serving as foundational knowledge that can be and should be activated for application in problem-solving situation is shown by Delahunty et al. ( 2020 ). They found that students show high reliance on memory when they are conceptualising convergent problem-solving tasks.

While Bereiter argues for problem-centred knowledge, he cautioned that engagement should be with problems of explanation rather than transient or practical problems. He opined that if learners only engage in transient or practical problem alone, they will only learn basic-category types of knowledge and fail to understand higher-order conceptual knowledge. For example, for photosynthesis, basic-level types of knowledge included facts about the conditions required for photosynthesis, listing the products formed from the process of photosynthesis and knowing that green leaves reflect green light. These basic-level knowledges should intentionally help learners learn higher-level conceptual knowledge that include learners being able to draw on the conditions for photosynthesis when they encounter that a plant is not growing well or is exhibiting discoloration of leaves.

Transient problems disappear once a solution becomes available and there is a high likelihood that we will not remember the problem after that. Practical problems, according to Bereiter are “stuck-door” problems that could be solved with or without basic-level knowledge and often have solutions that lacks precise definition. There are usually a handful of practical strategies, such as pulling or pushing the door harder, kicking the door, etc. that will work for the problems. All these solutions lack a well-defined approach related to general scientific principles that are reproducible. Problems of explanations are the most desirable types of problems for learners since these are problems that persist and recur such that they can become organising points for knowledge. Problems of explanations consist of the conceptual representations of (1) a text base that serves to represent the text content and (2) a situation model that shows the portion of the world in which the text is relevant. The idea of text base to represent text content in solving problems of explanations is like the idea of domain knowledge and structural knowledge (refers to knowledge of how concepts within a domain are connected) proposed by Jonassen ( 2000 ). He argued that both types of knowledges are required to solve a range of problems from well-structured problems to ill-structured problems with a simulated context, to simple ill-structured problems and to complex ill-structured problems.

Jonassen indicated that complex ill-structured problems are typically design problems and are likely to be the most useful forms of problems for learners to be engaged in inquiry. Complex ill-structured design problems are the “wicked” problems that Buchanan ( 1992 ) discussed. Buchanan’s idea is that design aims to incorporate knowledge from different fields of specialised inquiry to become whole. Complex or wicked problems are akin to the work of scientists who navigate multiple factors and evidence to offer models that are typically oversimplified, but they apply them to propose possible first approximation explanations or solutions and iteratively relax constraints or assumptions to refine the model. The connections between the subject matter of science and the design process to engineer a solution are delicate. While it is important to ensure that practical concerns and questions are taken into consideration in designing solutions (particularly a material artefact) to a practical problem, the challenge here lies in ensuring that creativity in design is encouraged even if students initially lack or neglect the scientific conceptual understanding to explain/justify their design. In his articulation of wicked problems and the role of design thinking, Buchanan ( 1992 ) highlighted the need to pay attention to category and placement. Categories “have fixed meanings that are accepted within the framework of a theory or a philosophy and serve as the basis for analyzing what already exist” (Buchanan, 1992 , p. 12). Placements, on the other hand, “have boundaries to shape and constrain meaning, but are not rigidly fixed and determinate” (p. 12).

The difference in the ideas presented by Dewey and Bereiter lies in the problem design. For Dewey, scientific knowledge could be learnt from inquiring into practical problems that learners are familiar with. After all, Dewey viewed “modern science as continuous with, and to some degree an outgrowth and refinement of, practical or ‘common-sense’ inquiry” (Brown, 2012 ). For Bereiter, he acknowledged the importance of familiar experiences, but instead of using them as starting points for learning science, he argued that practical problems are limiting in helping learners acquire higher-order knowledge. Instead, he advocated for learners to organize their knowledge around problems that are complex, persistent and extended and requiring explanations to better understand the problems. Learners are to have a sense of the kinds of problems to which the specific concept is relevant before they can be said to have grasp the concept in a functionally useful way.

To connect between problem solving, scientific knowledge and everyday experiences, we need to examine ways to re-negotiate the disciplinary boundaries (such as epistemic understanding, object of inquiry, degree of precision) of science and make relevant connections to common-sense inquiry and to the problem at hand. Integrated STEM appears to be one way in which the disciplinary boundaries of science can be re-negotiated to include practices from the fields of technology, engineering and mathematics. In integrated STEM learning, inquiry is seen more holistically as a fluid process in which the outcomes are not absolute but are tentative. The fluidity of the inquiry process is reflected in the non-deterministic inquiry approach. This means that students can use science inquiry, engineering design, design process or any other inquiry approaches that fit to arrive at the solution. This hybridity of inquiry between science, common-sense and problems allows for some familiar aspects of the science inquiry process to be applied to understand and generate solutions to familiar everyday problems. In attempting to infuse elements of common-sense inquiry with science inquiry in problem-solving, logic plays an important role to help learners make connections. Hypothetically, we argue that with increasing exposure to less familiar ways of thinking such as those associated with science inquiry, students’ familiarity with scientific reasoning increases, and hence such ways of thinking gradually become part of their common-sense, which students could employ to solve future relevant problems. The theoretical ideas related to complexities of problems, the different forms of inquiry afforded by different problems and the arguments for engaging in problem solving motivated us to examine empirically how learners engage with ill-structured problems to generate problem-centred knowledge. Of particular interest to us is how learners and teachers weave between practical and scientific reasoning as they inquire to integrate the components in the original problem into a unified whole.

3.1 Context

The integrated STEM activity in our study was planned using the S-T-E-M quartet instructional framework (Tan et al., 2019 ). The S-T-E-M quartet instructional framework positions complex, persistent and extended problems at its core and focusses on the vertical disciplinary knowledge and understanding of the horizontal connections between the disciplines that could be gained by learners through solving the problem (Tan et al., 2019 ). Figure  1 depicts the disciplinary aspects of the problem that was presented to the students. The activity has science and engineering as the two lead disciplines. It spanned three 1-h lessons and required students to both learn and apply relevant scientific conceptual knowledge to solve a complex, real-world problem through processes that resemble the engineering design process (Wheeler et al., 2019 ).

Connections across disciplines in integrate STEM activity

Frequency of different types of reasoning

In the first session (1 h), students were introduced to the problem and its context. The problem pertains to the issue of limited farmland in a land scarce country that imports 90% of food (Singapore Food Agency [SFA], 2020 ). The students were required to devise a solution by applying knowledge of the conditions required for photosynthesis and plant growth to design and build a vertical farming system to help farmers increase crop yield with limited farmland. This context was motivated by the government’s effort to generate interests and knowledge in farming to achieve the 30 by 30 goal—supplying 30% of country’s nutritional needs by 2030. The scenario was a fictitious one where they were asked to produce 120 tonnes of Kailan (a type of leafy vegetable) with two hectares of land instead of the usual six hectares over a specific period. In addition to the abovementioned constraints, the teacher also discussed relevant success criteria for evaluating the solution with the students. Students then researched about existing urban farming approaches. They were given reading materials pertaining to urban farming to help them understand the affordances and constraints of existing solutions. In the second session (6 h), students engaged in ideation to generate potential solutions. They then designed, built and tested their solution and had opportunities to iteratively refine their solution. Students were given a list of materials (e.g. mounting board, straws, ice-cream stick, glue, etc.) that they could use to design their solutions. In the final session (1 h), students presented their solution and reflected on how well their solution met the success criteria. The prior scientific conceptual knowledge that students require to make sense of the problem include knowledge related to plant nutrition, namely, conditions for photosynthesis, nutritional requirements of Kailin and growth cycle of Kailin. The problem resembles a real-world problem that requires students to engage in some level of explanation of their design solution.

A total of 113 eighth graders (62 boys and 51 girls), 14-year-olds, from six classes and their teachers participated in the study. The students and their teachers were recruited as part of a larger study that examined the learning experiences of students when they work on integrated STEM activities that either begin with a problem, a solution or are focused on the content. Invitations were sent to schools across the country and interested schools opted in for the study. For the study reported here, all students and teachers were from six classes within a school. The teachers had all undergone 3 h of professional development with one of the authors on ways of implementing the integrated STEM activity used in this study. During the professional development session, the teachers learnt about the rationale of the activity, familiarize themselves with the materials and clarified the intentions and goals of the activity. The students were mostly grouped in groups of three, although a handful of students chose to work independently. The group size of students was not critical for the analysis of talk in this study as the analytic focus was on the kinds of knowledge applied rather than collaborative or group think. We assumed that the types of inquiry adopted by teachers and students were largely dependent on the nature of problem. Eighth graders were chosen for this study since lower secondary science offered at this grade level is thematic and integrated across biology, chemistry and physics. Furthermore, the topic of photosynthesis is taught under the theme of Interactions at eighth grade (CPDD, 2021 ). This thematic and integrated nature of science at eighth grade offered an ideal context and platform for integrated STEM activities to be trialled.

The final lessons in a series of three lessons in each of the six classes was analysed and reported in this study. Lessons where students worked on their solutions were not analysed because the recordings had poor audibility due to masking and physical distancing requirements as per COVID-19 regulations. At the start of the first lesson, the instructions given by the teacher were:

You are going to present your models. Remember the scenario that you were given at the beginning that you were tasked to solve using your model. …. In your presentation, you have to present your prototype and its features, what is so good about your prototype, how it addresses the problem and how it saves costs and space. So, this is what you can talk about during your presentation. ….. pay attention to the presentation and write down questions you like to ask the groups after the presentation… you can also critique their model, you can evaluate, critique and ask questions…. Some examples of questions you can ask the groups are? Do you think your prototype can achieve optimal plant growth? You can also ask questions specific to their models.

3.2 Data collection

Parental consent was sought a month before the start of data collection. The informed consent adhered to confidentiality and ethics guidelines as described by the Institutional Review Board. The data collection took place over a period of one month with weekly video recording. Two video cameras, one at the front and one at the back of the science laboratory were set up. The front camera captured the students seated at the front while the back video camera recorded the teacher as well as the groups of students at the back of the laboratory. The video recordings were synchronized so that the events captured from each camera can be interpreted from different angles. After transcription of the raw video files, the identities of students were substituted with pseudonyms.

3.3 Data analysis

The video recordings were analysed using the qualitative content analysis approach. Qualitative content analysis allows for patterns or themes and meanings to emerge from the process of systematic classification (Hsieh & Shannon, 2005 ). Qualitative content analysis is an appropriate analytic method for this study as it allows us to systematically identify episodes of practical inquiry and science inquiry to map them to the purposes and outcomes of these episodes as each lesson unfolds.

In total, six h of video recordings where students presented their ideas while the teachers served as facilitator and mentor were analysed. The video recordings were transcribed, and the transcripts were analysed using the NVivo software. Our unit of analysis is a single turn of talk (one utterance). We have chosen to use utterances as proxy indicators of reasoning practices based on the assumption that an utterance relates to both grammar and context. An utterance is a speech act that reveals both meaning and intentions of the speaker within specific contexts (Li, 2008 ).

Our research analytical lens is also interpretative in nature and the validity of our interpretation is through inter-rater discussion and agreement. Each utterance at the speaker level in transcripts was examined and coded either as relevant to practical reasoning or scientific reasoning based on the content. The utterances could be a comment by the teacher, a question by a student or a response by another student. Deductive coding is deployed with the two codes, practical reasoning and scientific reasoning derived from the theoretical ideas of Dewey and Bereiter as described earlier. Practical reasoning refers to utterances that reflect commonsensical knowledge or application of everyday understanding. Scientific reasoning refers to utterances that consist of scientifically oriented questions, scientific terms, or the use of empirical evidence to explain. Examples of each type of reasoning are highlighted in the following section. Each coded utterance is then reviewed for detailed description of the events that took place that led to that specific utterance. The description of the context leading to the utterance is considered an episode. The episodes and codes were discussed and agreed upon by two of the authors. Two coders simultaneously watched the videos to identify and code the episodes. The coders interpreted the content of each utterance, examine the context where the utterance was made and deduced the purpose of the utterance. Once each coder has established the sense-making aspect of the utterance in relation to the context, a code of either practical reasoning or scientific reasoning is assigned. Once that was completed, the two coders compared their coding for similarities and differences. They discussed the differences until an agreement was reached. Through this process, an agreement of 85% was reached between the coders. Where disagreement persisted, codes of the more experienced coder were adopted.

4 Results and Discussion

The specific STEM lessons analysed were taken from the lessons whereby students presented the model of their solutions to the class for peer evaluation. Every group of students stood in front of the class and placed their model on the bench as they presented. There was also a board where they could sketch or write their explanations should they want to. The instructions given by the teacher to the students were to explain their models and state reasons for their design.

4.1 Prevalence of Reasoning

The 6h of videos consists of 1422 turns of talk. Three hundred four turns of talk (21%) were identified as talk related to reasoning, either practical reasoning or scientific reasoning. Practical reasoning made up 62% of the reasoning turns while 38% were scientific reasoning (Fig. 2 ).

The two types of reasoning differ in the justifications that are used to substantiate the claims or decisions made. Table 1 describes the differences between the two categories of reasoning.

4.2 Applications of Scientific Reasoning

Instances of engagement with scientific reasoning (for instance, using scientific concepts to justify, raising scientifically oriented questions, or providing scientific explanations) revolved around the conditions for photosynthesis and the concept of energy conversion when students were presenting their ideas or when they were questioned by their peers. For example, in explaining the reason for including fish in their plant system, one group of students made connection to cyclical energy transfer: “…so as the roots of the plants submerged in the water, faeces from the fish will be used as fertilizers so that the plant can grow”. The students considered how organic matter that is still trapped within waste materials can be released and taken up by plants to enhance the growth. The application of scientific reasoning made their design one that is innovative and sustainable as evaluated by the teacher. Some students attempted more ecofriendly designs by considering energy efficiencies through incorporating water turbines in their farming systems. They applied the concept of different forms of energy and energy conversion when their peers inquired about their design. The same scientific concepts were explained at different levels of details by different students. At one level, the students explained in a purely descriptive manner of what happens to the different entities in their prototypes, with implied changes to the forms of energy─ “…spins then generates electricity. So right, when the water falls down, then it will spin. The water will fall on the fan blade thing, then it will spin and then it generates electricity. So, it saves electricity, and also saves water”. At another level, students defended their design through an explanation of energy conversion─ “…because when the water flows right, it will convert gravitational potential energy so, when it reaches the bottom, there is not really much gravitational potential energy”. While these instances of applying scientific reasoning indicated that students have knowledge about the scientific phenomena and can apply them to assist in the problem-solving process, we are not able to establish if students understood the science behind how the dynamo works to generate electricity. Students in eighth grade only need to know how a generator works at a descriptive level and the specialized understanding how a dynamo works is beyond the intended learning outcomes at this grade level.

The application of scientific concepts for justification may not always be accurate. For instance, the naïve conception that students have about plants only respiring at night and not in the day surfaced when one group of students tried to justify the growth rates of Kailan─ “…I mean, they cannot be making food 24/7 and growing 24/7. They have nighttime for a reason. They need to respire”. These students do not appreciate that plants respire in the day as well, and hence respiration occurs 24/7. This naïve conception that plants only respire at night is one that is common among learners of biology (e.g. Svandova, 2014 ) since students learn that plant gives off oxygen in the day and takes in oxygen at night. The hasty conclusion to that observation is that plants carry out photosynthesis in the day and respire at night. The relative rates of photosynthesis and respiration were not considered by many students.

Besides naïve conceptions, engagement with scientific ideas to solve a practical problem offers opportunities for unusual and alternative ideas about science to surface. For instance, another group of students explained that they lined up their plants so that “they can take turns to absorb sunlight for photosynthesis”. These students appear to be explaining that the sun will move and depending on the position of the sun, some plants may be under shade, and hence rates of photosynthesis are dependent on the position of the sun. However, this idea could also be interpreted as (1) the students failed to appreciate that sunlight is everywhere, and (2) plants, unlike animals, particularly humans, do not have the concept of turn-taking. These diverse ideas held by students surfaced when students were given opportunities to apply their knowledge of photosynthesis to solve a problem.

4.3 Applications of Practical Reasoning

Teachers and students used more practical reasoning during an integrated STEM activity requiring both science and engineering practices as seen from 62% occurrence of practical reasoning compared with 38% for scientific reasoning. The intention of the activity to integrate students’ scientific knowledge related to plant nutrition to engineering practice of building a model of vertical farming system could be the reason for the prevalence of practical reasoning. The practical reasoning used related to structural design considerations of the farming system such as how water, light and harvesting can be carried out in the most efficient manner. Students defended the strengths of designs using logic based on their everyday experiences. In the excerpt below (transcribed verbatim), we see students applied their everyday experiences when something is “thinner” (likely to mean narrower), logically it would save space. Further, to reach a higher level, you use a machine to climb up.

Excerpt 1. “Thinner, more space” Because it is more thinner, so like in terms of space, it’s very convenient. So right, because there is – because it rotates right, so there is this button where you can stop it. Then I also installed steps, so that – because there are certain places you can’t reach even if you stop the – if you stop the machine, so when you stop it and you climb up, and then you see the condition of the plants, even though it costs a lot of labour, there is a need to have an experienced person who can grow plants. Then also, when like – when water reach the plants, cos the plants I want to use is soil-based, so as the water reach the soil, the soil will xxx, so like the water will be used, and then we got like – and then there’s like this filter that will filter like the dirt.

In the examples of practical reasoning, we were not able to identify instances where students and teachers engaged with discussion around trade-off and optimisation. Understanding constraints, trade-offs and optimisations are important ideas in informed design matrix for engineering as suggested by Crismond and Adams ( 2012 ). For instance, utterances such as “everything will be reused”, “we will be saving space”, “it looks very flimsy” or “so that it can contains [sic] the plants” were used. These utterances were made both by students while justifying their own prototypes and also by peers who challenged the design of others. Longer responses involving practical reasoning were made based on common-sense, everyday logic─ “…the product does not require much manpower, so other than one or two supervisors like I said just now, to harvest the Kailan, hence, not too many people need to be used, need to be hired to help supervise the equipment and to supervise the growth”. We infer that the higher instances of utterances related to practical reasoning could be due to the presence of more concrete artefacts that is shown, and the students and teachers were more focused on questioning the structure at hand. This inference was made as instructions given by the teacher at the start of students’ presentation focus largely on the model rather than the scientific concepts or reasoning behind the model.

4.4 Intersection Between Scientific and Practical Reasoning

Comparing science subject matter knowledge and problem-solving to the idea of categories and placement (Buchanan, 1992 ), subject matter is analogous to categories where meanings are fixed with well-established epistemic practices and norms. The problem-solving process and design of solutions are likened to placements where boundaries are less rigid, hence opening opportunities for students’ personal experiences and ideas to be presented. Placements allow students to apply their knowledge from daily experiences and common-sense logic to justify decisions. Common-sense knowledge and logic are more accessible, and hence we observe higher frequency of usage. Comparatively, while science subject matter (categories) is also used, it is observed less frequently. This could possibly be due either to less familiarity with the subject matter or lack of appropriate opportunity to apply in practical problem solving. The challenge for teachers during implementation of a STEM problem-solving activity, therefore, lies in the balance of the application of scientific and practical reasoning to deepen understanding of disciplinary knowledge in the context of solving a problem in a meaningful manner.

Our observations suggest that engaging students with practical inquiry tasks with some engineering demands such as the design of modern farm systems offers opportunities for them to convert their personal lived experiences into feasible concrete ideas that they can share in a public space for critique. The peer critique following the sharing of their practical ideas allows for both practical and scientific questions to be asked and for students to defend their ideas. For instance, after one group of students presented their prototype that has silvered surfaces, a student asked a question: “what is the function of the silver panels?”, to which his peers replied : “Makes the light bounce. Bounce the sunlight away and then to other parts of the tray.” This question indicated that students applied their knowledge that shiny silvered surfaces reflect light, and they used this knowledge to disperse the light to other trays where the crops were growing. An example of a practical question asked was “what is the purpose of the ladder?”, to which the students replied: “To take the plants – to refill the plants, the workers must climb up”. While the process of presentation and peer critique mimic peer review in the science inquiry process, the conceptual knowledge of science may not always be evident as students paid more attention to the design constraints such as lighting, watering, and space that was set in the activity. Given the context of growing plants, engagement with the science behind nutritional requirements of plants, the process of photosynthesis, and the adaptations of plants could be more deliberately explored.

5 Conclusion

The goal of our work lies in applying the theoretical ideas of Dewey and Bereiter to better understand reasoning practices in integrate STEM problem solving. We argue that this is a worthy pursue to better understand the roles of scientific reasoning in practical problem solving. One of the goals of integrated STEM education in schools is to enculture students into the practices of science, engineering and mathematics that include disciplinary conceptual knowledge, epistemic practices, and social norms (Kelly & Licona, 2018 ). In the integrated form, the boundaries and approaches to STEM learning are more diverse compared with monodisciplinary ways of problem solving. For instance, in integrated STEM problem solving, besides scientific investigations and explanations, students are also required to understand constraints, design optimal solutions within specific parameters and even to construct prototypes. For students to learn the ways of speaking, doing and being as they participate in integrated STEM problem solving in schools in a meaningful manner, students could benefit from these experiences.

With reference to the first research question of What is the extent of practical and scientific reasoning in integrated STEM problem solving, our analysis suggests that there are fewer instances of scientific reasoning compared with practical reasoning. Considering the intention of integrated STEM learning and adopting Bereiter’s idea that students should learn higher-order conceptual knowledge through engagement with problem solving, we argue for a need for scientific reasoning to be featured more strongly in integrated STEM lessons so that students can gain higher order scientific conceptual knowledge. While the lessons observed were strong in design and building, what was missing in generating solutions was the engagement in investigations, where learners collected or are presented with data and make decisions about the data to allow them to assess how viable the solutions are. Integrated STEM problems can be designed so that science inquiry can be infused, such as carrying out investigations to figure out relationships between variables. Duschl and Bybee ( 2014 ) have argued for the need to engage students in problematising science inquiry and making choices about what works and what does not.

With reference to the second research question , What is achieved through practical and scientific reasoning during integrated STEM problem solving? , our analyses suggest that utterance for practical reasoning are typically used to justify the physical design of the prototype. These utterances rely largely on what is observable and are associated with basic-level knowledge and experiences. The higher frequency of utterances related to practical reasoning and the nature of the utterances suggests that engagement with practical reasoning is more accessible since they relate more to students’ lived experiences and common-sense. Bereiter ( 1992 ) has urged educators to engage learners in learning that is beyond basic-level knowledge since accumulation of basic-level knowledge does not lead to higher-level conceptual learning. Students should be encouraged to use scientific knowledge also to justify their prototype design and to apply scientific evidence and logic to support their ideas. Engagement with scientific reasoning is preferred as conceptual knowledge, epistemic practices and social norms of science are more widely recognised compared with practical reasoning that are likely to be more varied since they rely on personal experiences and common-sense. This leads us to assert that both context and content are important in integrated STEM learning. Understanding the context or the solution without understanding the scientific principles that makes it work makes the learning less meaningful since we “…cannot strip learning of its context, nor study it in a ‘neutral’ context. It is always situated, always relayed to some ongoing enterprise”. (Bruner, 2004 , p. 20).

To further this discussion on how integrated STEM learning experiences harness the ideas of practical and scientific reasoning to move learners from basic-level knowledge to higher-order conceptual knowledge, we propose the need for further studies that involve working with teachers to identify and create relevant problems-of-explanations that focuses on feasible, worthy inquiry ideas such as those related to specific aspects of transportation, alternative energy sources and clean water that have impact on the local community. The design of these problems can incorporate opportunities for systematic scientific investigations and scaffolded such that there are opportunities to engage in epistemic practices of the constitute disciplines of STEM. Researchers could then examine the impact of problems-of-explanations on students’ learning of higher order scientific concepts. During the problem-solving process, more attention can be given to elicit students’ initial and unfolding ideas (practical) and use them as a basis to start the science inquiry process. Researchers can examine how to encourage discussions that focus on making meaning of scientific phenomena that are embedded within specific problems. This will help students to appreciate how data can be used as evidence to support scientific explanations as well as justifications for the solutions to problems. With evidence, learners can be guided to work on reasoning the phenomena with explanatory models. These aspects should move engagement in integrated STEM problem solving from being purely practice to one that is explanatory.

6 Limitations

There are four key limitations of our study. Firstly, the degree of generalisation of our observations is limited. This study sets out to illustrate what how Dewey and Bereiter’s ideas can be used as lens to examine knowledge used in problem-solving. As such, the findings that we report here is limited in its ability to generalise across different contexts and problems. Secondly, the lessons that were analysed came from teacher-frontal teaching and group presentation of solution and excluded students’ group discussions. We acknowledge that there could potentially be talk that could involve practical and scientific reasonings within group work. There are two practical consideration for choosing to analyse the first and presentation segments of the suite of lesson. Firstly, these two lessons involved participation from everyone in class and we wanted to survey the use of practical and scientific reasoning by the students as a class. Secondly, methodologically, clarity of utterances is important for accurate analysis and as students were wearing face masks during the data collection, their utterances during group discussions lack the clarity for accurate transcription and analysis. Thirdly, insights from this study were gleaned from a small sample of six classes of students. Further work could involve more classes of students although that could require more resources devoted to analysis of the videos. Finally, the number of students varied across groups and this could potentially affect the reasoning practices during discussions.

Data Availability

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to acknowledge the contributions of the other members of the research team who gave their comment and feedback in the conceptualization stage.

This study is funded by Office of Education Research grant OER 24/19 TAL.

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Tan, AL., Ong, Y.S., Ng, Y.S. et al. STEM Problem Solving: Inquiry, Concepts, and Reasoning. Sci & Educ 32 , 381–397 (2023). https://doi.org/10.1007/s11191-021-00310-2

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Reasoning processes in clinical reasoning: from the perspective of cognitive psychology

Hyoung seok shin.

Department of Medical Education, Korea University College of Medicine, Seoul, Korea

Clinical reasoning is considered a crucial concept in reaching medical decisions. This paper reviews the reasoning processes involved in clinical reasoning from the perspective of cognitive psychology. To properly use clinical reasoning, one requires not only domain knowledge but also structural knowledge, such as critical thinking skills. In this paper, two types of reasoning process required for critical thinking are discussed: inductive and deductive. Inductive and deductive reasoning processes have different features and are generally appropriate for different types of tasks. Numerous studies have suggested that experts tend to use inductive reasoning while novices tend to use deductive reasoning. However, even experts sometimes use deductive reasoning when facing challenging and unfamiliar problems. In clinical reasoning, expert physicians generally use inductive reasoning with a holistic viewpoint based on a full understanding of content knowledge in most cases. Such a problem-solving process appears as a type of recognition-primed decision making only in experienced physicians’ clinical reasoning. However, they also use deductive reasoning when distinct patterns of illness are not recognized. Therefore, medical schools should pursue problem-based learning by providing students with various opportunities to develop the critical thinking skills required for problem solving in a holistic manner.

Introduction

It is hard to describe clinical reasoning in a sentence, because it has been studied by a number of researchers from various perspectives, such as medical education, cognitive psychology, clinical psychology, and so forth, and they have failed to reach an agreement on its basic characteristics [ 1 ]. Accordingly, clinical reasoning has been defined in various ways. Some researchers defined clinical reasoning as a crucial skill or ability that all physicians should have for their clinical decision making, regardless of their area of expertise [ 2 , 3 ]. Others focused more on the processes of clinical reasoning; thus, they defined it as a complex process of identifying the clinical issues to propose a treatment plan [ 4 - 6 ]. However, these definitions are not so different. Taking this into account, it can be concluded that clinical reasoning is used to analyze patients’ status and arrive at a medical decision so that doctors can provide the proper medical treatment.

In reality, properly working clinical reasoning requires three domains of knowledge: diagnostic knowledge, etiological knowledge, and treatment knowledge [ 6 ]. From the perspective of cognitive psychology, structural knowledge is needed to integrate domain knowledge and find solutions based on the learner’s prior knowledge and experience [ 7 ], and structural knowledge can be constructed as a form of mental model by understanding the relations between the interconnected factors involved in clinical issues [ 8 , 9 ]. In this cognitive process, critical thinking skills such as causal reasoning and systems thinking can play a pivotal role in developing deeper understanding of given problem situations. Causal reasoning is the ability to identify causal relationships between sets of causes and effects [ 10 ]. Causality often involves a series or chain of events that can be used to infer or predict the effects and consequences of a particular cause [ 10 - 13 ]. Systems thinking is a thinking paradigm or conceptual framework where understanding is defined in terms of how well one is able to break a complex system down into its component parts [ 14 , 15 ]. It is based on the premise that a system involves causality between factors that are parts of the system as a whole [ 14 ]. Systems thinking is a process for achieving a deeper understanding of complex phenomena that are composed of components that are causally interrelated [ 14 - 16 ]. As a result, causal reasoning and systems thinking are skills that can help people to better understand complex phenomena in order to arrive at effective and targeted solutions that address the root causes of complex problems [ 10 , 12 , 15 ].

If cognitive skills work properly, one can make correct decisions all of the time. However, human reasoning is not always logical, and people often make mistakes in their reasoning. The more difficult the problems with which they are presented, the more likely they are to choose wrong answers that are produced by errors or flaws in the reasoning process [ 17 , 18 ]. Individual differences in reasoning skills—such as systems thinking, causal reasoning, and thinking processes—may influence and explain observed differences in their understanding. Therefore, to better assist learners in solving problems, instructors should focus more on facilitating the reasoning skills required to solve given problems successfully.

In this review paper, the author focuses on the reasoning processes involved in clinical reasoning, given that clinical reasoning is considered as a sort of problem-solving process. Therefore, this paper introduces concepts related to the reasoning processes involved in clinical reasoning and their influences on novices and experts in the field of medical education from the perspective of cognitive psychology. Then, based on the contents discussed, the author will be able to propose specific instructional strategies associated with reasoning processes to improve medical students’ reasoning skills to enhance their clinical reasoning.

Concepts and nature of reasoning processes

Generally, reasoning processes can be categorized into two types: inductive/forward and deductive/backward [ 19 ]. In an inductive reasoning process, one observes several individual facts first, then makes a conclusion about a premise or principle based on these facts. Yet there may be the possibility that a conclusion is not true even though a premise or principle in support of that conclusion is true, because the conclusion is generalized from the facts observed by the learner, but the learner does not observe all relevant examples [ 20 ].

In general, in a deductive reasoning process, according to Johnson-Laird [ 20 ], one establishes a mental model or a set of models to solve given problems considering general knowledge and principles based on a solid foundation. Then, one makes a conclusion or finds a solution based on the mental model or set of models. To verify a mental model, one needs to check the validity of the conclusions or solutions by searching for counterexamples. If one cannot find any counterexamples, the conclusions can be accepted as true and the solutions as valid. Consequently, the initial mental model or set of models can be used for deductive reasoning.

Anderson [ 17 ] proposed three different ways of solving complex problems: means-ends analysis, working backward, and planning by simplification. A means-ends analysis is a process that gets rid of differences between the current state and the ideal state in order to determine sub-goals in solving problems, and the process can be repeated until the major goal is achieved [ 21 - 23 ]. It can be considered an inductive reasoning process, because the distinct feature of means-ends analysis where it achieves sub-goals in consecutive order is similar to inductive reasoning. Working backward is addressed as an opposite concept to means-ends analysis [ 17 ], because it needs to set up a desired result to find causes by measuring the gap between the current state and the ideal state; then, this process is repeated until the root causes of a problem are identified. According to Anderson [ 17 ], means-ends analysis (inductive reasoning) is more useful in finding a solution quickly when a limited number of options are given or many sub-goals should be achieved for the major goal; whereas working backward (deductive reasoning) spends more time removing wrong answers or inferences to find the root causes of a problem. In conclusion, inductive and deductive reasoning processes have different features and can play different roles in solving complex problems.

The use of reasoning processes

A number of researchers across different fields have used inductive and deductive approaches as reasoning processes to solve complex problems or complete tasks. For example, Scavarda et al. [ 24 ] used both approaches in their study to collect qualitative data through interviews with experts, and they found that experts with a deductive approach used a top-down approach and those with an inductive approach used a bottom-up approach to solve a given problem. In a study of Overmars et al. [ 25 ], the results showed that a deductive approach explicitly illustrated causal relations and processes in 39 geographic contexts and it was appropriate for evaluating various possible scenarios; whereas an inductive approach presented associations that did not guarantee causality and was more useful for identifying relatively detailed changes.

Sharma et al. [ 26 ] found that inductive or deductive approaches can both be useful depending on the characteristics of the tasks and resources available to solve problems. An inductive approach is considered a data-driven approach, which is a way to find possible outcomes based on rules detected from undoubted facts [ 26 ]. Therefore, if there is a lot of available data and an output hypothesis, then it is effective to use an inductive approach to discover solutions or unexpected and interesting findings [ 26 , 27 ]. An inductive approach makes it possible to directly reach conclusions via thorough reasoning that involves the following procedures: (1) recognize, (2) select, and (3) act [ 28 ]. These procedures are recurrent, but one cannot know how long they should be continued to complete a task, because a goal is not specified [ 26 ]. Consequently, an inductive approach is useful when analyzing an unstructured data set or system [ 29 ].

On the other hand, a deductive approach sets up a desired goal first, then finds a supporting basis—such as information and rules—for the goals [ 26 ]. For this, a backward approach, which is considered deductive reasoning, gradually gets rid of things proved unnecessary for achieving the goal while reasoning; therefore, it is regarded as a goal-driven approach [ 28 ]. If the output hypothesis is limited and it is necessary to find supporting facts from data, then a deductive approach would be effective [ 26 , 28 ]. This implies that a deductive approach is more appropriate when a system or phenomenon is well-structured and relationships between the components are clearly present [ 29 ]. Table 1 shows a summary of the features and differences of the inductive and deductive reasoning processes.

Features of Inductive and Deductive Reasoning Processes

The classification according to the reasoning processes in the table is dichotomous, but they do not always follow this classification absolutely. This means that each reasoning process shows such tendencies.

Considering the attributes of the two reasoning processes, an inductive approach is effective for exploratory tasks that do not have distinct goals—for example, planning, design, process monitoring, and so on, while a deductive approach is more useful for diagnostic and classification tasks [ 26 ]. In addition, an inductive approach is more useful for discovering solutions from an unstructured system. On the other hand, a deductive approach can be better used to identify root causes in a well-structured context. While both reasoning approaches are useful in particular contexts, it can be suggested that inductive reasoning is more appropriate than deductive reasoning in clinical situations, which focus on diagnosis and treatment of diseases rather than on finding their causes.

Reasoning processes by novices and experts

As mentioned above, which reasoning process is more effective for reaching conclusions can be generally determined depending on the context and purpose of the problem solving. In reality, however, learners’ choices are not always consistent with this suggestion, because they are affected not only by the problem itself, but also by the learner. Assuming that learners or individuals can be categorized into two types, novices and experts, based on their level of prior knowledge and structural knowledge, much research has shown that novices and experts use a different reasoning process for problem solving. For example, in a study of Eseryel et al. [ 30 ], novice instructional designers who possessed theoretical knowledge but little experience showed different patterns of ill-structured problem solving compared to experts with real-life experience. Given that each learner has a different level of prior knowledge relating to particular topics and critical thinking skills, selecting the proper reasoning process for each problem is quite complex. This section focuses on which reasoning process an individual uses depending on their content and structural knowledge.

Numerous studies have examined which reasoning processes are used by experts, who have sufficient content and structural knowledge, and novices, who have little content and structural knowledge, for problem solving. The result of a study of Hong et al. [ 31 ] showed that children generally performed better when using cause-effect inferences (inductive approach) than effect-cause inferences (considered a deductive approach). According to Anderson [ 17 ], people are faced with some difficulties when they solve problems using induction. The first difficulty is in formulating proper hypotheses and the second is that people do not know how to interpret negative evidence when it is given and reach a conclusion based on that evidence [ 17 ]. Nevertheless, most students use a type of inductive reasoning to solve problems that they have not previously faced [ 32 ]. Taken together, the studies suggest that novices generally prefer an inductive approach to a deductive approach for solving problems because they may feel comfortable and natural using an inductive approach but tend to experience difficulties during problem-solving processes. From these findings, it can be concluded that novices are more likely to use inductive reasoning, but it is not always productive.

Nevertheless, there is still a controversy about which reasoning processes are used by experts or novices [ 33 ]. For example, experts in specific domains use an inductive approach to solving problems, but novices, who have a lower level of prior knowledge in specific domains, tend to use a deductive approach [ 23 ]. In contrast, according to Smith [ 34 ], studies in which more familiar problems were used concluded that experts preferred an inductive approach, whereas in studies that employed relatively unfamiliar problems that required more time and effort to solve, experts tended to prefer a deductive approach. In line with this finding, in solving physics problems, experts mostly used inductive reasoning that was faster and had fewer errors for problem solving only when they encountered easy or familiar problems where they could gain a full understanding of the situation quickly, but novices took more time to deductively reason by planning and solving each step in the process of problem solving [ 35 ].

Assuming that an individual’s prior knowledge consists of content knowledge such as knowledge of specific domains as well as structural knowledge such as the critical thinking skills required for problem solving in the relevant field, it seems experts use an inductive approach when faced with relatively easy or familiar problems; while a deductive approach is used for relatively challenging, unfamiliar, or complex problems. In the case of novices, it may be better to use deductive reasoning for problem solving considering that they have a lower level of prior knowledge and that even experts use deductive reasoning to solve complex problems.

Inductive and deductive reasoning in clinical reasoning

In medicine, concepts of inductive and deductive reasoning apply to gathering appropriate information and making a clinical diagnosis considering that the medical treatment process is a form of problem solving. Inductive reasoning is used to make a diagnosis by starting with an analysis of observed clinical data [ 36 , 37 ]. Inductive reasoning is considered as scheme-inductive problem solving in medicine [ 36 ], because in inductive reasoning, one first constructs his/her scheme (also considered a mental model) based on one’s experiences and knowledge. It is generally used for a clinical presentation-based model, which has been most recently applied to medical education [ 38 ].

In contrast, deductive reasoning entails making a clinical diagnosis by testing hypotheses based on systematically collected data [ 39 ]. Deductive reasoning is considered an information-gathering method, because one constructs a hypothesis first then finds supporting or refuting facts from data [ 36 , 40 ]. It has been mostly used for discipline-based, system-based, and case-based models in medical education [ 38 ].

Inductive and deductive reasoning by novice and expert physicians

A growing body of research explores which reasoning processes are mainly used by novices and experts in clinical reasoning. Novice physicians generally use deductive reasoning, because limited knowledge restricts them from using deductive reasoning [ 1 , 38 ]. Also, it is hard to consider deductive reasoning as an approach generally used by experts, since they do not repeatedly test a hypothesis based on limited knowledge in order to move on to the next stage in the process of problem solving [ 38 ]. Therefore, it seems that deductive reasoning is generally used by novices, while inductive reasoning is used by expert physicians in general. However, this may be too conclusive and needs to be further examined in the context of clinical reasoning.

In clinical reasoning, inductive reasoning is more intuitive and requires a holistic view based on a full understanding of content knowledge, including declarative and procedural knowledge, but also structural knowledge; thus, it occurs only when physicians’ knowledge structures of given problems are highly organized [ 38 ]. Expert physicians recognize particular patterns of symptoms through repeated application of deductive reasoning, and the pattern recognition process makes it possible for them to apply inductive reasoning when diagnosing patients [ 10 ]. As experts automate a number of cognitive sequences required for problem solving in their own fields [ 35 ], expert physicians automatically make appropriate diagnoses following a process of clinical reasoning when they encounter patients who have familiar or typical diseases. Such a process of problem solving is called recognition-primed decision making (RPDM) [ 41 , 42 ]. It is a process of finding appropriate solutions to ill-structured problems in a limited timeframe [ 10 ]. In RPDM, expert physicians are aware of what actions should be taken when faced with particular situations based on hundreds of prior experiences [ 10 ]. These prior experiences are called illness scripts in diagnostic medicine [ 10 ], and this is a concept similar to a mental model or schema in problem solving.

However, expert physicians do not always use inductive reasoning in their clinical reasoning. Jonassen [ 10 ] categorized RPDM into three forms of variations in problem solving by experts, and the first form of variation is the simplest and easiest one based on inductive reasoning, as mentioned above. The second type of variation occurs when an encountered problem is somewhat atypical [ 10 ]. Even expert physicians are not always faced with familiar or typical diseases when treating patients. Expert physicians’ RPDM does not work automatically when faced with atypical symptoms, because they do not have sufficient experiences relevant to the atypical symptoms. In this case, it can be said that they have weak illness scripts or mental models of the given symptoms. In the second variation, experts need more information and will attempt to connect it to their prior knowledge and experiences [ 10 ]. Deductive reasoning is involved in this process so that problem solvers can test their hypotheses in order to find new patterns and construct new mental models based on the newly collected data and previous experiences. The third variation of RPDM is when expert physicians have no previous experience or prior knowledge of given problem situations; in other words, no illness script or mental model [ 10 ]. Jonassen [ 10 ] argued that a mental simulation is conducted to predict the consequences of various actions by experts in the third variation. This process inevitably involves repetitive deductive reasoning to test a larger number of hypotheses when making a diagnosis.

Similarly, from the perspective of dual process theory as a decision-making process, decision making is classified into two approaches based on the reasoning style: type 1 and type 2 (or system 1 and system 2) [ 43 , 44 ]. According to Croskerry [ 44 ], the type 1 decision-making process is intuitive and based on experiential-inductive reasoning, while type 2 is an analytical and hypothetico-deductive decision-making process [ 44 , 45 ]. A feature that distinguishes the two processes is whether a physician who encounters a patient’s symptoms succeeds in pattern recognition. If a physician recognizes prominent features of the visual presentation of illness, type 1 processes (or system 1) are operated automatically, whereas type 2 (or system 2) processes work if any distinct feature of illness presentation is not recognized [ 44 ].

Only experienced expert physicians can use RPDM [ 10 , 46 ] or type 1 and 2 processes [ 43 ], because it can occur solely based on various experiences and a wide range of prior knowledge that can be gained as a result of a huge amount of deductive reasoning since they were novices. Consequently, it can be concluded that expert physicians generally use more inductive reasoning when they automatically recognize key patterns of given problems or symptoms, while sometimes they also use deductive reasoning when they additionally need processes of hypothesis testing to recognize new patterns of symptoms.

From the perspective of cognitive processes, clinical reasoning is considered as one of the decision-making processes that finds the best solutions to patients’ illnesses. As a form of decision making for problem solving, two reasoning processes have been considered: inductive and deductive reasoning. Deductive reasoning can be used to make a diagnosis if physicians have insufficient knowledge, sufficient time, and the ability to analyze the current status of their patients. However, in reality, it is inefficient to conduct thorough deductive reasoning at each stage of clinical reasoning because only a limited amount of time is allowed for both physicians and patients to reach a conclusion in most cases. A few researchers have suggested that using deductive reasoning is more likely to result in diagnostic errors than inductive reasoning, because evidence-based research, such as deductive reasoning, focuses mainly on available and observable evidence and rules out the possibility of any other possible factors influencing the patient’s symptoms [ 37 , 38 ]. However, when a physician encounters unfamiliar symptom and the degree of uncertainty is high, deductive reasoning is required to reach the correct diagnosis through analytical and slow diagnostic processes by collecting data from resources [ 44 ]. Taken together, in order to make the most of a limited timeframe and reduce diagnostic errors, physicians should be encouraged to use inductive reasoning in their clinical reasoning as far as possible given that patterns of illness presentation are recognized.

Unfortunately, it is not always easy for novice physicians to apply inductive or deductive reasoning in all cases. Expert physicians have sufficient capabilities to use both inductive and deductive reasoning and can also automate their clinical reasoning based on inductive reasoning, because they have already gathered the wide range of experiences and knowledge required to diagnose various symptoms. Novice physicians should make a greater effort to use inductive reasoning when making diagnoses; however, it takes experiencing countless deductive reasoning processes to structure various illness scripts or strong mental models until they reach a professional level. As a result, teaching not only clinical reasoning as a whole process but also the critical thinking skills required for clinical reasoning is important in medical schools [ 47 ]. For this, medical schools should pursue problem-based learning by providing students with various opportunities to gain content knowledge as well as develop the critical thinking skills —such as data analysis skills, metacognitive skills, causal reasoning, systems thinking, and so forth—required for problem solving in a holistic manner so that they can improve their reasoning skills and freely use both inductive and deductive approaches in any context. Further studies will be reviewed to provide detailed guidelines or teaching tips on how to develop medical students’ critical thinking skills.

Acknowledgments

Conflicts of interest

No potential conflict of interest relevant to this article was reported.

Author contributions

All work was done by HS.

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Trend Report 2021 – Delivering Justice / Case Study: Problem-Solving Courts in the US

Author: Isabella Banks , Justice Sector Advisor

Introduction

Problem-solving courts are specialised courts that aim to treat the problems that underlie and contribute to certain kinds of crime (Wright, no date). “Generally, a problem-solving court involves a close collaboration between a judge and a community service team to develop a case plan and closely monitor a participant’s compliance, imposing proper sanctions when necessary” (Ibid).  In the past three decades, problem-solving courts have become a fixture in the American criminal justice landscape, with over 3,000 established nationwide. All 50 states have appointed a statewide drug court coordinator, and at least 13 have introduced the broader position of statewide problem-solving court coordinator (Porter, Rempel and Mansky 2010; J. Lang, personal communication, October 28, 2020).

What does it mean for a court to be problem-solving?

Although a number of different types of problem-solving courts exist across the US, they are generally organised around three common principles: problem-solving, collaboration, and accountability (Porter, Rempel and Mansky 2010, p. iii.).

Problem-solving courts are focused on solving the underlying problems of those who perpetrate or are affected by crime. This includes reducing recidivism as well as rehabilitating participants (with the exception of domestic violence courts, as elaborated below), victims and the broader community (Ibid. p. iii.).

Problem-solving courts are also characterised by interdisciplinary collaboration among stakeholders in and outside of the criminal justice system. Dedicated staff who have been assigned to the problem-solving court work together to develop court policies and resolve individual cases in a relatively non-adversarial way. Ongoing collaboration between court staff and public agencies, service providers and clinical experts is also essential for providing appropriate treatment to problem-solving court participants (Ibid. p. 38). Because problem-solving courts aim to address the impact of crime on the community and increase public trust in justice, they also have frequent contact with community members and organisations and regularly solicit local input on their work (Ibid. p. 39).

Problem-solving courts aim to hold individuals with justice system involvement, service providers and themselves accountable to the broader community. For individuals with justice system involvement, this means holding them accountable for their criminal behaviour by promoting and monitoring their compliance with court mandates. In order to comply, problem-solving court participants must understand what is expected of them, regularly appear for status hearings, and have clear (extrinsic and intrinsic) incentives to complete their mandates. 

For service providers, this means providing services based on a coherent, specified and effective model, and accurately and regularly informing the court about participants’ progress. Problem-solving courts are also responsible for assessing the quality of service delivery and making sure models are adhered to (Ibid. p. 43-44). 

Lastly and perhaps most fundamentally, problem-solving courts must hold themselves to “the same high standards expected of participants and stakeholders” (Ibid. p. 44-45).  This means monitoring implementation and outcomes of their services using up-to-date data. 

What does problem-solving justice look like in practice?

Problem-solving justice comes in different forms. The original, best known, and most widespread problem-solving court model is the drug court. The first drug was created in 1989, after a judge in Miami Dade county became frustrated seeing the same drug cases cycling through her court and began experimenting with putting defendants into treatment (P. Hora, personal communication, October 16, 2020). This approach (elaborated in the sections that follow) gradually gained traction, and there are now over 3,000 drug courts across the US (Strong and Kyckelhahn 2016).

This proliferation of drug courts helped stimulate the emergence of three other well-known problem-solving court models: mental health, domestic violence and community courts (Porter, Rempel and Mansky 2010, p. iii.). Mental health courts are similar to drug courts in that they focus on rehabilitation, but different in that they aim for the improved social functioning and stability of their participants rather than complete abstinence (Ibid. p. 51). Domestic violence courts are unique in that they do not universally embrace participant treatment and rehabilitation as an important goal. Instead, many – thought not all – are primarily focused on victim support and safety and participant accountability and deterrence (Ibid. p. 52). 

Community courts “seek to address crime, public safety, and quality of life problems at the neighbourhood level. Unlike other problem-solving courts…community courts do not specialise in one particular problem. Rather, the goal of community courts is to address the multiple problems and needs that contribute to social disorganisation in a designated geographical area. For this reason, community courts vary widely in response to varying local needs, conditions, and priorities” (Lee et al. 2013). There are now over 70 community courts in operation around the world (Lee et al. 2013, p.1). Some are based in traditional courthouses, while others work out of storefronts, libraries or former schools. Though they typically focus on criminal offences, some community courts extend their jurisdiction to non-criminal matters to meet specific needs of the communities they serve as well (Ibid. p. 1.). Regardless of location and jurisdiction, all community courts take a proactive approach to community safety and experiment with different ways of providing appropriate services and sanctions (Wright n.d.).

Other less common problem-solving models include veterans courts, homeless courts, reentry courts, trafficking courts, fathering courts, and truancy courts (Ibid). 

The principles and practices of problem-solving justice can also be applied within non-specialised courts that already exist. In a 2000 resolution that was later reaffirmed in 2004, the Conference of Chief Justices and Conference of State Court Administrators advocated for, “Encourag[ing], where appropriate, the broad integration over the next decade of the principles and methods of problem-solving courts into the administration of justice to improve court processes and outcomes while preserving the rule of law” (Porter, Rempel and Mansky 2010, p. 3). Key features of a problem-solving approach to justice – which will be elaborated in the sections that follow – include: individualised screening and problem assessment; individualised treatment and service mandate; direct engagement of the participant; a focus on outcomes; and system change (Ibid. p. iv).

Problems and impacts

How and to what extent have problem-solving courts measured and mapped the following as a first step towards people-centred justice.

• The most prevalent justice problems within the population served
• The justice problems with greatest impact on the population served
• The justice problems that are most difficult to resolve and therefore tend to remain ongoing
• The groups most vulnerable to (systemic and daily) injustices within the population served

As their name suggests, problem-solving courts emerged to address the most prevalent, impactful, and difficult to resolve justice problems within the populations they serve. The first drug (and Drinking While Driving or DWI) courts were created as a response to the increase in individuals with substance use disorders in the criminal justice system and their levels of recidivism. Similarly, mental health courts “seek to address the growing number of [individuals with mental health needs] that have entered the criminal justice system” (Wright n.d.). As one interviewee put it, “The biggest mental health provider [in Los Angeles] is the county jail” (B. Taylor, personal communication, October 5, 2020).

Drug and mental health problems are among the most common issues faced by individuals responsible for both minor and more serious crime. These issues are difficult to resolve because judges – who have historically had little understanding of treatment and addiction – are inclined to hand down harsh sentences when defendants relapse or fail to complete their court mandate (B. Taylor, personal communication, October 5, 2020). This trend was particularly acute in the 1980s, when the war on drugs resulted in draconian sentencing laws that reduced judicial discretion (P. Hora, personal communication, October 16, 2020).

In order to understand and meet the needs of their unique populations, problem-solving courts track measures of problem prevalence and severity. As noted in the first section, early and individualised screening and problem assessment is a key feature of problem-solving justice. The purpose of such screenings is to “understand the full nature of the [participant’s] situation and the underlying issues that led to justice involvement.” 

For drug courts, relevant measures of problem severity may include: drug of choice; years of drug use; age of first use; criminal history; and treatment history (Porter, Rempel and Mansky 2010, p. 50). Mental health courts typically assess the nature and severity of their participants’ underlying mental health issues, and may also look at participant stability (in terms of health care, housing, compliance with prescribed medications, and hospitalisations) (Ibid. p. 51). 

Domestic violence courts and community courts are somewhat unique in that the primary population they serve include victims and members of the community as well as individuals with justice system involvement. Domestic violence courts focus on assessing the needs of victims of domestic violence in order to connect them with safety planning and other individualised services. Likewise, in addition to identifying the problems that impact individual participants, community courts focus on assessing the problems that impact the underserved (and also often disserved) neighbourhoods where they work. These should be identified through outreach in the relevant community but often include concentrations of lower level crimes – such as vandalism, shoplifting, and prostitution – as well as distrust of traditional justice actors (Ibid. p. 55-56).

Now that technical assistance is broadly available for problem-solving courts across the US, individualised screening and problem assessment has become increasingly data-driven and informed by validated needs assessment tools (B. Taylor, personal communication, October 16, 2020). 

Over the years, problem-solving courts have also become more adept at identifying groups within the populations they serve that are particularly vulnerable to injustice. The advancement of brain science, for example, has influenced many problem-solving courts to treat participants under 25 differently and give them an opportunity to age out of crime. Young people transitioning out of foster care are particularly vulnerable to justice involvement given their sudden lack of family support. Trafficked individuals, who used to be treated as criminals, are now widely recognised as victims (Ibid). Specialised problem-solving courts, diversion programs, and training initiatives have emerged to understand the unique needs and vulnerabilities of this population (Wright n.d.).

Problem-solving courts have also become more aware of racial inequities in the populations selected to receive treatment (B. Taylor, personal communication, October 16, 2020). Drug court participants in particular are often disproportionately white, with racial breakdowns that do not mirror the racial breakdowns of those arrested. This is largely a result of eligibility requirements tied to federal drug court funding, which has historically restricted individuals with violent criminal histories from participating. Drug courts have also been accused of cherry-picking participants who were most likely to be successful to improve their numbers and receive more funding. Both of these phenomena have had the effect of excluding disproportionate numbers of people of colour from drug treatment (Ibid). In addition to taking steps to mitigate these inequities, drug courts have increasingly come to recognise that cherry-picking low-risk cases reduces their effectiveness overall (P. Hora, personal communication, October 16, 2020).

Defining + Monitoring Outcomes

How and to what extent have problem-solving courts researched and identified the outcomes that people in the target population expect from justice processes.

In 1993, the first community court was set up in the Midtown neighbourhood of New York City (Lee et al. 2013, p.1). Inspired by the Midtown model, the Red Hook Community Justice Center was established in a particularly disadvantaged area of Brooklyn seven years later. Like the Midtown Court, the goal of the Red Hook Community Justice Center was “to replace short-term jail sentences with community restitution assignments and mandated participation in social services” (Taylor 2016). 

In the planning stages however, residents of Red Hook were not happy to learn that a new court was being introduced in their community. Though sustained community outreach, Red Hook court staff were able to change these negative perceptions and convince residents they wanted to do something different. They began by asking the community what outcomes were most important to them (B. Taylor, personal communication, October 5, 2020).  

This early engagement helped the Red Hook planners realise that tracking outcomes related to people’s presence in the court would not be enough to assess the court’s impact in the community. They would also need to look at outcomes that were meaningful to residents, asking questions like: How can we disrupt crime hot spots? How safe does the community feel? Do residents feel safe walking to the park, or the train? At what times? (Ibid).

Although the Red Hook community court model has since been replicated in different parts of the world, the experiences of two of these international courts illustrate that identifying the outcomes that community members expect from justice processes can sometimes be a challenge.

In 2005, England opened its first community court: the North Liverpool Community Justice Centre (NLCJC). A 2011 evaluation of the NLCJC acknowledged its innovative approach and “potentially transformative effect on criminal justice” but also noted:

How and why the Centre needs to connect with the public it is charged with serving remains one of the most complex and enduring concerns for staff...how consistently and how effectively the ‘community’ was contributing to the workings of the Centre provided a constant source of uncertainty” (Mair and Millings 2011).

After eight years of operation, the NLCJC was closed in 2013. Observers have since noted that a lack of grassroots community engagement in the planning and operation of the NLCJC was among the primary reasons that it ultimately failed to take hold (Murray and Blagg 2018; J. Lang, personal communication, October 28, 2020). 

One year after the NLCJC opened in England, the Neighbourhood Justice Centre (NJC) was piloted in the Collingwood neighbourhood of Melbourne, Australia. At the time, Collingwood had the highest crime rate in Melbourne, high rates of inequality, and a high concentration of services. This combination made it an ideal location for Australia’s first community court. 

Modelled on the Red Hook Community Justice Centre in Brooklyn and spearheaded by the State Attorney General at the time, Rob Hulls, the NJC pilot was focused on improving the community’s relationship with the justice system through local, therapeutic and procedural justice. Like Red Hook, it was designed based on evidence and an analysis of gaps in existing justice services. Despite shifting political winds –  including “tough-on-crime” rhetoric on the one hand and complaints of more favourable “postcode justice” available only for the NJC’s participants on the other – the NJC managed to secure ongoing state government support (J. Jordens, personal communication, October 19, 2020). 

Unlike the NLCJC, the NJC remains in operation today. The procedurally just design of the NJC building and approach of its magistrate, David Fanning, have earned the court significant credibility and legitimacy in the Collingwood community (Halsey and Vel-Palumbo 2018; J. Jordens, personal communication, October 19, 2020). Community and client engagement have continued to be a key feature of the NJC’s work, helping to reduce recidivism and increase compliance with community-based court orders (Halsey and Vel-Palumbo 2018) .

In spite of its success, some observers note that the NJC’s outreach efforts have not gone as far as they could have. Early consultations with a group of community stakeholders regarding the design and governance of the NJC were discontinued in the Centre’s later years. Although the reason for this is unclear and may well have been legitimate, the result was that key representatives of the community lost direct and regular access to NJC leadership over time (J. Jordens, personal communication, October 19, 2020). 

These examples illustrate that even under the umbrella of a one-stop-shop community court, identifying expected justice outcomes in the community as a first step towards problem-solving justice – and continuing to do so even after the court is well-established – is not a given. The extent to which this is achieved depends on the approach of the particular court and its efforts to create a reciprocal and collaborative relationship with the surrounding community.

How and to what extent have problem-solving courts determined whether existing justice processes deliver these outcomes and allow people in the target population to move on?

Problem-solving courts generally – and community courts and drug courts in particular – are created with the explicit intention to address gaps in existing justice processes. 

Community courts are typically established in communities that have been historically underserved and disproportionately incarcerated to provide a more holistic response to crime and increase trust in the justice system. 

In the early days of the Red Hook Community Justice Center, the community’s deep distrust of law enforcement emerged as a key challenge for the Center’s work. Red Hook staff approached this challenge by inviting police officers into the court and showing them the data they had collected on the justice outcomes that residents were experiencing. They helped the officers understand that by not addressing the root causes of crime in the Red Hook community, they were delaying crime rather than stopping it (B. Taylor, personal communication, October 5, 2020).

Over time, the court’s relationship with law enforcement has improved. In 2016, the Justice Center launched its “Bridging the Gap” initiative, which creates a safe space for young people and police officers to get to know each other and discuss difficult topics that offer the chance to explore the other’s perspective (Red Hook Justice News 2016; Sara Matusek 2017).

Similarly, the proliferation of drug courts across the country was a response to high rates of recidivism among individuals with substance use disorders, which persisted in spite of tough-on-crime sentencing practices. During the so-called “war on drugs” in the mid-1980s, judges across the country gradually began to realise that handing down increasingly long sentences to people with substance use disorders was not working. 

One such person was the late Honourable Peggy Hora, a California Superior Court judge responsible for criminal arraignments. Like other judges repeatedly confronted with defendants grappling with substance use disorders in the 1980s and 90s, Judge Hora initially felt that incarceration was the only tool available to her. Not much research had been done on incarceration at the time, so its detrimental effects were not yet widely known (P. Hora, personal communication, October 16, 2020). 

Determined to understand why the defendants that came before her seemed to be willing to risk everything to access drugs – even their freedom and the right to see their children – Judge Hora took a class on chemical dependency. This experience brought her to the realisation that “everything they were doing was wrong.” She quickly built relationships with people at the National Institute on Drug Abuse and began engaging with drug treatment research at a national level (Ibid). 

Judge Hora eventually went on to establish and preside over the nation’s second drug court in Alameda County, California. After learning more about procedural justice and seeing evidence that early drug courts worked and saved money in the long run, she helped promote the model across the country and around the world (Ibid).

How and to what extent have problem-solving courts created a system for monitoring whether new, people-centered justice processes deliver these outcomes and allow people in the target population to move on?

Outcomes monitoring is an essential component of problem-solving justice. As Rachel Porter, Michael Rempel, and Adam Manksy of the Center for Court Innovation set out in their 2010 report on universal performance indicators for problem-solving courts:

It is perhaps their focus on the outcomes generated after a case has been disposed that most distinguishes problem-solving courts from conventional courts. Like all courts, problem-solving courts seek to uphold the due process rights of litigants and to operate efficiently, but their outcome orientation demands that they seek to address the underlying issues that precipitate justice involvement (Porter, Rempel and Mansky 2010, p. 1.).

Measuring and monitoring people-centred outcomes was also key to problem-solving courts’ early success. Because the problem-solving approach was so different from the status quo, showing evidence that it worked was necessary for building political and financial support. This meant clearly articulating the goals of problem-solving courts and finding ways to measure progress towards them (B. Taylor, personal communication, October 14, 2020).

In their report, What Makes a Court Problem-Solving? Porter, Rempel, and Mansky identify universal indicators for each of the three organising principles of problem-solving courts. They include: (under problem-solving) individualised justice and substantive education for court staff; (under collaboration) links with community-based agencies and court presence in community; and (under accountability) compliance reviews, early coordination of information, and court data systems (Porter, Rempel and Mansky 2010, p. 57).  Many of these problem-solving principles and practices can be (and are) applied and monitored in traditional courts. 

To ensure delivery of individualised justice for example, any court staff can engage the individuals appearing before it by making eye contact, addressing them clearly and directly, and asking if they have any questions about the charges or their mandate (Ibid). This kind of engagement can “radically change the experience of litigants, victims, and families” and “improve the chance of compliance and litigant perceptions of court fairness” (Ibid). Similarly, any court can prioritise and track its use of alternative sanctions – such as community service or drug treatment – and its efforts to link individuals to existing services in the community (Ibid).

The extent to which a particular (problem-solving or traditional) court monitors progress towards these people-centred outcomes depends on its ability to track compliance and behaviour change among participants. This can be achieved through regular compliance reviews, which provide “an ongoing opportunity for the court to communicate with [participants] and respond to their concerns and circumstances” (Ibid. p. 60-61). Investing in electronic data systems that track and coordinate information also makes it easier for a court to monitor its overall impact on case outcomes and improve the quality of its mandates (Ibid).

Successful outcomes monitoring also depends crucially on a court’s ability to develop strong relationships with researchers. Without this, early problem-solving courts like the Red Hook Community Justice Center would not have been able to, for example, quantify the impact of a 7-day jail stay in terms of budget, jail population, and bookings per month. Strong research partnerships also made it possible to compare successful and unsuccessful court participants, which was necessary to assess and improve the quality of the court’s services (B. Taylor, personal communication, October 14, 2020).

Outcomes monitoring at the Red Hook Community Justice Center was not without its challenges, however. Because most people who come before the court are charged with less serious crimes, their treatment mandates are relatively short. The short amount of time the Red Hook staff and service providers have to work with these participants means that outcomes related to individual progress are not likely to show a full picture of the court’s impact. The Red Hook Community Justice Center addressed this by also measuring outcomes related to the court’s impact on the community. What was the effect on social cohesion and stability when someone’s brother, father, or son was allowed to remain in the community instead of being incarcerated? (B. Taylor, personal communication, October 5, 2020).

Another challenge faced by community courts broadly is that traditional outcomes monitoring systems are not well-equipped to acknowledge the reality that everything is connected. Where does one draw the line between service providers and justice providers? If a restorative justice process facilitated under the supervision of the court fails to reconcile the parties in conflict but has a positive impact on the lives of the support people who participate, should it be considered a success or failure? 

A former Red Hook staff member involved in the court’s peacemaking initiative shared a story of a young, devout woman with a new boyfriend who mistreated her and who her children strongly disliked. When she tried to throw him out, the boyfriend would use her Christian values against her and convince her to let him stay. Eventually, he punched someone and was arrested on assault charges. His case was referred to a restorative justice circle for resolution. In the circle, the boyfriend was very aggressive and as a result, his case was sent back to court. The woman and her children asked if they could continue meeting in circle without him because they found it helpful (Ibid).

After a series of circle sessions together, the woman came to realise that her abusive boyfriend was using drugs and found the courage to kick him out. In his absence, the woman and her children were able to reconcile and reunite. The woman returned to school and her oldest son found a job. The criminal case that started the process was ultimately unresolved, but from a more holistic and common sense perspective the impact of the circles on the family was positive (Ibid). How should success be measured in this case? This is a challenge that community courts attempting to measure and monitor people-centred justice regularly face.

Evidence-Based Solutions

How and to what extent have problem-solving courts introduced interventions that are evidence-based and consistently deliver the justice outcomes that people in the target population look for.

Problem-solving courts have introduced a number of interventions that have proven to deliver people-centred outcomes for the communities they serve. Although different interventions work for different populations, direct engagement with participants and the delivery of individualised treatments are two key elements of the problem-solving orientation that all problem-solving courts share (Porter, Rempel and Mansky 2010, p. 29-30). 

As described in the previous section, direct engagement means that the judge speaks to participants directly and becomes actively engaged in producing positive change in their lives (Ibid. p. 30-31). This effort to ensure that participants feel heard, respected and experience the process as fair is supported by research on procedural justice. 

Individualised treatment means that the interventions delivered are tailored to the specific problems of each participant. This requires that the court offer “a continuum of treatment modalities and services to respond to the variety and degrees of need that participants present.” This service plan must be revisited by the court on a regular basis and adjusted depending on the participant’s reported progress (Ibid. p. 29-30).

Despite this shared approach to justice delivery, different problem-solving courts have identified different types of treatments and ways of monitoring whether they work that are unique to the populations they serve.

Community courts like the Red Hook Community Justice Center, for example, generally work with the residents in their neighbourhood to find out what is important to them rather than imposing a predetermined set of solutions. 

The Neighbourhood Justice Centre in Melbourne did this through a unique problem-solving process that took place outside of the courtroom and which participants could opt into voluntarily. In a confidential, facilitated discussion based on restorative and therapeutic justice principles, participants were given an opportunity to share their perspective on the problems they were facing and empowered to become collaborators in their own rehabilitation. Important takeaways from this process would be reported back to the court’s magistrate so he could help them move forward – for example by changing their methadone (1) dose or changing the number of treatments they received per week. The collaborative nature of the sessions helped ensure that the treatment plans mandated by the court were realistic for participants. Though the content of these sessions was unpredictable and varied, the co-design process remained constant (J. Jordens, personal communication, October 19, 2020; Halsey and Vel-Palumbo 2018).

With that said, certain interventions have proven to consistently improve outcomes for communities, victims, and individuals with justice system involvement when applied to low-level cases. These include: using (validated) screening and assessment tools (2); monitoring and enforcing court orders (3); using rewards and sanctions; promoting information technology (4); enhancing procedural justice (5); expanding sentencing options (to include community service and shorter interventions that incorporate individualised treatment); and engaging the community (6).

In 2009, the National Institute of Justice funded a comprehensive independent evaluation of the Red Hook Community Justice Center to assess whether it was achieving its goals to reduce crime and improve quality of life in the Red Hook neighbourhood through these interventions (Lee et al. 2013, p. 2.). The evaluation found that:

The Justice Center [had] been implemented largely in accordance with its program theory and project plan. The Justice Center secured the resources and staff needed to support its reliance on alternative sanctions, including an in-house clinic and arrangements for drug and other treatment services to be provided by local treatment providers...The Justice Center’s multi-jurisdictional nature, as well as many of its youth and community programs, evolved in direct response to concerns articulated in focus groups during the planning process, reflecting a stated intention to learn of and implement community priorities (Ibid. p. 4).

Using a variety of qualitative and quantitative research methods, the evaluation also concluded that Red Hook had successfully: changed sentencing practices in a way that minimised incarceration and motivated compliance; provided flexible and individualised drug treatment; sustainably reduced rates of misdemeanour recidivism among young people and adults; and reduced arrests in the community. 

In spite of the robust evidence supporting their approach, many community courts experience resistance to their efforts to help participants address underlying issues of substance use and mental disorders through treatment. As Brett Taylor, a Senior Advisor for Problem-Solving Justice and former defence attorney at the Red Hook explains:

Some critics of community courts say that [this] is not the job of courts and should be handled by other entities. In a perfect world, I would agree. However, in the reality of the world today, people with social service needs continue to end up in the courts. Court systems across the country have realised that if defendants with social service needs are not given treatment options, those defendants will be stuck in the revolving door of justice and continue to clog the court system....Although it may not comport with the vision of success that many defence attorneys had upon entering this work, I can tell you that nothing beats seeing a sober, healthy person approach you on the street and hearing, ‘Thank you for helping me get my life back on track’ (Taylor 2016, p. 25).

In contrast to the broad and community-based approach to treatment taken by community courts, drug courts focus specifically on providing drug treatment. In the words of Judge Peggy Hora, drug treatment is “painful and difficult.” Because of this, drug courts start with external changes as their goal, but ultimately aim for internal change. This means appropriately matching participants with evidence-based treatment and using neutral language that assists, supports, and encourages participants along the way. Because relapse is such a common feature of recovery, drug courts focus on keeping people in appropriate treatment as long as necessary for them to eventually graduate from the program (P. Hora, personal communication, October 16, 2020).

Drug court treatments have become increasingly evidence-based since the 1990s due to a growing movement toward performance measurement in the non-profit sector:

The emergence of drug courts as a reform of courts’ traditional practice of treating drug-addicted offenders in a strictly criminal fashion coincided with renewed interest in performance measurement for public organisations. The argument for measuring the performance of drug courts is compelling because they are a recent reform that must compete with existing priorities of the judicial system for a limited amount of resources. This makes it incumbent upon drug courts to demonstrate that the limited resources provided to them are used efficiently and that this expenditure of resources produces the desired outcomes in participants (Rubio et al. 2008, p. 1).

This movement was further strengthened by the development of a cutting edge performance measurement methodology known as the “balanced scorecard.” Created for the business sector, the balanced scorecard method aims to go beyond traditional measures of success and get a more balanced picture of performance by incorporating multiple perspectives. This method was adapted to create CourTools, a set of ten performance measures designed to evaluate a small set of key functions of trial courts (Ibid. p. 2). 

Because “the nature of addiction and the realities of substance use treatment require extended times to disposition for drug court participants,” many of the performance measures developed for conventional trial courts (such as reduced time to disposition) are not directly applicable to drug courts. However, the increased application of performance measurement to courts and the creation of CourTools in particular helped make way for the development of the first set of nationally recommended performance measures for Adult Drug Courts in 2004 (Ibid. p. 4).

Developed by a leading group of scholars and researchers brought together by the National Drug Court Institute (NDCI) and published for the first time in 2006, these included four key measures of drug court performance: retention; sobriety, in-program recidivism; and units of service (Ibid. p. 5).

Retention refers to the amount of time drug court participants remain in treatment. “Longer retention not only indicates success in treatment but also predicts future success in the form of lower post treatment drug use and re-offending”  (Ibid. p. 5). Sobriety – both during and after treatment –  is another important goal of drug courts. “As the participant proceeds through the program, a trend of decreasing frequency of failed [drug] tests should occur. Research has shown that increasing amounts of time between relapses is associated with continued reductions in [drug] use” (Rubio et al. 2008, p. 5). In-program recidivism is the rate at which drug court participants are re-arrested during the course of their participation. This is expected to be lowered through a combination of “judicial supervision, treatment, and rewards and sanctions” unique to drug courts (Ibid. p.5; US Government and Accountability Office, 2005). Finally, units of service refers to the dosages in which drug court treatment services – including, but not limited to substance use treatment – are delivered. These are usually measured in terms of days or sessions of service provided (Rubio et al. 2008, p. 5).

Since their development, these four measures of drug court performance have been actively promoted by leading technical assistance providers like the Center for Court Innovation (CCI) and the National Center for State Courts (NCSC) (Ibid. p. 6). They have since been adopted and adapted by a number of states across the US. The NCSC facilitates this process, but decisions about what specifically to measure are made by the advisory committee convened by the state-level agency responsible for drug courts (Ibid). Additional performance measures used by some states relate to, for example: accountability, social functioning, processing, interaction with other agencies, compliance with quality standards, and  juvenile drug court measures, family drug court measures, and domestic violence drug court measures (Ibid. p. 10).

In 2007, the NCSC surveyed statewide drug court coordinators from across the country about their use of state-level performance measurement systems (SPMS). Out of 45 states that completed the surveys, 58% were using a SPMS in their drug courts. Most of these were adult drug courts (Ibid. p. 14). Although the frequency with which these states reported performance measurement data varied from quarterly to annually, the majority did provide data to a central agency (Ibid. p. 15). 

The development and widespread use of SPMS have helped drug courts deliver treatments that are increasingly evidence-based in the sense of consistently delivering the outcomes that their participants need. However, the NCSC survey found that the state-level performance measures used were not entirely balanced in that they typically focused more on the effectiveness of drug courts than their efficiency, productivity, or procedural satisfaction (Ibid. p. 20). The NCSC therefore recommended that a more balanced, national and uniform set of drug court performance measures be developed to measure performance more holistically and facilitate comparisons of performance across states (Ibid. p. 18).

How and to what extent have problem-solving courts used outcome-based monitoring (discussed in the previous section) to continuously improve these interventions and replace interventions that have proven ineffective?

Because of their problem-solving orientation and focus on outcomes, problem-solving courts are by their nature adaptive and capable of developing new treatment modalities to meet different kinds of needs. As Brett Taylor, Senior Advisor for Problem-Solving at the Center for Court Innovation put it, “the problem-solving court environment creates a space in which there is more room for creativity. If you were to redesign the justice system now, there wouldn’t be only courts you could go to, there would be different justice mechanisms and modalities available to treat different levels of issues. Perhaps that is why new modalities develop within problem-solving courts” (B. Taylor, personal communication, October 19, 2020).

A clear example of this creative and outcomes-based approach to improvement was the way the problem-solving dialogue process developed at the Neighbourhood Justice Center (NJC) was adapted over time to meet changing demands in the community. As Jay Jordens, a Neighbourhood Justice Office at the NJC who introduced the process explains: “different problems would arise that would demand a re-design of the court’s approach” (J. Jordens, personal communication, October 19, 2020).

For example, the NJC began to notice that people responsible for family violence were participating in problem-solving dialogues without sharing this part of their history. In response, the NJC developed a tailored problem-solving process for people who were respondents to a family violence order in which this part of their past would be addressed from the start. The NJC also began facilitating support meetings for victims of family violence, including for example parents who were being mistreated by their children. The process was designed to solicit feedback about the new approach after victims had tried it. Eventually, it earned the support of the police in the community because it consistently delivered outcomes for a unique population (Ibid).

A second adaptation of the problem-solving process at the NJC was made when court staff noticed that many young people were opting out. Many of the court-involved young people in the Collingwood community were refugees from South Sudan who were experiencing the effects of intergenerational trauma. Realising that the process as it was originally imagined was too interrogative for this population, the NJC began holding circles with the young person, their mother, and one or two support workers. A facilitator would begin by asking humanising questions of everyone in the circle. Although the young person would often pass when it was their turn to speak, participating in the circle gave them an opportunity to listen, relax, and improve their relationships with the adults sitting in the circle with them. These problem-solving circles were designed to prioritise safety concerns and would often result in an agreement among the participants to get external support and/or attend family therapy.

Jay Jordens notes that such adaptations were possible in spite of, not because of, an operational framework of specialisation within the court that made collaboration a choice rather than an expectation among Centre staff. “We aren’t there yet where these processes are intuitive,” he explained, “we still need to actively facilitate them” (Ibid).

Because of their systematic approach to outcomes monitoring and performance measurement, drug courts have made a number of improvements to the treatment they provide as well. First and foremost, they have learned to avoid net widening: “the process of administrative or practical changes that result in a greater number of individuals being controlled by the criminal justice system” (Leone n.d.).

Specifically, drug courts have learned that putting the wrong people in the wrong places results in bad outcomes. An example of this is cherry picking the easiest cases for drug treatment: a common practice among drug courts in the early years of their development that later proved to be harmful. Evidence has shown that drug courts are most effective when they focus on treating high-risk, high-needs participants who are most likely to reoffend (P. Hora, personal communication, October 16, 2020). Cherry picking low-risk cases in order to inflate measures of success means putting them in more intensive treatment than they need and failing to appropriately match treatments with risk. Over time, this entraps people in the criminal justice system unnecessarily and reduces drug courts’ potential to meaningfully reduce crime (B. Taylor, personal communication, October 19, 2020).

Cherry picking low-risk cases for drug treatment has also resulted in racially biased outcomes. Because of the ways racial bias is embedded in the American criminal justice system, young white defendants have historically been more likely to be assessed as low-risk and eligible for specialised treatment than participants of colour. Participants of colour who were selected for drug court programming also tended to flunk out or leave voluntarily at higher rates than white participants.

In response to these trends, drug courts developed a toolkit on equity and inclusivity to examine the data and understand why this was happening. They introduced HEAT (Habilitation Empowerment Accountability Therapy), a new drug treatment modality geared towards young black men which was recently evaluated with very positive results. They have also worked harder generally to ensure that treatments are culturally appropriate for the different populations they serve.

Drug courts have also become more sophisticated at treating different kinds of drug addiction. The Matrix Model, for example, was developed to engage a particularly difficult population – stimulant (methamphetamine and cocaine) users – in treatment. Previously considered “untreatable” by many drug courts, stimulant users treated using the Matrix Model have shown statistically significant reductions in drug and alcohol use, risky sexual behaviors associated with HIV transmission, and improved psychological well-being in a number of studies (P. Hora, personal communication, October 16, 2020; National Institute of Drug Abuse 2020).

Drug court judges who once took a “blaming and shaming” approach have shifted towards a more people-centred one, as evidenced by changes in the language used to describe participants. In response to research in the medical sector demonstrating that people who are described as addicts receive lower quality care and fewer prescriptions, drug courts have increasingly replaced the term “addiction” with “substance use disorder” (P. Hora, personal communication, October 16, 2020).

In line with this shift, attitudes towards medically assisted drug treatment have also changed dramatically over the years. Whereas most drug courts previously did not allow the use of methadone in treatment, the field has now clearly adopted medically assisted treatment after finding that it was consistent with improved graduation rates, among other outcomes. Though not universally accepted, it is now considered a best practice supported by decades of research (Ibid).

On a more systematic level, a 2007 analysis of performance measurement data collected by the state of Wyoming provides an example of how drug courts have started to use this data to improve the quality of their treatments and overall impact. Based on results related to the key measures of drug court performance introduced in the previous section – retention, sobriety, in-program recidivism and units of service – the NCSC made a number of programmatic recommendations for drug courts across the state. First, they suggested that drug courts aim to support participants’ education and employment-related needs, as both attainment of a diploma and employment at admission to treatment were associated with increased graduation rates. They also recommended that additional resources be made available for young participants of colour, who were found to have higher rates of positive drug tests and recidivism than young white participants (Rubio et al. 2008, p. 17).

Innovations + Delivery Models

How and to what extent have problem-solving courts scaled their people-centered service delivery model to deliver justice outcomes for a larger population.

Many problem-solving courts across the US continue to start in the way the first problem-solving courts did: with judges deciding to do things differently. With that said, the proliferation of problem-solving courts across the country can be traced to three primary factors: science and research; technical assistance; and changes in legal education.

Research has helped bring problem-solving courts to scale by showing that the problem-solving approach to justice, if properly implemented, can be effective. Research on procedural justice and advancements in understanding of the science of addiction have been particularly important in this respect. Increased awareness of major studies in these areas have helped the field shift towards evidence-based working and helped legal professionals learn from past mistakes. More and more judges realise that relapse is part of recovery, and that mandated treatment within a drug court structure delivers positive outcomes for participants (B. Taylor, personal communication, October 19, 2020).

Once a number of problem-solving courts had been established around the country, technical assistance providers emerged to help them take a data-driven approach. This means working with communities to look at the numbers and identify the biggest crime problems they are struggling with and introducing a problem-solving court that is responsive to those issues. It also means using screening and needs assessment tools to make informed sentencing decisions and match participants to appropriate treatments. Technical assistance has helped problem-solving courts increase their impact and effectiveness and over time deliver outcomes for larger populations (Ibid).

As problem-solving courts like the Red Hook Community Justice Center have become better known, law students and young legal professionals have become more aware of and enthusiastic about problem-solving justice as an alternative to adversarial ways of working (Ibid). This represents a significant shift from the early days of problem-solving courts, when judges and lawyers alike were reluctant to embrace non-conventional conceptions of their roles as legal professionals. Prosecutors called problem-solving courts “hug-a-thug” programs. Defence attorneys resisted the idea of a court being a cure-all for their clients. Judges insisted that they “weren’t social workers” and shouldn’t be doing this kind of work (P. Hora, personal communication, October 16, 2020). Service providers were concerned too: they feared that involving the justice system in treatment would ruin their client relationships.

Over time, judges have come to see that their roles could expand without violating something sacrosanct about being a judge. In 2000, the Conference of Chief Justices and Conference of State Court Administrators adopted a resolution supporting the use of therapeutic justice principles. Since then, experience presiding over a drug court has come to be seen as a positive in judicial elections (Ibid).

Despite early concerns that problem-solving courts were “soft on crime,” prosecutors and defense attorneys have largely come on board as well. Research has demonstrated that when problem-solving courts acknowledge their gaps in knowledge and defer to service providers for clinical expertise, they can be successful in supporting treatment. As a result of advances in research, the emergence of problem-solving technical assistance, and important cultural shifts, drug and mental health courts are now widely recognised as appropriate and welcome additions to the field (Ibid). This acceptance has facilitated their spread nationally and as far as Australia and New Zealand.

Court numbers are not the only relevant measure for evaluating the extent to which problem-solving courts have successfully scaled, however. In addition to horizontal scaling of courts across the country, vertical integration of problem-solving principles and practices within particular jurisdictions is an important indicator of problem-solving courts’ spread and influence (J. Lang, personal communication, October 28, 2020).

As explained in the introduction, the principles and practices of problem-solving justice can be and are increasingly applied by traditional justice actors and in existing, non-specialised courts. Police departments across the country are learning that they can divert defendants to treatment from the get-go, without necessarily waiting for a case to be processed through the courts (Ibid). A prominent example of police-led diversion is LEAD (Law Enforcement Assisted Diversion) in Seattle, “a collaborative community safety effort that offers law enforcement a credible alternative to booking people into jail for criminal activity that stems from unmet behavioural needs or poverty” (Law Enforcement Assisted Diversion, n.d.). The Seattle LEAD model was externally evaluated and found to deliver a range of positive outcomes for individuals with justice system involvement and the community (LEAD National Support Bureau n.d.-a). The model has been replicated successfully and is now operating in over thirty-nine counties in the US (LEAD National Support Bureau n.d.-b).

Cases that do reach court are also increasingly diverted outside of it. Prosecutors and judges who are not operating within a problem-solving court can nevertheless apply problem-solving principles by linking defendants to services and making use of alternative sentences in lieu of jail time. This “problem-solving orientation” has allowed problem-solving justice to be applied in more instances and settings without necessarily setting up new problem-solving courts. One indication that problem-solving courts have already scaled “horizontally” in the US – and that this “vertical” scaling is the latest trend – is the fact that the US government’s drug courts funding solicitation in 2020 no longer includes a category for the creation of a new drug court (J. Lang, personal communication, October 28, 2020).

Evidence of this trend towards vertical scaling can be found as far away as Australia. As a specific alternative to horizontal replication, the Neighbourhood Justice Centre (NJC) has developed resources to support judges at the Melbourne Magistrates Court to adopt a problem-solving approach to their work. Over time, this court has become a “laboratory of experimentation” for problem-solving principles and practices as well as other complementary technologies (i.e. therapeutic or procedural justice approaches)  that need to be tested before broader roll-out. In a similar vein, New York City’s courts have carried the innovative principles and practices of community courts into centralised courthouses in Brooklyn and the Bronx rather than creating more Red Hooks (Ibid).

How and to what extent have problem-solving courts funded their service delivery model in a sustainable way?

Drug courts have been successful in obtaining large and sustainable streams of federal funding due to the strong research partnerships they developed from the start. Early data collection and evaluation persuaded funders that the problem-solving approach would deliver positive outcomes and save money by reducing incarceration costs. The fact that Florida Attorney General  Janet Reno – who set up the nation’s first drug court in 1989 – worked with Assistant Public Defender Hugh Rodham (7) in Miami Dade County also helped make drug courts a success and capture the attention of the federal government early on.

Importantly, federal funding for drug courts was often conditional upon their participation in rigorous evaluations. This demonstrated the effectiveness of the drug court model in a way that may not have been possible had the drug courts had to fund the research themselves, and justified their continued funding (P. Hora, personal communication, October 16, 2020). In recent years, states and counties have become a significant source of funding for drug courts as well  (J. Lang, personal communication, October 28, 2020).

Although the federal government has also helped fund other types of problem-solving courts, drug courts are by far the most sustainably funded. Only recently has the government made it possible for community courts to apply for direct funding, or indirect funding as subgrantees of the Center for Court Innovation. The long-term funding for many community courts is provided by local municipalities (Ibid). Funding community courts is a unique challenge because in addition to standard line items like project director and case worker salaries, they must find a way to cover less conventional expenses support for community volunteers and circle participants (often in the form of food, which the government is not willing to fund) (B. Taylor, personal communication, October 19, 2020).

Direct federal funding for other kinds of problem-solving courts is very limited. What funding has been made available to them has gone primarily towards research and the establishment of state-level coordinators and problem-solving court infrastructure. This has helped to increase awareness of the problem-solving principles and practices at the state level and encouraged their application in different areas (P. Hora, personal communication, October 16, 2020).

Private foundations have supported various aspects of problem-solving justice initiatives in certain parts of the country, but have not yet committed to doing so in a sustained way (J. Lang, personal communication, October 28, 2020).

To what extent have problem-solving courts leveraged the following sustainable financing strategies: public-private partnerships and smart (user) contributions?

Community courts in New York – including the Red Hook Community Justice Center and the Midtown Community Court – have benefitted from public-private partnerships to the extent that their planning and operations have been led by the Center for Court Innovation, a public-private partnership between the New York court system and an NGO. Over the years, these courts have also partnered with local “business improvement districts” to supervise community service mandates and offer employment opportunities to program graduates (Ibid).

Some treatment courts do also charge a nominal participant fee, which can range from $5-$20 per week (Wallace 2019). These user contributions can be used for grant matching, among other things. Charging people for their participation in problem-solving programming is generally not regarded as good practice, however (J. Lang, personal communication, October 28, 2020).

More broadly, problem-solving courts and community courts in particular can be said to be financially sustainable in that they often save taxpayer money (Wallace 2019). Although it takes time to realise the benefits of the upfront costs of creating and running a drug court for example, research has demonstrated that once established, the associated cost savings range from more than $4,000-$12,000 per participant (Office of National Drug Court Policy 2011). The Red Hook Community Justice Center alone was estimated to have saved local taxpayers $15 million per year (primarily) in victimisation costs that were avoided as a result of reduced recidivism (Halsey and de Vel-Palumbo 2018). The cost savings associated with problem-solving courts have helped them to continue to be competitive applicants for federal, state and local, and sometimes private grant funding over the years and in spite of changing political winds (Wallace 2019).

• Enabling environment

How and to what extent have regulatory and financial systems created/enabled by the government supported problem-solving courts and made it possible for this service/activity to scale?

Most if not all states in the US have allowed drug courts to become part of state legislation, which makes possible their continued operation (P. Hora, personal communication, October 16, 2020).

How and to what extent have the outcomes-based, people-centered services delivered by problem-solving courts been allowed to become the default procedure?

Problem-solving courts have not been allowed to become the default procedure in that adversarial courts and procedures remain the standard way of responding to crime in the US. In the words of Judge Hora, “There is no question that the number of people served is growing, but this remains only a drop in the bucket. For every person served there are 6-7 who aren’t” (Ibid). However, the expanding presence of problem-solving courts has helped the justice sector shift away from the excessively punitive state sentencing laws and tough-on-crime rhetoric of the late 1980s towards a more restorative and evidence-based way of working (B. Taylor, personal communication, October 5, 2020).

Problem-solving courts have enabled cultural change by demonstrating to lawyers and judges that defendants do better when they are able to access treatment, while at the same time allowing these traditional legal players to act as intermediaries and retain a gatekeeping role. As discussed in previous sections, police, prosecutors, and judges alike have grown increasingly comfortable with diverting cases from the adversarial track to community-based treatment (Ibid).

It is a paradox that the US has developed and spread the problem-solving courts model as the country with the highest incarceration rates in the world. Former Senior Advisor of Training and Technical Assistance at the Center for Court Innovation, Julius Lang, speculates that this punitive backdrop is what has allowed alternatives to incarceration to flourish in the US and become so highly developed. At the same time, countries with lower baseline penalties that have set up problem-solving courts, such as Canada and Australia, have developed creative means of engaging defendants who need treatment since there is less of a threat of incarceration (J. Lang, personal communication, October 28, 2020).

How and to what extent have problem-solving courts stimulated (or benefitted from) investment into justice research and development?

Problem-solving courts have both stimulated and benefited from investment into justice research and development. As discussed in the previous sections, the success of problem-solving courts in the US can be attributed in large part to their strong research partnerships. 

From the start, “problem-solving courts always took responsibility for their own research and their own outcomes” (Ibid). Problem-solving justice initiatives run by the Center for Court Innovation, for example, always worked directly with researchers. This produced a huge amount of evaluation literature, which was important for securing the buy-in and funding necessary to continue operating (B. Taylor, personal communication, October 14, 2020). 

The fact that federal funding has incentivised high-quality evaluations has also gone a long way to build a foundation of evidence demonstrating drug courts’ effectiveness (P. Hora, personal communication, October 16, 2020).

Leadership + Pathways

How and to what extent have justice sector leaders’ skills and collaborations enabled/hindered problem-solving courts to increase access to justice by delivering the outcomes people need at scale.

Strong leadership has been essential to problem-solving courts’ ability to deliver the treatment outcomes people need at scale. Without the leadership of visionary judges and other leaders aiming to do things differently, they would never have come into existence in the first place. 

Because of the tendency to maintain the status quo, individual problem-solving courts also rarely get off the ground without a strong champion. The reason for this can be traced to problem-solving principles and practices themselves: the goal is not to force people to change, but to make them change because they want to. In the same way, effective leaders can persuade system actors that problem-solving justice is the way to achieve common goals (B. Taylor, personal communication, October 14, 2020).

Community courts in particular require strong leadership. This can sometimes pose problems for the courts’ long-term stability. For example, a community court in North Liverpool was championed by prominent national politicians. Their leadership was important for the court’s establishment and initial funding, but changes in national leadership and the lack of local support were major factors in the court’s ultimate closure (J. Lang, personal communication, October 28, 2020).

As mentioned above, community courts may struggle when their early champions move on. To avoid this and prepare for the eventual departure of the personalities who are driving change, it is important to put the courts’ internal ways of working into writing. As previously discussed, it is also necessary to obtain evidence that the court’s approach works, as this is a more important driver of funding than good leadership in the long-run (B. Taylor, personal communication, October 5, 2020).

Mid-level leadership within problem-solving courts also matters. Since staff are often employed and supervised by various partner agencies – rather than the director of the project as a whole – it is particularly important that they be selected with care, trained in the project’s mission, policies and practices, and incentivised to work as part of a single team (J. Jordens, personal communication, October 19, 2020).

How and to what extent have problem-solving courts contributed to/benefited from new high-level strategies or pathways towards people-centred justice in the US?

High-level strategies at the state level and in the form of technical assistance have benefitted problem-solving courts significantly by facilitating their replication. This is particularly true of drug courts, for which state-wide coordination mechanisms were set up at an early stage.

Recognising that substance use disorder was a major problem, and persuaded by the same research as federal legislators, state officials began to set up mechanisms that would allow them to receive federal drug court funding. This also allowed them to strategise about which counties would most benefit from drug courts (or other problem-solving courts), and which standards to impose. 

Together, state-wide coordination mechanisms created an infrastructure for the improvement and replication of drug courts nationwide, and made it easier to apply problem-solving practices and principles in new settings. Whereas trainings on brain science and what’s working in treatment used to be reserved for drug court judges, there are now few states that do not include them in judicial training for all new judges. The same can be said for trainings for prosecutors, defence attorneys, and service providers (P. Hora, personal communication, October 16, 2020).

The emergence of technical assistance providers specialising in problem-solving justice such as the Center for Court Innovation, Justice System Partners, the National Center for State Courts, and the Justice Management Institute have also helped problem-solving courts to coordinate and replicate in strategic ways. By developing listservs and organising conferences, these organisations have enabled people in various problem-solving courts to support each other across state and international lines. Over time, these efforts have created shared principles and legitimacy around the movement for problem-solving justice (J. Lang, personal communication, October 28, 2020).

To what extent have problem-solving courts contributed to/played a role in a broader paradigm shift towards people-centered justice?

As mentioned in the introduction, a fifth key feature of the problem-solving orientation is system change. By educating justice system stakeholders about the nature of behavioural problems that often underlie crime and aiming to reach the maximum number of cases within a given jurisdiction, problem-solving courts seek to make broader impact within the justice system and community (Porter, Rempel and Mansky 2010, p. 32-33).

Since the first drug court was set up in 1989, legal professionals have become increasingly aware that many people with social problems end up in the justice system: a system that was never intended to address those problems. Problem-solving courts have contributed to a broader paradigm shift towards people-centred justice to the extent that they have helped these professionals:

• Acknowledge this issue;
• Recognise that lawyers are not equipped to deal with this issue (American law schools do not prepare them to);
• Connect with service providers in the community;
• Leverage the coercive power of the justice system in a positive way;
• Encourage success in treatment programs using procedural justice.

By taking a collaborative approach to decision-making, delivering individualised justice for each participant while at the same time holding them accountable, educating staff, engaging the broader community, and working to produce better outcomes for people, problem-solving courts have demonstrated what people-centred criminal justice can look like in the US and around the world.

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(1) Methadone is a synthetic opioid used to treat opioid dependence. Taking a daily dose of methadone in the form of a liquid or pill helps to reduce the cravings and withdrawal symptoms of opioid dependent individuals.

(2) “A screening tool is a set of questions designed to evaluate an offender’s risks and needs fairly quickly…An assessment tool is a more thorough set of questions administered before an offender is matched to a particular course of treatment or service.” Taylor 2016, p. 7.

(3) “The main monitoring tool community courts use is compliance hearings, in which participants are periodically required to return to court to provide updates on their compliance.” Taylor, 2016, p. 9.

(4) “Community courts have promoted the use of technology to improve decision-making. Technology planners created a special information system for the Midtown Community Court to make it easy for the judge and court staff to track defendants…Information that’s reliable, relevant, and up-to-date is essential for judges to make the wisest decisions they can.” Taylor 2016, p. 12-13.

(5) In community courts, “judges often speak directly to the offender, asking questions, offering advice, issuing reprimands, and doling out encouragement. This reflects an approach known as procedural justice…Its key components, according to Yale Professor Tom Tyler, are voice, respect, trust/neutrality, and understanding.” Taylor 2016, p. 15.

(6) “Community courts emphasize working collaboratively with the community, arguing that the justice system is stronger, fairer, and more effective when the community is invested in what happens inside the courthouse.” Taylor 2016, p. 22.

(7) Hugh Rodham was the brother of Hillary Clinton, who would become the First Lady a few years later.

View References

Amanda Cissner and Michael Rempel. (2005).  The State of Drug Court Research: Moving Beyond ‘Do They Work?’ , Center for Court Innovation.

Brett Taylor. (2016). Lessons from Community Courts: Strategies on Criminal Justice Reform from a Defense Attorney . Center for Court Innovation, p. 3.

Cheryl Wright, (n.d.). Tackling Problem-Solving Issues Across the Country . National Center for State Courts (NCSC).

Cynthia Lee, Fred Cheesman, David Rottman, Rachael Swaner, Suvi Lambson, Michael Rempel and Ric Curtis. (2013). A Community Court Grows in Brooklyn: A Comprehensive Evaluation of the Red Hook Community Justice Center . National Center for State Courts, Center for Court Innovation, p.1.

David Wallace. (2019). Treatment Court: Is Yours Sustainable? (Part Four) . Justice Speakers Institute.

David Wallace. (2019). Treatment Court: Is Yours Sustainable? (Part One) . Justice Speakers Institute.  

Dawn Marie Rubio, Fred Cheesman and William Federspiel. (2008). Performance Measurement of Drug Courts: The State of the Art . National Center for State Courts, Volume 6, p. 1.

George Mair and Matthew Millings. (2011). Doing Justice Locally: The North Liverpool Community Justice Centre . Centre for Crime and Justice Studies.

Halsey and de Vel-Palumbo. (2018). Courts As Empathetic Spaces: Reflections on the Melbourne Neighbourhood Justice Centre . Griffith Law Review, 27(4).

Interview with Brett Taylor, Senior Advisor for Problem-Solving Justice, Center for Court Innovation, October 5, 2020.

Interview with Brett Taylor, Senior Advisor for Problem-Solving Justice, Center for Court Innovation, October 14, 2020.

Interview with Brett Taylor, Senior Advisor for Problem-Solving Justice, Center for Court Innovation, October 16, 2020.

Interview with Brett Taylor, Senior Advisor for Problem-Solving Justice, Center for Court Innovation, October 19, 2020.

Interview with Jay Jordens, Education Program Manager – Therapeutic Justice, Judicial College of Victoria, October 19, 2020.

Interview with Judge Peggy Hora, President, Justice Speakers Institute, October 16, 2020.

Interview with Julius Lang, Senior Advisor, Training and Technical Assistance, Center for Court Innovation, October 28, 2020.

Law Enforcement Assisted Diversion (LEAD) , King County.

LEAD National Support Bureau, (n.d.). Evaluations . 

LEAD National Support Bureau. (n.d.). LEAD: Advancing Criminal Justice Reform in 2020 .

Mark Halsey and Melissa de Vel-Palumbo. (2018). Courts As Empathetic Spaces: Reflections on the Melbourne Neighbourhood Justice Centre . Griffith Law Review 27 (4). 

Matthew Leone, Net widening , Encyclopaedia of Crime and Punishment, SAGE Reference.

National Institute of Drug Abuse (2020). The Matrix Model (Stimulants) , Principles of Drug Addiction Treatment: A Research-Based Guide

Office of National Drug Court Policy. (2011). Drug Courts: A Smart Approach to Criminal Justice .

Rachel Porter, Michael Rempel and Adam Mansky. (2010). What Makes a Court Problem-Solving? Universal Performance Indicators for Problem-Solving Justice . Center for Court Innovation, p. 1

Red Hook Justice News. (2016).  Bridging the Gap: Youth, Community and Police . 

Sarah Matusek. (2017). Justice Center celebrates Bridging the Gap birthday . The Red Hook Star Revue. 

Sarah Murray and Harry Blagg. (2018). Reconceptualising Community Justice Centre Evaluations – Lessons from the North Liverpool Experience . Griffith Law Review 27 (2).

Suzanne Strong and Tracey Kyckelhahn. (2016).  Census of Problem-Solving Courts, 2012 . Bureau of Justice Statistics.

US Government and Accountability Office, 2005.

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Using a Writing Strategy to Help Middle School Students Solve Complex Word Problems

The claim-evidence-reasoning method promotes critical thinking by giving students a framework for solving multistep problems.

Before the start of the school year, my instructional leadership team meets to review several data sets thoroughly. We then discuss practices that all teachers should implement during classroom instruction. One of our required practices this past school year was teaching students how to utilize the claim-evidence-reasoning (CER) framework in all content areas and grade levels. CER is a writing strategy that promotes analytical thinking .

Our math instructional lead teacher, Mrs. Crusoe, saw this as an opportunity to  have all math teachers participate in a professional development (PD) session focused on using CER when teaching students how to solve rigorous, multistep math problems. Ultimately, we wanted to ensure that all math teachers thoroughly understood each CER component because we believe that yields mathematically proficient students.

From my experience, educators tend to use the SOLVE or CUBES  strategy for problem-solving. These methods are good mnemonics; however, the CER model has several benefits beyond assisting a student’s memory.

The CER model is particularly helpful when students are solving multistep math problems that require them to explain each step meticulously. The math PD session that my school facilitated to build teacher capacity focused on teaching students how to apply each part of CER and the eight standards for mathematical practice .

Twelve math teachers worked independently to align each component of CER with the standards and wrote a short report explaining their alignment. Then the math leaders met to develop an agreed-upon alignment to implement when teaching students how to use CER. 

When creating the framework , we were intentional about using math vocabulary that the students were expected to know, as well as asking the students questions that would guide their critical thinking and assist them in leveraging the standards.

Collectively, the math department agreed that it was vital for the framework to counter students’ desire to opt out of doing word problems, write the infamous acronym IDK (“I don’t know”) when answering word problems, or rush through them and come up with an illogical answer.

For the claim component, we wanted students to unlock the prompt (word problem) by applying the three-reads literacy strategy to read for context, read to understand the quantities and their relationships, and read to understand what mathematical questions can be answered.

When students apply this strategy, they can state their final answer, answer the question thoroughly, be precise, and communicate a claim that makes sense mathematically. When identifying the claim, students are strongly encouraged to employ Mathematical Practice 1: Make sense of problems and persevere in solving them, and Mathematical Practice 6: Attend to precision. 

For the evidence component, we intentionally got students into the habit of showing their work in a method that best suited their problem-solving. Students may use words, numbers, graphs, symbols, data tables, or drawings when communicating their problem-solving.

When supporting their claim, students are encouraged to employ Mathematical Practice 2: Reason abstractly and quantitatively, Mathematical Practice 4: Model with mathematics, and Mathematical Practice 5: Use appropriate tools strategically. 

For the reasoning component, we wanted students to justify their problem-solving method by explicitly communicating how the information given in the problem helped them decide upon a strategy to employ, the math skill(s) they learned that helped solve the problem, and the concepts they built on to solve the problem.

When conveying reasoning, students are encouraged to employ Mathematical Practice 3: Construct viable arguments and critique the reasoning of others, Mathematical Practice 7: Look for and make use of structure, and Mathematical Practice 8: Look for and express regularity in repeated reasoning.

Our CER framework can be implemented as is or tweaked based on your staff’s input and student needs. The critical thing to remember is that students will not master this skill overnight. Teachers will experience better student success if they allocate time daily to model the CER process when solving multi-step word problems.

To ensure that the process is student-led, it is strongly recommended that teachers allow students to use CER when working independently, in groups, and as a whole class. Ultimately, the end goal is for students to understand the CER framework so that they can apply it in real-world situations. 

CER MONITORING

In addition to utilizing collaborative planning to facilitate PD on effective CER implementation, my math team monitors student CER work samples every quarter via a rubric. We engage in weekly discourse about pedagogical practices and student misconceptions about problem-solving.

The admin team continuously assesses student and teacher CER implementation through quarterly learning walks. Learning walk data is shared within 72 hours, and each teacher is afforded individualized feedback and action steps to implement. 

As we close out this school year, my math department recognizes that we still have a lot of work to do. However, we are celebrating the fact that we have seen student gains in benchmark assessment scores, CER work samples, and student attitudes toward math. Going into the next school year, we expect even greater gains. 

Sabrina Crusoe, Sayema Tareq, Adrienne Westlake, and  Michelle Richardson contributed to this article.  

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10.1.2: Problem Solving Approaches and Interventions

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There are six problem solving approaches and interventions most commonly used among practitioners. Each approach examines a different aspect of a social problem. The nature of the problem and people involved determines the most appropriate intervention to apply.

A social systems approach examines the social structure surrounding the problem or issue. This approach requires macro, meso, and micro levels of analysis (see pages 12-13) to help understand the structure of the problem and the arrangement of individuals and social groups involved. Analysis requires comprehension of the entire issue and parts associated, as well as, which components and protocols of the structure are independent or dependent of each other. Application of this approach requires grasp of the complete problem including the hierarchy, order, patterns, and boundaries of individuals and social groups including their interactions, relationships, and processes as a body or structure surrounding the issue (Bruhn and Rebach 2007).

The interventions deployed using a social systems approach focus on establishing and maintaining stability for all parties even while change is occurring. Social system interventions require change agents or leaders such as sociological practitioners to help control and guide inputs (what is put in or taken into the problem) and outputs (what is produced, delivered, or supplied resulting from change) used in problem solving (Bruhn and Rebach 2007). This approach requires the involvement of everyone in the social structure to design or re-design the system and processes around the issue.

The human ecology approach examines the “web of life” or the ecosystem of a social problem or issue. This approach is often visually represented by a spider web to demonstrate how lives are interlinked and interdependent. A human ecology approach focuses on macro and meso levels of analysis to develop knowledge about the social bonds, personal needs, and environmental conditions that impede or support life challenges and opportunities for individuals. Practitioners evaluate and analyze where individuals and groups fit in the social structure or ecosystem and their roles. The purpose of this approach is to identify cognitive and emotional boundaries people experience living in social systems to help confront and remove the obstacles they face.

Interventions applied in a human ecological approach target changes in families, institutions, and small communities. The goal is to confront the stressors and strain created by social situations and settings. Interventions from a human ecology approach help people determine acceptable behaviors within different social environments (Bruhn and Rebach 2007). Practitioners work with social groups to remove collaborative challenges between groups in a social ecosystem and the individuals working and living within them. Change is concentrated on developing a new system and process to support and remove obstacles for individuals effected by a social problem.

• Describe the social systems approach and explain what type of social problems or issues this approach is the most valid method to use.
• Describe the human ecology approach and explain what type of social problem or issues this approach is the most valid method to use.
• A county mental health court
• Gender neutral bathrooms on a college campus
• Anti-bullying campaign in local K-12 schools

A life cycle approach examines the developmental stages and experiences of individuals facing issues or various life crises. Meso and micro levels of analysis are required with this method. Data gathered assists practitioners in understanding the adaption of individuals or groups to change, challenges, and demands at each developmental stage of life (Bruhn and Rebach 2007). Analysis incorporates evaluation of interpersonal connections between a person and the environment, life transitions, and patterns. This approach if applicable when working with individuals, groups, and organizations, which all have and go through a life cycle and stages of development.

Interventions using this approach target changes in social norms and expectations of individuals or groups facing difficulties. Practitioners help identify the context and issues creating anxiety among individuals or groups and facilitate coping strategies to attack their issues. This approach builds on positive personal and social resources and networks to mend, retrain, or enable development and growth.

The clinical approach evaluates disease, illness, and distress. Both meso and micro levels of analysis are required for this method. Practitioners assess biological, personal, and environmental connections by surveying the patient or client’s background, and current and recent conditions (Bruhn and Rebach 2007). A Patient Evaluation Grid (PEG) is the most commonly used tool for data collection. This approach requires in-depth interactions with the patient or client to identify themes associated with their condition and the structure of the social system related to their illness and support. When applying this approach in medical practice, the evaluation and analysis leads to a diagnosis.

• Describe the life cycle approach and explain what type of social problems or issues this approach is the most valid method to use.
• Describe the clinical approach and explain what type of social problem or issues this approach is the most valid method to use.
• Policing strategies to reduce crime and improve community relationships
• Reductions in self-injury or cutting among teens
• A community college social work education degree program

Intervention in a clinical approach concentrates on removal of symptoms, condition, or changes in the individual to solve the problem. The overarching goal of this method is to prevent the problem from reoccurring and the solution from interfering with the individual’s functioning. Problem management must minimally disrupt the social system of the patient or client.

A social norms approach focuses on peer influences to provide individuals with accurate information and role models to induce change (Bruhn and Rebach 2007). This approach observes macro, meso, and micro levels of analysis. Intervention centers on providing correct perceptions about thinking and behavior to induce change in one’s thoughts and actions. This technique is a proactive prevention model aimed at addressing something from happening or arising.

There are three levels of intervention when applying a social norms approach (Bruhn and Rebach 2007). Practitioners use interventions independently or together for a comprehensive solution. At the universal level of intervention , all members of a population receive the intervention without identifying which individuals are at risk. A selective level of intervention directs assistance or services to an entire group of at risk individuals. When specific individuals are beyond risk and already show signs of the problem, they receive an indicated level of intervention . A comprehensive intervention requires an integration of all three levels.

Practitioners assist communities in problem solving by applying a community based approach . All three levels of analysis (macro, meso, and micro) are required for this method. The aim of this approach is to plan, develop, and implement community based interventions whereby local institutions and residents participate in problem solving and work towards preventing future issues. Practitioners work with communities on three outcomes, individual empowerment, connecting people, and improving social interactions and cooperation (Bruhn and Rebach 2007). Concentrating on these outcomes builds on community assets while tailoring solutions to local political, economic, and social conditions. By building bridges among individuals and groups in the community, practitioners facilitate connections between services, programs, and policies while attacking the problem from multiple vantage points.

A community based approach helps ensure problem analysis, evaluation, and interventions are culturally and geographically appropriate for local residents, groups, and organizations. To operate effectively, this intervention requires practitioners to help facilitate face-to-face interactions among community members and develop a communication pattern for solving community problems. To build an appropriate intervention, practitioners must develop knowledge and understanding about the purpose, structure, and process of each group, organization, and collaboration within the community (Bruhn and Rebach 2007). Upon implementation, a community based approach endows local residents and organizations to observe and monitor their own progress and solutions directly.

• Describe the social norms approach and explain what type of social problems or issues this approach is the most valid method to use.
• Describe the community based approach and explain what type of social problem or issues this approach is the most valid method to use.
• Human trafficking prevention program
• Reductions in electronic cigarette, vaping, and new tobacco product usage

Problem Solving Toolbox: Problems Solving Methodologies

• Inquiry & Analysis/Judging
• Problems Solving Methodologies
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Problem Solving Methodologies tool

• A 5-Step Problem-Solving Strategy (https://academic.cuesta.edu/acasupp/as/407.htm) Appreciate the Complexities Involved in Decision-Making & Problem Solving
• Problem-Solving Techniques (http://www.mindtools.com/pages/main/newMN_TMC.htm) The 25+ tools in this section help you solve complicated business problems

Journal Articles & eBooks

• Analyses of Information Systems Students' Applications of Two Holistic Problem Solving Methodologies. by Musa, P. F., Edmondson, V., & Munchus, G. Journal of Information Systems Education. Winter2005, Vol. 16 Issue 4, p391-408. 18p.
• Breakthrough problem solving with action learning: Concepts and cases by Marquardt, M. J., & Yeo, R. K Publication Date: 2012 eBooks on EBSCOhost
• Decision making and problem solving strategies by Adair, John Publication Date: 2010 Ebrary
• Diagnostic Quality Problem Solving: A Conceptual Framework and Six Strategies. by DE MAST, JEROEN Quality Management Journal. 2013, Vol. 20 Issue 4, p21-36. 16p.
• Problem posing based on investigation activities by university students. by Ponte, J., & Henriques, A. Educational Studies in Mathematics; May2013, Vol. 83 Issue 1, p145-156, 12p, 1 Diagram
• Problem Solving for Teams : Make Consensus More Achievable by Pokras, Sandy Publication Date: 2010 eBooks on EBSCOhost
• Scenario Visualization : An Evolutionary Account of Creative Problem Solving by Arp, Robert Publication Date: 2008 eBooks on EBSCOhost
• Solving Everyday Problems with the Scientific Method : Thinking Like a Scientist by Mak, Don K., Angela T. Mak, and Anthony B. Mak. Publication Date: 2009 eBooks on EBSCOhost
• Solving Problems with Design Thinking : 10 Stories of What Works by Liedtka, J., Bennett, K. B., & King, A. Publication Date: 2013 eBooks on EBSCOhost
• Toward a creative problem-solving methodology with knowledge provision by Zhu, Z., Nagalingam, S., & Hsu, H. Applied Artificial Intelligence. Oct2011, Vol. 25 Issue 9, p836-881. 46p.

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