relevance of critical thinking in social science methods

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5.1: Understanding Critical Thinking

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Introduction

Society depends on persuasion. We are flooded with content from TV, radio, magazines, books, billboards, and the Internet. This leaves us with hundreds of choices about what to buy, where to go, and who to be. It’s easy to lose our heads in the cross-current of competing ideas—unless we develop skills in critical thinking. When we think critically, we can make choices with open eyes.

It has been said that human beings are rational creatures.

Yet no one is born as an effective thinker. Critical thinking is a learned skill. This is one reason that you study so many subjects in higher education—math, science, history, psychology, literature, and more. A broad base of courses helps you develop as a thinker. You see how people with different viewpoints arrive at conclusions, make decisions, and solve problems. This gives you a foundation for dealing with complex challenges in your career, your relationships, and your community.

Creative thinking often involves analyzing an idea into parts and then combining those parts in a new way. Another source of creativity is taking several ideas and finding an unexpected connection among them. In either case, you are thinking at a very high level. You are going beyond agreement and disagreement to offer something unique—an original contribution of your own.

Critical and creative thinking are exciting. The potential rewards are many, and the stakes are high. Your major decisions in life—from choosing a major to choosing a career—depend on your skills at critical and creative thinking.

Use the suggestions in this module to claim the thinking powers that are your birthright. The critical thinker is one aspect of the successful student who lives inside you.

The Benefits of Critical Thinking

Successful students are critical thinkers. But why does that matter? Seeing yourself as a critical thinker offers many benefits.

Critical thinking frees us from nonsense. Novelist Ernest Hemingway once said that anyone who wants to be a great writer must have a built-in, shockproof “crap” detector (Rees Cheney 1990). That inelegant comment points to a basic truth: As critical thinkers, we are constantly on the lookout for thinking that’s inaccurate, sloppy, or misleading.

Critical thinking is a skill that will never go out of style. At various times in human history, nonsense has been taken for the truth. For example, people have believed the following:

• Illness results from an imbalance in the four vital fluids: blood, phlegm, water, and bile.
• Racial integration of the armed forces will lead to the destruction of soldiers’ morale.
• Women are incapable of voting intelligently.
• People will never invent anything smaller than a transistor. (This was before the computer chip.)

Critical thinkers in history arose to challenge short-sighted ideas such as these listed. These courageous men and women held their peers to higher standards of critical thinking.

Critical thinking frees us from self-deception. Critical thinking is a path to freedom from half- truths and deception. You have the right to question everything that you see, hear, and read. Acquiring this ability is a major goal of a college education.

One of the reasons that critical thinking is so challenging—and so rewarding—is that we have a remarkable capacity to fool ourselves. Some of our ill-formed thoughts and half-truths have a source that hits a little close to home. That source is ourselves.

Successful students are willing to admit the truth when they discover that their thinking is fuzzy, lazy, based on a false assumption, or dishonest. These students value facts. When a solid fact contradicts a cherished belief, they are willing to change the belief.

Critical thinking is thorough thinking. For some people, the term critical thinking has negative connotations. If you prefer, use thorough thinking instead. Both terms point to the same activities: sorting out conflicting claims, weighing the evidence, letting go of personal biases, and arriving at reasonable conclusions. These activities add up to an ongoing conversation—a constant process, not a final product.

We live in a culture that values quick answers and certainty. These concepts are often at odds with effective thinking. Thorough thinking is the ability to examine and reexamine ideas that might seem obvious. This kind of thinking takes time and the willingness to say three subversive words: I don’t know .

Thorough thinking is the basis for much of what you do in school—reading, writing, speaking, listening, note taking, test taking, problem solving, and other forms of decision making. Skilled students have strategies for accomplishing all these tasks. They distinguish between opinion and fact. They ask probing questions and make detailed observations. They uncover assumptions and define their terms. They make assertions carefully, basing them on sound logic and solid evidence. Almost everything that we call knowledge is a result of these activities. This means that critical thinking and learning are intimately linked.

Characteristics and Behaviors of Critical Thinkers, Part 1

The highest levels of critical thinking call for the highest investments of time and energy. Also, moving from a lower level of thinking to a higher level often requires courage and an ability to tolerate discomfort. Give yourself permission to experiment, practice, and learn from mistakes.

The following suggestions identify things to look for to deepen your critical thinking skills:

Look for different perspectives. Imagine Donald Trump, Cesar Chavez, and Barack Obama assembled in one room to debate the most desirable way to reshape our government. Picture Beyoncé, Oprah Winfrey, and Mark Zuckerberg leading a workshop on how to plan your career.

When seeking out alternative points of view, let scenes like these unfold in your mind.

Dozens of viewpoints exist on every important issue—reducing crime, ending world hunger, preventing war, and educating children, to name a few. But few problems have any single, permanent solution. Each generation produces its own answers to critical questions on the basis of current conditions. Our search for answers is a conversation that spans centuries. On each question, many voices are waiting to be heard.

You can take advantage of this diversity by seeking out alternative views with an open mind. When talking to another person, be willing to walk away with a new point of view—even if it’s the one you brought to the table—when faced or presented with new evidence.

Look for assertions. Speakers and writers present their key terms in a larger context called an assertion . An assertion is a complete sentence that directly answers a key question. For example, consider this sentence from an earlier lesson: “Mastery means attaining a level of skill that goes beyond technique.” This sentence is an assertion that answers an important question, How do we recognize mastery?

Look for multiple solutions. When asking questions, let go of the temptation to settle for just a single answer. Once you have come up with an answer, say to yourself, “Yes, that is one answer. Now what’s another?” Using this approach can sustain honest inquiry, fuel creativity, and lead to conceptual breakthroughs. Be prepared. The world is complicated, and critical thinking is a complex business. Some of your answers might contradict others. Resist the temptation to have all of your ideas in a neat, orderly bundle.

Look for logic and evidence. Uncritical thinkers shield themselves from new information and ideas. As an alternative, you can follow the example of scientists, who constantly search for evidence that contradicts their theories. The following suggestions can help you do so.

The aim of using logic is to make statements that are clear, consistent, and coherent. As you examine a speaker’s or writer’s assertions, you might find errors in logic—assertions that contradict each other or assumptions that are unfounded.

Also assess the evidence used to support points of view. Evidence comes in several forms, including facts, expert testimony, and examples. To think critically about evidence, ask questions such as the following:

• Are all or most of the relevant facts presented?
• Are the facts consistent with one another?
• Are facts presented accurately or in a misleading way?
• Are opinions mistakenly being presented as facts?
• Are enough examples included to make a solid case for the viewpoint?
• Do the examples truly support the viewpoint?
• Are the examples typical? Could the author or speaker support the assertion with other similar examples?
• Is the expert credible—truly knowledgeable about the topic?
• Does this evidence affirm or contradict something that I already know?

Characteristics and Behaviors of Critical Thinkers, Part 2

In addition to knowing what to look for, critical thinkers understand that there are many different perspectives and they have to consider all points of view.

Consider controversial topics. Many people have mental hot spots —topics that provoke strong opinions and feelings. Examples are abortion, homosexuality, gun control, and the death penalty. To become more skilled at examining various points of view, notice your own particular hot spots. Make a clear intention to accept your feelings about these topics and to continue using critical thinking techniques in relation to them.

One way to cool down our hot spots is to remember that we can change or even give up our current opinions without giving up ourselves. That’s a key message behind the power processes: “Ideas are tools” and “Detach.” These articles remind us that human beings are much more than the sum of their current opinions.

Consider alternatives. One path to critical thinking is tolerance for a wide range of opinions. Taking a position on important issues is natural. When we stop having an opinion on things, we’ve probably stopped breathing.

Problems occur when we become so attached to our current viewpoints that we refuse to consider alternatives. Likewise, it can be disastrous when we blindly follow everything any person or group believes without questioning its validity. Many ideas that are widely accepted in Western cultures—for example, civil liberties for people of color and the right of women to vote—were once considered dangerous. Viewpoints that seem outlandish today might become widely accepted a century, a decade, or even a year from now. Remembering this idea can help us practice tolerance for differing beliefs and, in doing so, make room for new ideas that might alter our lives.

Consider the source. A critical thinker takes into consideration the source of the information being reviewed. For example, you may have an article on the problems of manufacturing cars powered by natural gas. It might have been written by an executive from an oil company. Check out the expert who disputes the connection between smoking and lung cancer. That “expert” might be the president of a tobacco company.

This is not to say that we should dismiss the ideas of people who have a vested interest in stating their opinions. Rather, we should take their self-interest into account as we consider their ideas.

Characteristics and Behaviors of Critical Thinkers, Part 3

Critical thinkers take specific actions to continue to build their critical thinking skills, as described in the following suggestions:

Define terms. Imagine two people arguing about whether an employer should limit health care benefits to members of a family. To one person, the word family means a mother, father, and children; to the other person, the word family applies to any individuals who live together in a long-term, supportive relationship. Chances are the debate will go nowhere until these two people realize that they’re defining the same word in different ways.

Conflicts of opinion can often be resolved—or at least clarified—when we define our key terms up front. This is especially true with abstract, emotion-laden terms such as freedom, peace, progress , or justice . Blood has been shed over the meaning of those words. Define terms with care.

Understand before criticizing. Polished debaters are good at summing up their opponents’ viewpoints—often better than the people who support those viewpoints themselves. Likewise, critical thinkers take the time to understand a statement of opinion before agreeing or disagreeing with it.

Effective understanding calls for listening without judgment. Enter another person’s world by expressing her viewpoint in your own words. If you’re conversing with that person, keep revising your summary until she agrees that you’ve stated her position accurately. If you’re reading an article, write a short summary of it. Then scan the article again, checking to see whether your synopsis is on target.

Be willing to be uncertain. Some of the most profound thinkers have practiced the art of thinking by using a magic sentence: “I’m not sure yet.”

Those are words that many people do not like to hear. Our society rewards quick answers and quotable sound bites. We’re under considerable pressure to utter the truth in 10 seconds or less.

In such a society, it is courageous and unusual to take the time to pause, look, examine, be thoughtful, consider many points of view, and be unsure. When a society adopts half-truths in a blind rush for certainty, a willingness to embrace uncertainty can move us forward.

Write about it. Thoughts can move at blinding speed. Writing slows down that process. Gaps in logic that slip by us in thought or speech are often exposed when we commit the same ideas to paper. Writing down our thoughts allows us to compare, contrast, and combine points of view more clearly—and therefore to think more thoroughly.

Notice your changing perspectives. Researcher William Perry found that students in higher education move through stages of intellectual development (Rees Cheney 1990). In earlier stages, students tend to think there is only one correct viewpoint on each issue, and they look to their instructors to reveal that truth. Later, students acknowledge a variety of opinions on issues and construct their own viewpoints.

Remember that the process of becoming a critical thinker will take you through a variety of stages. Give yourself time, and celebrate your growing mastery of critical thinking.

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Original research article, critical thinking as a necessity for social science students capacity development: how it can be strengthened through project based learning at university.

• 1 Department of Economics Education, Faculty of Teacher Training and Education, Universitas Tanjungpura, Pontianak, Indonesia
• 2 Universitas Khairun, Ternate, Indonesia

Critical thinking is necessary for students because it empowers them to solve problems, especially during the learning stage and in real-life situations within society. Based on this fact, the present study proposes a citizenship project model that aims to enhance the Elementary School Teacher Education Study Program by emphasizing critical thinking among students during the teaching of Civic Education at universities in Indonesia. The research is of the experimental quasi-research type, which comprises two classes: an experimental class and a control class. Both the classes were conducted to compare the effectiveness of the proposed citizenship project learning model. The statistical package for the social sciences was used for data analysis. To attain the required results on the implementation of the citizenship project learning model, there were several stages, including problem identification, problem formulation, information gathering, documenting the process, showcasing the results, and reflective analysis of the model implementation process. The results have revealed a significant improvement in the critical thinking abilities of the students in the experimental class category compared to the control-class category. Thus, it is concluded that the adoption of a citizenship project learning model is appropriate for critical thinking skills' improvement of students taking up citizenship education study programs at universities.

Introduction

The development of critical thinking skills is very essential for every student of higher education today ( Kwangmuang et al., 2021 ). Globally, it has been found that 85% of teachers entail a belief that today's students have limited critical thinking abilities, mostly at the time of entry to university ( Jimenez et al., 2021 ). This development is coupled with the fact that, the world of present is facing rapid transformation in technology and scientific knowledge ( Kraus et al., 2021 ), something that is affecting people from all walks of life, including eroding their love for nationalism and affecting their attachment to nationalistic values ( Smith, 1983 ). This situation is also worsened by the existing learning models on citizenship ( Maitles, 2022 ), which are said to have not fully assisted students in developing critical thinking skills, thereby leading to difficulties in reasoning with the mindset of mature citizenry. This in turn affects their communication skills and leads to difficulties in responding to social phenomena that take place in society ( Castellano et al., 2017 ).

Because of the importance of critical thinking in solving problems related to students' learning, critical thinking cannot be separated from educational institutions ( Kim and Choi, 2018 ), especially from institutions of higher education ( Collier and Morgan, 2008 ), which are empowered to address challenges related to human resource development through the implementation of teaching and learning content. This development in turn influences students' change in mindset toward a positive direction by bringing about a change in their attitudes ( Sapriya, 2008 ). For this reason, when citizenship education is included in the realm of higher education it needs to contribute to the development of critical thinking skills as one of the compulsory subjects being taught to each student at any university in Indonesia, with the aim of achieving a 2045 Golden Indonesia ( Malihah, 2015 ). The main concern of such an educational course would be to create students who are able to instill a sense of nationalism and patriotism, as well as inculcate a sense of responsibility as future citizens who are competitive, intelligent, independent, and are able to defend their homeland, nation, and state ( Dirwan, 2018 ). Based on the above reasons, this paper focuses on the development of the critical thinking ability of student teachers by the lecturers of citizenship education by using a project-based citizenship learning model.

The concept of developing such critical thinking skills has paved the way for designing a Project Citizen model, which has been named a project-based citizenship learning model. This model has a twofold objective. Because it not only emphasizes the development of abilities in the form of mastery of skills alone, but more importantly, it emphasizes being critical in views and at decision-making, intellectually, and in character thinking ( CCE, 1998 ; Budimansyah, 2009 ; Nusarastriya et al., 2013 ; Falade et al., 2015 ; Adha et al., 2018 ) presented in practice through daily activities. To prepare students to realize the mastery of skills, such as critical thinking skills, positive mentality, and independent personality, a project-based citizenship learning model ( Adha et al., 2018 ) serves as an appropriate problem-based instructional treatment that can lead students to hone their critical thinking skills ( Brookfield, 2018 ).

The project citizen learning model is a strategy and art in the learning process to meet the learning objectives, especially students' critical thinking skills ( Susilawati, 2017 ). The Project Citizen model can develop students' abilities in terms of knowledge, skills, and civic character, as well as shape their democratic attitudes, and hence moral values ( Ching Te Lin et al., 2022 ). In addition, it can encourage student participation as citizens who are trained and prepared to learn to solve problems, both in the educational realms and government circles, as well as in society and family ( CCE, 1998 ; Budimansyah, 2009 ; Lukitoaji, 2017 ). The Project Citizen model can also encourage students acquire skillset such as intentional development through change. In fact, people can actively become involved in these changes, which may effectively take place on an ongoing basis ( Dharma and Siregar, 2015 ).

Therefore, the project citizenship model in Civic Education learning must be implemented because it is a major contributor to advancing students' critical thinking skills. This model works in such a way that it attracts or calls students to participate in dealing with social problems within a democratic and constitutional way of thinking in society through a Project Citizen-based learning process ( Budimansyah, 2009 ; Fry and Bentahar, 2013 ).

This research was conducted at Khairun Ternate University, a state university founded within the Province of North Maluku, Indonesia. Being one of the most favored universities in the region, its leadership ensures that the institution become a center of critical thinking and knowledge development, one of the soft skills required for national growth and development by shaping students and citizenship education students as future leaders. This study sought 1. To determine whether project-based citizenship education lectures can lead to improvement in critical thinking skills among students; 2. To examine students' critical thinking ability before taking up the study of Citizenship Education, we used a project-based citizenship learning model; and 3. To understand the difference in critical thinking ability between students who were taught using the project-based citizenship learning model and those who were taught using conventional models.

Basing on the above-mentioned aspects, this study sought to address and fill the gaps in students' thinking abilities, by sharpening their ways of looking at the varying citizenship challenges faced in the country. The author(s) implemented a project-based learning conceptual model, as it entailed the required aspects in improving students' thinking competences.

Literature review

Citizenship education as a compulsory subject at university.

The inclusion of subjects pertaining to Citizenship Education at all levels of education is required to sharpen and transform students into responsible stakeholders in nation building ( Gaynor, 2010 ; Kawalilak and Groen, 2019 ) of any given country. In Indonesia, Citizenship Education has of recent times attracted the attention of everyone by leading to varying discussions and policies ( Marsudi and Sunarso, 2019 ) on the program and steps for its implementation as a course or subject that promotes democratic values and shapes citizens into responsible persons who think positively and decide wisely.

Citizenship Education is also basically a vehicle for educating citizens to become democratic citizens ( Hahn, 1999 ). The implementation of this type of education program is carried out by carefully designing the material to be delivered from the curriculum so that it can be applied, assessed, and updated for the purposes of the community ( Callahan and Obenchain, 2013 ). This educational effort is believed to be an integral part of the process of transforming society in all aspects of life, whether social, political, economic, cultural, or spiritual.

By law, Citizenship Education is compulsory because it is enshrined in the Indonesian Constitution. According to Law No. 20 of 2003 on the National Education System ( Nurdin, 2015 ), Citizenship Education explicitly refers to the task of education, whereby it should be able to determine the potential of students and be able to change their morals and character for the better ( Raihani, 2014 ). The law explicitly states that the task of education is to improve the behavior of educated people. Changes in behavior and character have the potential to advance the nation and the state at large. Therefore, education must aim to develop the potential for students to become faithful and obedient servants of God, be healthy, knowledgeable, and competent. These abilities must meet three domains: knowledge, affective, and psychomotor abilities.

Philosophical basis for citizenship education

Every science has a philosophical foundation as a scientific root that can be used as the basis of knowledge ( Ginzburg, 1934 ). Likewise, Citizenship Education too has its own foundation, ontologically, epistemologically, and axiologically ( Uljens and Ylimaki, 2017 ). As it is known that Citizenship Education (Civics) developed from the civic concept with a lexical basis based on the word used in ancient Rome, namely, Civicus ( Cresshore, 1986 ; Winataputra, 2001 ). At that time, Civicus had the meaning of citizens. This term has been adapted especially in Indonesia as a concept called “Citizenship Education.”

Citizenship Education has developed both scientifically and in curricular form, hence, it touches on the broader aspects of sociocultural activities with the nature and various kinds of studies and dimensions ( Cresshore, 1986 ). Furthermore, the epistemological study of Citizenship Education focuses on the topic of “citizenship transmission,” the essence of the first social science study to obtain knowledge believed to be a tradition of self-evident truth. When drawn into learning, Citizenship Education lies at the core of social studies learning ( Anderson et al., 1997 ), which includes studies of scientific disciplines both in practice and concepts called “social studies” ( Barr et al., 1978 ; Soemantri, 2011 ). As a cross-disciplinary study, Citizenship Education is substantially driven by various types of scholarships, including political, social, and humanities. Although integrated into various studies, Citizenship Education can be held in the school sector, universities, and communities ( Winataputra, 2001 ).

From the description given above, it can be interpreted that the inclusion of Citizenship Education as a scientific area of specialization determines the study of what, how, and for what knowledge is constructed. We have long recognized terms in the study of Educational Philosophy, which include perennialism, essentialism, progressivism, and reconstructionism ( Brameld, 1955 ). The four terms of Educational Philosophy are related to Citizenship Education, among which philosophically Civic Education (Civics) is based on the concept of “reconstructed philosophy of education” which has a suitability to fulfill scholarship in terms of “perennialism, essentialism, progressivism, reconstructionism” ( Winataputra, 2001 ). The philosophy of essentialism looks at educational needs, which is the result of a proof that has been tested and experienced. The foundation is taken through an eclectic state that is philosophically centered on sophisticated knowledge (ideas) and reality (real).

The linkage between these educational philosophies makes this philosophical view sociopolitical in line with the Indonesian human conception, which is still an ideal–conceptual profile that must be realized and fought for continuously ( Winataputra and Budimansyah, 2007 ). Citizenship Education is expected to have an effect on three roles, namely; first, in the role of a curriculum that has a planned concept for educational institutions, both legally at the level of the education unit and outside of official activities; second, having an engagement plan to play an active role in the community in the context of social and cultural interaction; and third, having a role in the treasures of scientific knowledge, both in the sector of concept studies, academic ideas, and studies that have certain objects, systems, and methods for science. Such a role when examined has aspects, namely, the first aspect, the most important aspect is the academic subject as content that brings changes from their learning experience, for example, the standard content of Citizenship Education subjects, which determine scientific studies and determine the development of the study; the second aspect, in terms of scientific studies carried out including classroom action research, so that teachers will always reflect in every lesson they do ( Winataputra, 2001 ).

Critical thinking skills' development through citizenship education

As it is known, critical thinking in solving problems and finding solutions is indispensable to the learning of Civic Education for students as prospective teachers ( Ige, 2019 ). Moreover, at this time, digital students are challenged with a lot of information that can trap them in the flow of incorrect information (hoax); therefore, students must be critical and selective to the information available. To break down the problem of students' critical thinking ability, certainly not apart from educational institutions, especially college institutions, which are the right institutions to address this challenge, namely applying learning through content and touching the realm of thinking skills ( Sapriya, 2008 ; Aboutorabi, 2015 ; Borden and Holthaus, 2018 ; Japar, 2018 ).

One of the supports in critical thinking is hunting assumptions, which is one of the indicators of critical thinking ability in the Brookfield assumption. Critical thinking explores alternatives to decisions, actions, and practices from views mastered in a variety of contexts, as well as engaging in experience and information ( Brookfield, 2012 ). In this case, students are required to master critical thinking, namely, hunting assumptions, checking assumptions, seeing things from different viewpoints, and taking informed actions ( Brookfield et al., 2019 ). These four aspects help them by serving as the bases for critical thinking in a learning process that focuses on uncovering and examining assumptions, exploring alternative perspectives, and taking information-based actions as a result ( Brookfield, 2019 ). Critical thinking is best experienced as a social learning process, which is important to the learning of Civic Education, which is oriented toward society. This critical thinking ability is also necessary for students to participate in political and community life ( Banks, 1985 ; Sapriya, 2008 ; Budimansyah and Karim, 2009 ; Setiawan, 2009 ; Wahab, 2011 ; Brookfield, 2012 ). At this critical thinking stage, students can think more systematically and critically, and have high sensitivity to cultural differences, as well as local, national, and global perspectives, with a future orientation ( Kalidjernih, 2009 ; Shaw, 2014 ; Lilley et al., 2017 ). One approach can be implemented through education, by honing critical thinking skills during the learning process, to gain a high learning experience to face social problems from various aspects ( Raiyn and Tilchin, 2017 ; Alkhateeb and Milhem, 2020 ).

From the various opinions given above, the ability to think critically of hunting assumptions is needed in the course of the Civic Education field covering many topics and problems ( Cohen, 2010 ). The implementation of a Project Citizen-based learning model as one of the powerful ways to build an understanding in Civic Education aims to provide learning that focuses on the ability of students to solve problems, so that this provision can benefit them while facing and solving various problems of life.

These abilities are manifested not only in the form of mastery of skills, but more importantly, also by the ability to think critically, mentally, and characteristically ( CCE, 1998 ; Budimansyah, 2009 ; Nusarastriya et al., 2013 ; Falade et al., 2015 ; Adha et al., 2018 ). To prepare students to realize the mastery of skills, critical thinking skills, and mental and independent character, the Project Citizen learning model is a problem-based instructional treatment that can lead students to cultivate their critical thinking skills.

The Project Citizen learning model is a strategy and art in the learning process so as to meet the learning objectives that need to be achieved, particularly as regards the critical thinking skills of students ( Susilawati, 2017 ). This is because the Project Citizen model is able to develop the knowledge, proficiency, and character of democratic civic that allows and encourages the participation of students as democratic citizens. The said model can also help in dealing with problems that can be learned and trained according to the situation of self-condition of the environment faced by anyone, as many things are learned in terms of education, government, society, and family ( CCE, 1998 ; Budimansyah, 2009 ; Warren et al., 2013 ; Lukitoaji, 2017 ; Bentahar and O'Brien, 2019 ). The Project Citizen model is also able to encourage the development of change in an intentional manner, so that actively and effectively, the change occurs continuously ( Dharma and Siregar, 2015 ; Marzuki and Basariah, 2017 ). Therefore, it is important to apply the Project Citizen model to the learning of Civic Education as a major contribution to advancing students' critical thinking skills. This is because the learning model of Project Citizen invites students to participate in dealing with social problems in democracies and constitutional ways of thinking in the community through a learning process based on the project citizenship ( Budimansyah, 2009 ; Anker et al., 2010 ; Fry and Bentahar, 2013 ; Romlah and Syobar, 2021 ).

Thus, the learning model of citizen project lecturers and students can reflect on the studies they found during the course of their studies. The study was conducted by each group that was formed at the beginning of the meeting. Finally, lecturers and students hold joint discussions in the classroom by presenting data and information to create alternative solutions to the urgent problems they had to solve.

Methodology

In this study, a quasi-experimental research method was used. A quasi-experimental research approach is mostly referred to as nonrandomized, pre-post-test intervening research design (Harris et al., 200), which is used across fields of study. In the case of this study, the researchers used control groups and experimental groups but did not randomly segregate (non-random assignment) the participants into the two groups ( Creswell, 2017 ).

In this study, researchers want to see and learn more about the new learning model; therefore, they use two different classes, namely control and experimentation, to compare the classes that use project citizens (experimental) with classes that use the old method ( Sukmadinata, 2005 ). From both classes, researchers can compare the effectiveness of the experimental class learning model with that of the control class model. In addition, researchers will also observe how the results of both experiment and control classes reached high values. The researchers' approach is quantitative. This approach was determined by the researchers because it aimed to statistically test and compare both control and experimental classes. Furthermore, this approach emphasized testing to see an average comparison of the two groups that were statistically the same at the beginning of treatment.

Object and area of the study

This study was conducted at Khairun University in North Maluku Province, Indonesia. The research subjects were undergraduate students of the Elementary School Teacher Education Program and were basically those attending Civic Education courses as their major field of study. The research population comprised of all elementary school teacher Education Study Program students in Semester III totaling 100 of them, consisting of two classes, experimental classes and control classes. Each class consisted of 50 student teachers. The experimental classes of 42 females and 8 male students were experimented with a project-based citizenship learning model. In the control class, there were 44 female students and 6 male students using a conventional learning approach.

Data collection techniques

Data collection comes in various forms ( Gray and Bounegru, 2019 ), which can be either qualitative or quantitative data, comprised of either structured or unstructured data collection instruments or tools ( Pitcher et al., 2022 ). Data in its raw form may have no meaning, but due to the setting up of research targets, most research data are given meaning through interpretation by the authors, just like how the authors used with this study.

This means that data collection can be carried out with the help of written tests ( Silvia and Cotter, 2021 ). So in regard to this research too, the data were obtained through written tests, because this is a way the research chose so as to determine the critical thinking abilities of students, for both the experimental and control classes, before or after the treatment, with the method that had been chosen. This test was administered to students in the form of a detailed questionnaire. The question instrument used in the implementation of this research was a written test sheet that was formulated previously through the validation process by the validator. The hypothesis in this study is H 0 : there is no difference in hunting assumption ability between the experimental and control classes. H 1 : There are significant differences in hunting assumption ability between control-class experiments.

Normality test

Parametric statistical analyses were used to compare the average experimental and control classes. In the early stages of the test, a prerequisite test was conducted using a normality test, with the following results:

Based on Table 1 , the Sig. = 0.200 in the experiment, where G is the group. = 0.200 in the control group. The score is Sig. = 0.200 > 0.05 in both groups. Thus, it can be concluded that normally distributed data displayed a level of significance of = 0.05. A homogeneity test was also performed. = 0.344. This score is >0.05, indicating that the data are homogeneous. After conducting a prerequisite test, a t -test was performed on the Sig results. (2-tailed) = 0.259, with a significance level of a = 0.05.

Table 1 . Normality test results.

The score indicates that there are no significant differences between the experimental and control groups, so both groups are eligible to be subject to research. The average similarity between two groups is a measure of the effectiveness of a citizen's project-learning model. There was a significant difference at the final measurement after the intervention.

The findings and discussion are the answers to the formulation of the problem, which is the main focus of this study. This section presents the results of this study. Before implementing the lecture process of learning using the project-based citizenship model, the students were first given an initial trial test to establish the extent of their ability to think critically. Based on the initial proficiency tests conducted, the students' ability to think critically revealed no limitations in ability. The results of the students' initial ability tests are illustrated in Table 2 .

Table 2 . Abilities of students thinking critically.

From the exposure in Table 2 , the basic ability score of critical thinking for both the control class and experimentation descriptively obtained an average similarity that is not much different from the ability of early critical thinking of the students.

Furthermore, the initial ability to hunt assumptions students also conducted different tests in experimental and control classes using the static test. This was done to determine the difference in students' initial critical thinking ability based on the classification of low, medium, and high categories. The test results are listed in Table 3 .

Table 3 . Classification of basic abilities of the students' critical thinking.

The results of the category wise classification test in Table 3 indicate that the initial ability to display critical thinking skills in the experimental and control classes did not show significant differences. This is illustrated in the classification of the ability based on low, medium, and high categories, which also show no significant differences.

Therefore, it is necessary to implement a learning model that can maximize the ability to think critically by the students, that is: through the Citizen Project model. The Citizen Project model was implemented during the 10 meetings. Step-by-step, learning is underway to implement the learning model. The implementation of this Citizen Project learning model achieved the criteria and gained success in the ability to hunt assumptions for students. This can be seen in the tables that describe in general the classification of the low, medium, and high categories. This exposure resulted from the implementation of the learning model project. An explanation citing the success of the citizenship project-based learning is presented in the following table.

Based on the normality test in Table 4 , it can be seen that the total score of overall hunting assumptions of students in both class control and class normal distributed experiments can be calculated and then a t -test conducted. The t -test results showed a sig. (2-tailed) = 0.00 at =0.05, which means that Ha1 is received. Thus, it can be concluded that there are significant differences in critical thinking abilities between the control classes and the experiments.

Table 4 . General differences in student critical thinking ability.

Then, based on the normality tests in low-category students, the total hunting assumptions were scored in a normally distributed experimental class. However, if the control class is not normally distributed, then a t -test cannot be done for the Mann–Whitney U test. The Mann–Whitney U test results obtained were sig. =0.00 at = 0.05, which means that the Ha1 is received. Thus, it can be concluded that there is a significant difference in the hunting assumption ability of low-category students between experimental and control classes.

Then, for students in the moderate category based on the normality test given in the table, the total score of the hunting assumption's ability of moderate-category students either in the control group or in the normally distributed experimental group is calculated, and then a t- test conducted. The t -test results had a large score. (2-tailed) = 0.00 at =0.05, which means the Ha1 is received. Thus, it can be concluded that there are significant differences in hunting assumption capability in general for students in the moderate categories between the control classes and experiments.

For students in the high category based on the normality test for high-category students, the total hunting assumption's ability score in the normal distribution experiment class was reached but in the normal distribution control class, the t -test could not be performed for the Mann–Whitney U test. The Mann–Whitney U test results obtained were sig. =0.00 at = 0.05, which means that the Ha1 is received. Thus, it can be concluded that there is a significant difference in the hunting assumption ability of high-category students between experimental and control classes.

There are also differences in the ability of students to hunt assumptions after the implementation of the Citizen Project model learning in the low, medium, and high categories. The results are outlined in Table 5 .

Table 5 . Student critical thinking ability based on classification.

Based on the normality test in Table 5 , we can see the total score of the critical thinking ability of all students in the normally distributed control group. However, if the experimental group is not normally distributed, then a t -test cannot be performed to test the Mann–Whitney U test results which were obtained as sig. =0.00 at = 0.05, which means that H 1 is received. Thus, it can be concluded that there was a significant difference in the ability of students' critical thinking between the experimental and control classes.

Then, based on the normality test on the ability of thinking critically among the students in the low category if the total critical thinking skills' ability score in the experimental class and control is not normally distributed, then t -test cannot be conducted to test the results of the Mann–Whitney U test in both the control and experimental classes. The Mann–Whitney U test results obtained were sig. = 0.00 at = 0.05, which means that H 1 is received.

Thus, it can be concluded that there was a significant difference in the ability of thinking critically among students in the low category between the experimental and control classes. Then, for the ability of critical thinking, students in the moderate category based on the normality test mentioned in the table can be seen to reach the total score of student critical thinking ability in both class control and class normally distributed experiments. The t -test results had a large score. (2-tailed) = 0.00 at = 0.05 which means that H 1 is received.

Thus, it can be concluded that there are significant differences in the ability of critical thinking of the students in the moderate category between the control classes and experiments.

For high-category students, based on normality tests on the ability of critical thinking of the students in the high-category score, the total critical thinking ability is normally distributed, in control classes. However, if the experimental class is not normally distributed, then a t- t est cannot be performed to test the Mann–Whitney U test results obtained. = 0.00 at = 0.05, which means that H 1 is received.

Thus, it can be concluded that there was a significant difference in students' critical thinking abilities in the high category between the experimental and control classes. To perceive the difference in the development of hunting assumption's ability to conduct an analysis of pre-test and post-test scores, the analysis included the examination of the magnitude of N-Gain in each class, both control and experimentation. The analysis was conducted on both categories based on initial ability.

Based on the table, we can see the difference in improved hunting assumptions between the control classes and experiments that are reviewed from the initial ability. If we analyze the groups based on indicators of critical thinking ability, we can see that in the control group, the improvement of critical thinking skills' ability is almost entirely in the low category, both in the subclass based on the initial ability and on the ability to critical thinking that students are in a low category.

In the experimental class, hunting assumptions increased in the moderate category. There was no increase in the low category, and it was placed in the ability to critical thinking of students. An increase in high-category critical thinking was also not seen. Furthermore, if we analyze the ability to critical thinking based on the initial ability, it can be seen that the control class shows an increase in the ability to critical thinking in the low category. In the experimental class, although the increase was not classified as high, in all classes, critical thinking showed an increase in the moderate category in the experimental class, which was significantly higher compared to the control class on improved critical thinking ability.

The ability to think critically by the students has an important element in assuming, identifying thinking critical skills, comparing critical thinking abilities based on students' opinions, and performing actions and movements to change old habits by promoting the application of new habits properly ( Brookfield, 2012 ). A study on the ability to think critically is intended to give students an understanding of building hypotheses or assumptions, seeing from data and facts to be identified, tracing figures and experts to compare, and making movements as a form of application of student work as their ability to critical thinking present day required life skill ( Brookfield, 2018 ; Gonzalez et al., 2022 ). Thus, the citizen project learning model is suitable for improving students' critical thinking skills through six learning steps. The six steps were identifying problems, formulating or selecting problems, collecting information or data, creating portfolio file documents, displaying studies, and reflecting on the findings discussed together ( Budimansyah, 2009 ; Dewey, 2021 ). The project citizen learning model is based on strategy “inquiry learning, discovery learning, problem-solving learning, research-oriented learning” (learning through research, learning to find/disclose, learning problem-solving, and learning-based research).

This model is packaged by Dewey, who is called a project citizen. This model is appropriate when applied to Citizenship Education to increase students' awareness and thinking ability, as well as to build smart and good citizen characters ( Budimansyah, 2008 ; Rafzan et al., 2020 ). Thus, through the process of learning the citizen project model, lectures have combined theoretical and practical studies that allow the readiness of students with their groups to undergo a mature process. In particular, Civic Education courses have a wide scope of studies, with a project citizen learning model able to train students to improve critical thinking skills, especially critical thinking hunting assumptions.

Project citizen-based learning in Civic Education courses to improve critical thinking skills and sharpens the argumentative way of reasoning among students, hence making them obtain good results. The results of the analysis of the influence of learning on the ability to critical thinking based on the learning model of project citizenship learning conclude that: the ability of students to think critically in the experimental class, in general, differs significantly compared to the control class; the ability to think critically of students in the low category in experimental class among students differed significantly compared to the control class; the critical thinking ability of students with moderate categories in experimental class differed significantly compared to the control class; and lastly, the critical thinking ability of students in the high category in experimental class was significantly higher compared to the control class.

Based on the statistical analysis of critical thinking assumptions' ability, it can be concluded that the understanding of the student's capacity to think critically through experimental classes, using project citizen-based learning models to ensure students learn from low to medium, and attain high critical thinking skills has been enhanced by learning steps that lead them to be more active and productive in understanding information and critical opinions. This means that there is uniformity in the acquisition of value in understanding students' opinion through critical arguments, which indicate that the citizen's project model can improve the critical thinking ability of students, gauged through exchange of opinions.

From the description given above, it appears that the learning model of a project-based citizenship education model has a significant impact on students' development of the critical thinking skills' ability. This is because the implementation of citizenship-based project learning provides learning steps based on experience. Such an experience can help students develop their knowledge, skills, and skills (civic knowledge, civic skills, and civic disposition) ( Fry and Bentahar, 2013 ; Fajri et al., 2018 ).

Conclusively, a project-based citizenship learning model, as a social learning model, has been found to be effective in developing critical thinking skills that impact on all students' competencies. Competency is the ability of students to conduct a given task independently based on the citizenship-based project learning model applied in the course of Civic Education to enhance students' abilities in problem-solving from concept to real-life realization stage ( Medina-Jerez et al., 2010 ; Mitchell et al., 2017 ; Yusof et al., 2019 ). In other words, the project-based model used in this research is expected to contribute to improved students' reasoning capacity while at school and in a real-life situation.

The result is in accordance with Brookfield's (2012 , 2018) opinion about the aims and objectives of the student's critical thinking ability, who states that social problems could be solved by making decisions based on hypotheses and critical thinking. Based on a deeper analysis and investigation of the research findings and discussion, the application of the project-based citizenship learning model in the Civics Education course was able to create an effective learning atmosphere in sharpening students' critical thinking skills and motivating them to be good and responsible human beings. This statement is in line with the objectives of the Civic Education course, which emphasizes the process of creating students who are intelligent, have good character and required morals in society ( Banks, 1985 ; Branson, 1994 ; Budimansyah and Suryadi, 2008 ; Budimansyah, 2009 ; Setiawan, 2009 ). Thus, the results of the study confirmed that the project-based citizenship learning model is not only a proof of the evidence of the improvement in students' critical thinking skills, but the study also notes that the learning model can as well be effective in helping students develop reasoning abilities and good critical thinking abilities which may also help them in solving various issues within society.

Facilitating the growth of critical thinking abilities of a student leads to critical reasoning, hence encouraging productive discussions, which in turn leads to acceptable criticisms and an open exchange of ideas among students to be easily understood, including those ideas based on assumptions and hypotheses. Based on the exposure of the results and discussion of research on the ability to hunt assumptions, students who were engaged in a project-based citizenship learning model obtained better scores for their critical thinking abilities. This implies that such students experience an improvement in their hunting assumption ability compared to students studying through conventional learning. Assembling a project citizen learning model in Civic Education courses can improve students' ability to hunt assumptions. Thus, it can be concluded that Civic Education courses with the application of the learning model project-based citizenship learning model can improve students' critical thinking skills.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Ethics statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author contributions

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

This study was funded by University Administration.

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.

Publisher's note

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Yusof, N. A., Kamaruddin, S., Bakar, F. D. A., Mahadi, N. M., and Murad, A. M. A. (2019). Structural and functional insights into TRiC chaperonin from a psychrophilic yeast, Glaciozyma antarctica. Cell Stress Chaperones. 24, 351–368.

Keywords: citizenship education, citizenship learning project model, critical thinking skills, elementary education, teacher preparation, university curriculum, university education

Citation: Witarsa and Muhammad S (2023) Critical thinking as a necessity for social science students capacity development: How it can be strengthened through project based learning at university. Front. Educ. 7:983292. doi: 10.3389/feduc.2022.983292

Received: 01 July 2022; Accepted: 26 September 2022; Published: 09 January 2023.

Reviewed by:

Copyright © 2023 Witarsa and Muhammad. 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.

This article is part of the Research Topic

Design, Implementation, Assessment, and Effectiveness of Hybrid Problem-Based Learning

3 Social science theories, methods, and values

Learning Objectives for this Chapter

After reading this Chapter, you should be able to:

• understand, apply, and evaluate core social science values, concepts, and theories, which can help inform and guide our understanding of how the world works, how power is defined and exercised, and how we can critically understand and engage with these concepts when examining the world around us.

Social science theory: theories to explain the world around us

As we have discussed in previous chapters, social science research is concerned with discovering things about the social world: for instance, how people act in different situations, why people act the way they do, how their actions relate to broader social structures, and how societies function at both the micro and macro levels. However, without theory, the ‘social facts’ that we discover cannot be woven together into broader understandings about the world around us.

Theory is the ‘glue’ that holds social facts together. Theory helps us to conceptualise and explain why things are the way they are, rather than only focusing on how things are. In this sense, different theoretical perspectives, such as those discussed in this Chapter, act as different lenses through which we can see and interpret the world around us.

Theory testing and generation is also an important part of social scientific research. As shown in the image below, different theories are rooted in different philosophical foundations. That is, various theories arise in accordance with different ways of seeing and living in the world, as well as different understandings about how knowledge is understood and constructed. As we learned earlier in the book, these concern both ontological and epistemological considerations, but also axiological considerations; that is, questions about the nature of value,  and what things in the world hold value (including in relation to one another). While theory is rooted in these philosophical foundations, however, it also gives way to different ways of doing research, both in terms of the methodology and methods employed. Overall, using different theoretical perspectives to consider social questions is a bit like putting on different pairs of glasses to see the world afresh.

Below we consider some foundational social science theories. While these are certainly not the only  theoretical perspectives that exist, they are often considered to be amongst the most influential. They also provide helpful building blocks for understanding other theoretical perspectives, as well as how theory can be applied to guide and build social scientific knowledge.

Structural functionalism

Structural functionalism is a theory about social institutions, ‘social norms’ (i.e., the often unspoken rules that govern social behaviours), and social stability. We talk more about social institutions in the next Chapter of this book, but essentially they are the ‘big building blocks’ of society that act as both repositories and creators/instigators of social norms. These include things like school/education, the state (often called a meta-institution), the family, the economy, and more. In this regard, structural functionalism is considered a macro theory; that is, it considers macro (large) structures in society, and concerns how they work in an interdependent way to produce what structural functionalists believe to be ‘harmonious’ and stable societies. Structural functionalists are particularly concerned with social institutions’ manifest and latent functions, as well as their functions and dysfunctions (Merton [1910-2003]).

Manifest functions of social institutions include things that are overt and obvious. By contrast, latent functions of social institutions are those that are more hidden or secondary. For instance, a manifest function of the social institution of school is to teach students new knowledge and skills, which can assist them to move into chosen careers. Alternatively, we might also argue that school has other latent functions, such as socialisation and conformity to social norms, and building relationships with peers.

In addition to manifest and latent functions, structural functionalists are also concerned with the  functions  and  dysfunctions  of social institutions. They believe, for instance, that dysfunctions play just as much of an important role as functions, because they enable social institutions to identify and punish them, thereby making an example of dysfunctional elements (e.g., punishing those committing crime). This serves to reinforce social norms around how society should function.

Reflection exercise

Take a piece of paper and, in your own words, write down a brief definition of structural functionalism. Then re-read the above sub-section. How does your understanding fit with the information above?

Structural functionalism: want to learn more?

If you’d like to reinforce your understanding of structural functionalism, the below video provides a good summary that might be helpful.

Functionalism (YouTube, 5:40) :

Phenomenology

Phenomenology is the study of our experiences and how our consciousness makes sense of the phenomena (be they objects, people or ideas) around us. As a methodology or approach in the social sciences it has garnered renewed interest in the last few decades to better understand the world around us by studying how we experience the world in a subjective and often individual manner. It is, thus, considered a ‘micro’ theory.

This philosophical approach was developed by Edmund Husserl (1859–1938), and his students and critics in France and Germany (key figures were philosophers Martin Heidegger (1889-1976), Jean-Paul Sartre (1905-1980) and Maurice Merleau-Ponty (1908-1961)) and later made it to the US via influential sociologists, such as Alfred Schütz (1899–1959).

Phenomenologists reject objectivity and instead focus on the subjective and intersubjective, the relations between people, and between people and objects. So, rather than trying to come to some objective truth, they are more interested in relationships and connections between the individual and the world around them. Indeed, there is a strong centering of and focus on the individual and their experiences of the world that phenomenologists believe can tell us about society at large. The individual is also key, as there is a focus on the sensory and the body both as instruments of enquiring as well as enquiry. Thus, we are always already part of the world around us and have to make sense of being here, but also want to go beyond ourselves by understanding others and how they relate to the world. The body features as a key site for such enquiries as it is the physical connection we have with people and objects around us. Further, there is a focus on everyday, mundane experiences as they have much to tell us about how society operates. This background environment in which we as people operate is called a lifeworld,  the shared horizon of experience we share and inhabit. It is marked by linguistic, cultural, and social codes and norms.

One key method inherent to Husserl’s early approaches is ‘bracketing’ , the process of standing back or aside from phenomena to understand it better. Such processes of ‘reflexivity’ and understanding our taken for granted attitudes and beliefs about certain phenomena are crucial to enable the social sciences to better understand the world around us. Debates in philosophy continue around whether such a bracketing is ever fully possible, especially considering that we as humans remain trapped in our minds and  bodies. Nonetheless, phenomenology has had a profound impact in most social sciences to redirect the focus towards the intersubjective nature of life and the lifeworld, within which we experience the world around us.

Take a piece of paper and, in your own words, write down a brief definition of phenomenology. Then re-read the above sub-section. How does your understanding fit with the information above?

Phenomenology: want to learn more?

If you’d like to reinforce your understanding of phenomenology, the below video provides a good summary that might be helpful.

Understanding Phenomenology (YouTube, 2:59) :

Symbolic interactionism

Symbolic interactionism is related to phenomenology as it is also a theory focused on the self. In this regard, it’s also a micro theory – it has particular focus on individuals and how they interact with one another. Symbolic interactionists say that symbolism is fundamental to how we see ourselves and how we see and interact with others. George Herbert Mead (1863-1931) is often regarded as the founder of this theory and his focus was on the relationship between the self and others in society. He considered our individual minds to function through interactions with others and through the shared meanings and symbols we create for the people and objects around us. Mead’s best known book Mind, Self, Society, was posthumously put together by his students and demonstrates how our individual minds allow us to use language and symbols to make sense of the world around us and how we construct a self based on how others perceive us.

Charles Cooley’s (1864-1929) concept of the “ looking glass self ” points out, for instance, that other peoples’ perceptions of us can also influence and change our perceptions of ourselves. Other sociologists, such as Erving Goffman (1922-1982), have built on this understanding, suggesting that ‘all of life is a stage’ and that each of us play different parts, like actors in a play. Goffman argued that we adapt our personality, behaviours, actions, and beliefs to suit the different contexts we find ourselves in. This understanding is often referred to as a ‘dramaturgical model’ of social interaction; it understands our social interactions to be performative – they are the outcomes of our ‘play acting’ different roles.

In explaining this theory, Goffman also referred to what he called ‘impression management’. As part of this, for instance, Goffman drew a crucial distinction between what he referred to as our ‘ front stage selves ‘ and our ‘ backstage selves ‘. For Goffman, our ‘front stage selves’ are those that we are willing to share with the ‘audience’ (e.g., the person or group with whom we are interacting). Alternatively, our ‘backstage selves’ are those that we keep for ourselves; this is the way we act when we are alone and have no audience.

Goffman also pointed to the important role that stigma can play in how we see ourselves and thus, how we act and behave in relation to others. Stigma occurs when “the reaction of others spoils normal identity”. Goffman argued that those who feel stigmatised by others (e.g., through public discourses and ‘frames’ of social issues that vilify certain groups of people) also experience changes in the way they see themselves – that is, their own sense of self-identity is ‘spoiled’. This can lead to other negative effects, such as social withdrawal and poorer health and wellbeing.

Take a piece of paper and, in your own words, write down a brief definition of symbolic interactionism. Then re-read the above sub-section. How does your understanding fit with the information above?

This exercise is to be conducted in small groups. First, get into a small group with other students. Then, do the following:

• Think about your daily life, activities, and interactions with others.
• Take a few moments to identify at least three examples of social symbols that you and other group members frequently use to interpret the world around you.
• Talk about how each of the group members interprets/responds to these symbols. Are there similarities? Are there differences?

Students should share/discuss their thoughts within the group, and if undertaken in a class environment, then report back to the class.

Symbolic interactionism: want to learn more?

If you’d like to reinforce your understanding of symbolic interactionism, the below videos provide good summaries that might be helpful.

Symbolic Interactionism (YouTube, 3:33) provides an easy-to-understand summary of symbolic interactionism:

What does it mean to be me? Erving Goffman and the Performed Self (YouTube, 1:58) provides a helpful summary of Erving Goffman’s conception of the ‘performed self’ – including his notions of a ‘front stage’ and ‘backstage’ self:

Conflict theories

Conflict theories focus particularly on conflict within and across societies and, thus, are particularly interested in power: where it does and doesn’t exist, who does and doesn’t hold it, and what they do or don’t do with it, for example. These theories hold that societies will always be characterised by states of conflict and competition over goods, resources, and more. These conflicts can arise along various lines, though

this group of theories emanate from the work of Karl Marx (1818-1883), who saw the capitalist economy as a primary site of conflict.

In Marx’s view, social ills emanated particularly from what he described as an upper- and lower-class structure, which had been perpetuated across multiple societies (e.g., in ancient societies in terms of slave owners/slaves, or in pre-Enlightenment times between the feudal peasantry/aristocracy). He saw capitalism as replicating this upper/lower class structure through the creation of a bourgeoisie (upper class, who own the means of production) and proletariat (lower class, who supply labour to the capitalist market). Marx also talked about a lumpenproletariat , an underclass without class consciousness and/or organised political power. Classical Marxism takes a macro lens: it is particularly concerned with how power is invested in the social institution of the capitalist economy. In this sense, classical Marxism represents a structural theory of power.

Marx argued that the only way for society to be fairer and more equal was if the proletariat was to rise up and revolt against the bourgeoisie; to “smash the chains of capitalism”! Thus, he strongly advocated for revolution as a means of creating a fairer, utopic society. He stated, “Philosophers have hitherto only interpreted the world, in various ways; the point is to change it” (Marx 1968: 662). Nevertheless, a series of revolutions in the early 20th century that drew on Marxist thinking resulted in power vacuums that made way for violent, totalitarian regimes, as political philosopher Hannah Arendt (1906-1975) argued in On the Origins of Totalitarianism . On this basis, subsequent conflict theorists (and critical theorists) have tended towards advocating for more incremental reforms, as opposed to revolution.

Take a few moments to watch the below two videos, which explain conflict theory in greater detail.

Key concepts: Conflict theory – definition and critiques (YouTube, 2:49) :

Political theory – Karl Marx (YouTube, 9:27) :

After watching these videos, take a piece of paper and, in your own words, write down a definition of conflict theories. After doing so, re-read the above sub-section. How does your understanding fit with the information in the above sub-section, and in the videos? Was anything missing? Is anything still unclear?

Critical theories

Marx saw the capitalist economy as a primary site of oppression, between the working class and the property owning class. Marx advocated for revolution, where the proletariat were urged to rise up and break the chains of capitalism by overthrowing the bourgeoisie. Marx saw this as being necessary for ensuring the freedom of the working classes. Critical theory develops from the work of Karl Marx, supplementing his theory of capitalism with other sociological and philosophical concepts.

Gramsci and cultural hegemony

In addition to Marx, critical theory utilised the work of Italian political philosopher Antonio Gramsci, specifically his concept of ‘Cultural Hegemony’. When we refer to ‘hegemonic’ social norms, we’re referring to social norms that are regarded as ‘common sense’ and thus, which overshadow and suppress alternative norms. Hegemonic norms typically reflect the values of the ruling classes (in Marxist terms, the bourgeoisie). To learn more, you might like to watch the video below:

Hegemony: WTF? An introduction to Gramsci and cultural hegemony (YouTube, 6:25)

Developing from this, critical theory also considers how power and oppression can operate in more subtle ways across the whole of society. Critical theory does not seek to actively bring about revolution, as the possibility for a revolution in the years post-World War Two was unlikely. Whilst critical theorists are by no means opposed to revolution, their focus lies more in identifying how capitalist society and its institutions limits advancement of human civilisation. In this respect, conflict theorists see more opportunities for praxis than classical Marxists.

Critical theory observes how the Enlightenment ideals of freedom, reason, and liberalism have developed throughout the first half of the 1900s. Ultimately, critical theorists see that reason has not necessarily progressed in a positive way throughout history. In fact, reason has developed to become increasingly technical, interested in classifying, regulating, and standardising all aspects of human society and culture. German philosopher Theodor Adorno (1903-1969) thought that Nazi Germany and the holocaust is a devastating example of the potential evils of rationality if developed without a critical perspective.

Another, less extreme, example of this tendency toward standardisation is in the production of art and culture. Big budget films, typically in the superhero or science fiction genre, all appear to be virtually identical: extravagant special effects, epic soundtracks, and relatively simple plots. However, this is not to say that such films are of a poor quality. Rather the similarity and popularity of these films indicates a homogenisation of culture. If culture is merely the reproduction of the same, how can society progress beyond its current point?

This critique of the development of reason throughout the 20th century does not mean that we must abandon reason entirely. To do so would be to discount the vast wealth of knowledge that humanity has come to grasp, as well as prevent further knowledge production. Instead, critical theorists argue that reason should be critiqued to uncover what has been left out of its development thus far, as well as open up the possibility for a more free, progressive form of society.

At its core, then, critical theory can be thought about as being an additional theoretical lens through which we can look at and understand the social world around us. In tune with Flyvbjerg’s (2001) conception of phronetic social science, critical theorists are also concerned with disrupting the systems they observe as a means of achieving social change. Critical theory urges us to recognise, understand and address how capitalist society reproduces itself and limits the free organisation of human beings.

Take a few moments to watch Critical theory definition and critiques (YouTube, 3:26) , which explains critical theory in greater detail.

Take a piece of paper and, in your own words, write down a brief definition of critical theories. Then re-read the above sub-section. How does your understanding fit with the information above and the video?

Critical theory can be applied in myriad different ways to better understand the world around us. In  Critical theory and the production of mass culture (YouTube, 2:12) , critical theory is adopted as a lens to understand and critique the production of mass culture. Watch the video and then consider the questions below.

• Can you think of examples where you could argue that the primary objective of producing art is to preserve the economic structure of the capitalist system?
• Do you agree with the proposition that mass-consumed entertainment, like popular television shows, are only  produced as a source of light entertainment and escapism from work, and thus serve to placate and pacify the worker? Why or why not? (What other  purposes might such entertainment serve, if any?)
• Do you agree with Adorno’s proposition that the products of the ‘culture industry’ are not only the artworks, but also the consumers themselves? Why or why not?

Critical race theory

Critical race theory applies a critical theory lens to the notion of race, seeking to understand how the concept of race itself can act as a site of power and oppression. Arising from the work of American legal scholars during the 1980s (including key thinkers like Derrick Bell [1930-2011] and Kimberlé Crenshaw [1959-]), it originally sought to understand and challenge “the ways in which race and racial power [were]… cosnstructed and represented in American legal culture and, more generally, in American society as a whole.” (Crenshaw et al. 1995: xiii) In particular, it questioned whether the civil rights afforded to African Americans in the aftermath of the civil rights movement had made a substantive impact on their experiences of social justice. Critical race theorists argued that more needed to be done; that civil rights had not had the desired impacts because (amongst other reasons) they:

• were imagined, shaped and brought into being by (predominantly) white, male middle- or upper-class lawyers, and thus, were only imagined within the bounds of white ontology,
• did not move beyond race – race still mattered, and
• implicitly perpetuated white privilege (e.g. they were constrained to only imagine redress and justice within the existing oppressive, white hegmonic system).

Crenshaw (1995: xiii) writes that, although critical race scholars’ work is heterogenous, they are nevertheless united by the following common interests:

• “The first is to understand how a regime of white supremacy and its subordination of people of color have been created and maintained in America, and, in particular, to examine the relationship between that social structure and professed ideas such as ‘the rule of law’ and ‘equal protection’.”
• “The second is a desire not merely to understand the vexed bond between law and racial power but to change it.”

In Australia, scholars have also taken up aspects of a critical race lens to understand how privilege is bound up with race. As Moreton-Robinson (2015: xiii) puts it, in Australia:

Race matters in the lives of all peoples; for some people it confers unearned privileges, and for others it is the mark of inferiority. Daily newspapers, radio, television, and social media usually portray Indigenous peoples as a deficit model of humanity. We are overrepresented as always lacking, dysfunctional, alcoholic, violent, needy, and lazy… For Indigenous people, white possession is not unmarked, unnamed or invisible; it is hypervisible…

Crenshaw has been crucial in also stressing the key importance of understanding how race can also intersect with other aspects of social identity, such as gender, to produce a ‘double’ or ‘triple’ oppression. In Australia, Professor Aileen Moreton-Robinson’s 2000 book, Talkin’ up to the white woman, was also crucial in understanding how Australian feminism could also be oppressive of Indigenous Australian women by not seeing and hearing them or the specific issues they face/d. She called for the need for “white feminists to relinquish some power, dominance and privilege in Australian feminism to give Indigenous women’s interest some priority” (Moreton-Robinson 2000: xxv). This emphasised that an intersectional lens was needed to acknowledge the different but cumulative impacts of both racial oppression and sexism. At the centre of this argument is the reality that “all white feminists [in Australia] benefit from colonisation; they are overwhelmingly represented and disproportionately predominant, have the key roles, and constitute the norm, the ordinary and the standard of womanhood in Australia” (Moreton-Robinson 2000: xxv).

Uproar over critical race theory

During 2020, racial sensitivity training in the USA prompted widespread discussion about critical race theory. Former US President, Donald Trump, posits in the video below that the theory, and the kinds of racial sensitivity training it promotes, are fundamentally racist – against white people. Others argued that this represented a deep misunderstanding of the theory, but also an ignorance of the extent and power of white privilege.

For an example of former President Trump’s views, watch  Trump: Racial sensitivity training on white privilege is ‘racist’ (YouTube, 3:16) :

Postmodern critique of critical race theory

Postmodernists have levelled critique at critical race theory on the basis that understanding/explaining power as being rooted in racial difference has the consequence of reinforcing and perpetuating the validity of ‘race’. Postmodernism, however, rejects the distinct, conceptual bounds of ‘race’ and racialised identities. Instead, it sees race itself as a social construction, which should be questioned and disrupted, thereby leading to new insights that aren’t constrained by socially constructed definitions of race.

Kwame Anthony Appiah, for example, seeks to “probe the very definitions of race itself. He bypasses the empirical question of whether racism exists to ask the theoretical question of what race and racism are” (in Chong-Soon Lee 1995: 441)

Take a piece of paper and, in your own words, write down a brief definition of critical race theory . Then re-read the above sub-section. How does your understanding fit with the information above?

Putting theory into action: rethinking crime through a critical lens

Critical criminologists apply a critical theory lens to the study of crime and criminality. In this regard, critical criminology is concerned with understanding how the criminal justice system can act as a site of power and oppression; a perspective that tends to sit in contrast with western (non-critical) criminology, which sees the criminal justice system as a natural social institution that has the primarily purpose of protecting society against deviants (criminals) and making an example of those who fail to comply with hegemonic social norms. (This non-critical view draws parallels, for example, with the perceived ‘functions’ of the criminal justice system under a structural functionalist perspective, and its role in making examples of ‘dysfunctional’ elements of society.)

Critical criminologists in Australia have considered the role of the criminal justice system as a key site of oppression under, for example, Australian settler colonialism. For instance, Indigenous Australians are, per capita, the most incarcerated peoples in the entire world ( Anthony & Baldry 2017 ) and these incarceration rates are rising, not reducing (ABS 2018). In using a critical lens to understand the difference between incarceration rates for Indigenous and non-Indigenous Australians, however, we can seek better insight into how the criminal justice system operates as a site of oppression, perpetuating white settler colonial norms and values, which seek to punish alternative ontologies and epistemologies. Lynch (cited in Cunneen and Tauri 2016: 26) argued,

In short, criminology is one of the disciplines that established the conditions necessary for maintenance of the status quo of power. It can only do so by oppressing those who would undermine the status quo. In this sense, criminology must be viewed as a science of oppression.

In part, this oppression operates through the construction of knowledge and truth within (positivist) criminology (which relates to Foucault’s conception of power-knowledge, as we touched on last week). In turn, this also involves what Cunneen and Tauri (2016: 26) describe as “the ideologically driven dismissal of Indigenous knowledge about the social world as ‘subjective’, ‘unscientific’, and/or at best ‘folk epistemology’… which in turn paves the way for excluding other ways of knowing from the Western, criminological lexicon”.

In their book, Decolonising criminology, Blagg and Anthony (2019: 22-23) set out a taxonomy for what they see as a decolonised criminology (noting, though, that Blagg and Anthony themselves are non-Indigenous researchers, though they have worked closely with Indigenous peoples and communities for decades).  In their taxonomy (which we have included an adapted version of below), they include the following probing comparisons between a positivist (largely uncritical) criminology and a decolonised (critical) criminology:

A table comparing positivist and decolonial approaches to criminology.

Source: Authors’ adaptation from Blagg & Anthony (2019: 22-23 )

The probes and questions that Blagg & Anthony pose in the above taxonomy are critical in their focus and intent; they seek to critique the criminal justice system as a site of colonial power, but they also seek to change it — through research that produces knowledge about these truths. This is, in essence, a reframing (to use Bacchi’s term) of the nature of criminological research towards a richer, and more historically and culturally contextualised understanding of the Australian criminal justice system. As a result, this produces different knowledge about crime and justice in Australia: knowledge that shifts blame away from the individual (the ‘bad’ Indigenous citizen, to use Moreton-Robinson’s [2009] language) to the structures, history and continuation of colonial oppression.

Critical or radical criminology?

Radical criminology is rooted in the Marxist conflict tradition and sees the capitalist economy as being central to the definitions of crime (arrived at by the bourgeoisie) that constrict, control and suppress the working classes (proletariat).

In contrast (or in addition to), critical criminology is interested in more than just class relations and also sees different opportunities for praxis – tending to favour a more incremental approach to social change as opposed to widespread revolution ( Bernard 1981 )

Drawing on a critical criminology and decolonising perspective, consider the below graph, which shows the over-representation of Indigenous Australians in prisons, indicating an upward trend from 2008-2018. Then consider, from a critical criminology standpoint, what kinds of ‘truths’ might you draw on to help explain this trend?

(To guide your thinking, you may like to revisit the above taxonomy by Blagg and Anthony.)

Watch the below short clip of Senator Patrick Dodson talking in March 2021 about the issue of Aboriginal and Torres Strait Islander deaths in custody. Consider LNP Senator, Amanda Stoker’s response to Senator Pat Dodson, in particular her comment that she “understand[s] the outrage is real… because the lives of every person, though our justice system are important, no matter the colour of their skin.”

In #Estimates , @SenatorDodson fires up over a lack of action on deaths in custody. @stoker_aj ‘s response: “I understand the outrage is real…because the lives of every person, through our justice system are important, no matter the colour of their skin.” #Auspol @SBSNews @NITV pic.twitter.com/jgsb8y9YcD — Naveen Razik (@naveenjrazik) March 26, 2021

What do you think about Senator Stoker’s response to Senator Dodson? How might you analyse her response, through a critical race theory lens?

Choose one of the following social issues:

• The gender pay gap
• The workplace ‘stress’ epidemic
• Homelessness
• Childhood obesity

Consider how your chosen social issue might be explained by drawing on the different theoretical perspectives outlined earlier in this Chapter. Record your thoughts in a short, written explanation.

Reflection exercise: a critical reading of meritocracy

Kim and Choi (2017: 112) define meritocracy as “a social system in which advancement in society is based on an individual’s capabilities and merits rather than on the basis of family, wealth, or social background.” According to Kim and Choi (2017: 116), meritocracy has two key features: “impartial competition” and “equality of opportunity”.

The notion of meritocracy has arisen over the past few centuries primarily in response to feudalism and absolute monarchy, where power and privilege are handed down on the basis of familial lines (‘nepotism’) or friendships (‘cronyism’). This kind of system could (and often did) place people into positions of power, regardless of whether they were the most appropriate or ‘best’ person for the job. In essence, then, the notion of meritocracy is intended to tie social advancement to merit; that is, the focus is supposed to be on ‘what you know’ rather than ‘who you know’, which seems a noble cause, right? Many have argued, however, that a blinkered belief in meritocracy leaves a lot of things out of the ‘frame’.

The belief in meritocracy, and its focus on ‘what you know’ rather than ‘who you know’, can have both positive and negative impacts. Take a piece of paper and write a short list of each.

If critical theory operates according to the broad Marxist understanding of history as class struggle, post-structuralism is a theory that attempts to abandon the idea of grand historical narratives altogether. Fundamentally, post-structuralism differs from other social theories in its rejection of metanarratives , its critique of binaries, and its refusal to understand all human action as being shaped solely by universal social structures. Whilst there is much disagreement between post-structuralist thinkers, these three broad trends help us to understand this social theory.

Post-structuralism

Post-structural accounts of conflict and power can take a macro and micro lens. They see power as transcending social structures, like social institutions (e.g., the state, the economy) and instead being all around us at all times. Michel Foucault (1926-1984), for example, argued that power is everywhere and acts upon us to shape our identities, bodies, behaviours, and being. In terms of a liberal democratic society, therefore, where coercive (‘sovereign’) power is only exerted by the state under certain specific circumstances, Foucault argued that the state otherwise uses its power to create ‘responsibilised’ citizens who absorb hegemonic (i.e. authoritative/dominant) social norms and use these to govern themselves . This relates to what Fairclough (1995: 257) referred to as power by consent:

We live in an age in which power is predominantly exercised through the generation of consent rather than through coercion… through the inculcation of self-disciplining practices rather than through the breaking of skulls (though there is still unfortunately no shortage of the latter).

Foucault was also particularly interested in the link between power and knowledge. He argued that those who hold the power tend to construct knowledge and ‘truth’ in certain ways, which can reinforce their power by, for example, perpetuating certain social norms. This is elaborated on by Watts and Hodgson (2019) in reading 5.2, where they describe Foucault’s conception of power/knowledge as follows:

Truth is not neutral or objective, and is not simply a thing that can be verified scientifically because its ‘truth value’ is dependent on the operation and circulation of power (think, for example, the oft-quoted phrase that ‘truth is whatever the powerful say it is’). In the context of the human and social sciences, power creates knowledge and is also a force for the translation of knowledge of and about human beings into practice… For example, the moment we speak into existence the concept of something as commonplace as ‘human being’ or ‘human rights’ or ‘social justice’ we are using some form of power (truth) to render such things thinkable and knowable as things in the world (Watts and Hodgson 2019: 85-86).

Take a piece of paper and, in your own words, write down a brief definition of Foucault’s post-structural concept of power. Then, re-read the above account. Does your definition align with the information above?

Beck and Risk Society

The notion of risk society is outlined by Ulrich Beck in his 1992 book ‘Risk Society: Towards a New Modernity’. Where society was once organised around wealth distribution based on scarcity, Beck argues that society is becoming increasingly based on the distribution of risks. Risks are defined as “a systematic way of dealing with hazards and insecurities induced and introduced by modernization itself” (Beck 1992: 21). Beck argues that the process of modernisation is no longer focused exclusively on the creation of new technologies, but rather the focus lies in the management of risks of potential technologies. As such, modernisation is becoming increasingly reflexive, involved not only in the production of technologies to meet needs, but rather investigating the often unknown side-effects of technologies. For example, a nuclear energy plant might be built in order to meet society’s increasing energy demand. However, this solution to a specific problem then must deal with the new issue of disposing of this radioactive waste that modernisation itself has produced. This is just one example of the ecological risks inherent with the development of new technologies, which often have unintended side-effects, that must themselves be uncovered and solved.

Postmodernism

Before we can get to postmodernism, we need to define modernism to see what postmodernism wants to supersede. Modernism describes the social upheaval and major changes of 20th century life. It is marked by processes of industrialisation, rationalisation and bureaucratisation – in short a world in which the sciences seemed to provide ever more answers and ultimate truths about the world and us. Modernism or modernity was also about hope for a new society, unfettered technological and material progress and, with advances in scientific fields, led to longer lives and new and exciting materials to make new things to make life easier (think household machines). It was also punctured by some key social movements that brought the world to the brink of destruction in the epic fight over what ultimate truth should prevail. The key political ideologies of fascism, socialism and liberalism clashed in the second World War over their different visions for a new world order. In the post war climate of a new stand-off between socialism/communism and liberalism or the Soviet bloc and ‘the West’ many writers, academics and artists became disillusioned with the modernist project. Slowly critiques of these universalising truths and meta-narratives came to think of this time as a time of postmodernism. Jean-François Lyotard (1924-1998) defined postmodernism as the ‘incredulity towards meta-narratives’, by which he meant that increasingly people were no longer persuaded by grand or master narratives about themselves, a particular nation, people or even humanity. The singular, stable, coherent modern subject was thrown into a void and thus becomes fragmented, fluid and plural in the postmodern. No one truth exists anymore and the certainty of facts becomes disputed and muddied once more. Thus, postmodernity is about scepticism, deconstruction and questioning rather than offering answers and solutions. This has made it a controversial theory or topic as it offers little in the way of hope for a better world, indeed it is often seen as dystopic. Inherent in many postmodern critiques of current society is a critique of (late) capitalism and consumer or mass culture that pervade every aspect of our lives, whilst others focus on technology and its pervasive intrusion into our daily lives.

Resources for further learning

• Moreton-Robinson, A. 2015. ‘Introduction: white possession and Indigenous sovereignty matters.’ In. Moreton-Robinson, A.  The White Possessive: property, power and Indigenous sovereignty,  pp. xi-xxiv.
• Powers, C. 2009. Sociology as a coherent discipline: unifying themes. In. Powers, C. Making sense of social theory , Chapter 16.
• Watts, L. and Hodgson, D. 2019. ‘Power and knowledge’. In. Watts, L. and Hodgson, D. Social justice theory and practice for social work, Chapter 5.
• Cunneen, C. and Tauri, J. 2016. ‘Towards a critical Indigenous criminology.’ In. Cunneen, C. and Tauri, J. Indigenous criminology, pp. 23-43.
• Kim, C.H. and Choi, Y.B. 2017. How meritocracy is defined today – contemporary aspects of meritocracy. Economics and Sociology, 10(1): 112-121.
• Flyvbjerg, B. 2001. ‘Values in social and political inquiry.’ In. Flyvbjerg, B. Making social science matter, Chapter 5.

Other resources:

• Watego, C. 2021.  ‘Who are the real criminals? Making the case for abolishing criminology.’ (YouTube, 1:35:01),
• Anderson, E. 2017. ‘How good social science can and ought to be value-laden’ (YouTube, 17:00) .
• Zigon, J. and Throop, J. 2021. ‘ Phenomenology ‘ Open Encyclopedia of Anthropology .

Introduction to the Social Sciences Copyright © 2023 by The University of Queensland is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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1 Introduction to Critical Thinking

I. what is c ritical t hinking [1].

Critical thinking is the ability to think clearly and rationally about what to do or what to believe.  It includes the ability to engage in reflective and independent thinking. Someone with critical thinking skills is able to do the following:

• Understand the logical connections between ideas.
• Identify, construct, and evaluate arguments.
• Detect inconsistencies and common mistakes in reasoning.
• Solve problems systematically.
• Identify the relevance and importance of ideas.
• Reflect on the justification of one’s own beliefs and values.

Critical thinking is not simply a matter of accumulating information. A person with a good memory and who knows a lot of facts is not necessarily good at critical thinking. Critical thinkers are able to deduce consequences from what they know, make use of information to solve problems, and to seek relevant sources of information to inform themselves.

Critical thinking should not be confused with being argumentative or being critical of other people. Although critical thinking skills can be used in exposing fallacies and bad reasoning, critical thinking can also play an important role in cooperative reasoning and constructive tasks. Critical thinking can help us acquire knowledge, improve our theories, and strengthen arguments. We can also use critical thinking to enhance work processes and improve social institutions.

Some people believe that critical thinking hinders creativity because critical thinking requires following the rules of logic and rationality, whereas creativity might require breaking those rules. This is a misconception. Critical thinking is quite compatible with thinking “out-of-the-box,” challenging consensus views, and pursuing less popular approaches. If anything, critical thinking is an essential part of creativity because we need critical thinking to evaluate and improve our creative ideas.

II. The I mportance of C ritical T hinking

Critical thinking is a domain-general thinking skill. The ability to think clearly and rationally is important whatever we choose to do. If you work in education, research, finance, management or the legal profession, then critical thinking is obviously important. But critical thinking skills are not restricted to a particular subject area. Being able to think well and solve problems systematically is an asset for any career.

Critical thinking is very important in the new knowledge economy.  The global knowledge economy is driven by information and technology. One has to be able to deal with changes quickly and effectively. The new economy places increasing demands on flexible intellectual skills, and the ability to analyze information and integrate diverse sources of knowledge in solving problems. Good critical thinking promotes such thinking skills, and is very important in the fast-changing workplace.

Critical thinking enhances language and presentation skills. Thinking clearly and systematically can improve the way we express our ideas. In learning how to analyze the logical structure of texts, critical thinking also improves comprehension abilities.

Critical thinking promotes creativity. To come up with a creative solution to a problem involves not just having new ideas. It must also be the case that the new ideas being generated are useful and relevant to the task at hand. Critical thinking plays a crucial role in evaluating new ideas, selecting the best ones and modifying them if necessary.

Critical thinking is crucial for self-reflection. In order to live a meaningful life and to structure our lives accordingly, we need to justify and reflect on our values and decisions. Critical thinking provides the tools for this process of self-evaluation.

Good critical thinking is the foundation of science and democracy. Science requires the critical use of reason in experimentation and theory confirmation. The proper functioning of a liberal democracy requires citizens who can think critically about social issues to inform their judgments about proper governance and to overcome biases and prejudice.

Critical thinking is a   metacognitive skill . What this means is that it is a higher-level cognitive skill that involves thinking about thinking. We have to be aware of the good principles of reasoning, and be reflective about our own reasoning. In addition, we often need to make a conscious effort to improve ourselves, avoid biases, and maintain objectivity. This is notoriously hard to do. We are all able to think but to think well often requires a long period of training. The mastery of critical thinking is similar to the mastery of many other skills. There are three important components: theory, practice, and attitude.

III. Improv ing O ur T hinking S kills

If we want to think correctly, we need to follow the correct rules of reasoning. Knowledge of theory includes knowledge of these rules. These are the basic principles of critical thinking, such as the laws of logic, and the methods of scientific reasoning, etc.

Also, it would be useful to know something about what not to do if we want to reason correctly. This means we should have some basic knowledge of the mistakes that people make. First, this requires some knowledge of typical fallacies. Second, psychologists have discovered persistent biases and limitations in human reasoning. An awareness of these empirical findings will alert us to potential problems.

However, merely knowing the principles that distinguish good and bad reasoning is not enough. We might study in the classroom about how to swim, and learn about the basic theory, such as the fact that one should not breathe underwater. But unless we can apply such theoretical knowledge through constant practice, we might not actually be able to swim.

Similarly, to be good at critical thinking skills it is necessary to internalize the theoretical principles so that we can actually apply them in daily life. There are at least two ways to do this. One is to perform lots of quality exercises. These exercises don’t just include practicing in the classroom or receiving tutorials; they also include engaging in discussions and debates with other people in our daily lives, where the principles of critical thinking can be applied. The second method is to think more deeply about the principles that we have acquired. In the human mind, memory and understanding are acquired through making connections between ideas.

Good critical thinking skills require more than just knowledge and practice. Persistent practice can bring about improvements only if one has the right kind of motivation and attitude. The following attitudes are not uncommon, but they are obstacles to critical thinking:

• I prefer being given the correct answers rather than figuring them out myself.
• I don’t like to think a lot about my decisions as I rely only on gut feelings.
• I don’t usually review the mistakes I have made.
• I don’t like to be criticized.

To improve our thinking we have to recognize the importance of reflecting on the reasons for belief and action. We should also be willing to engage in debate, break old habits, and deal with linguistic complexities and abstract concepts.

The  California Critical Thinking Disposition Inventory  is a psychological test that is used to measure whether people are disposed to think critically. It measures the seven different thinking habits listed below, and it is useful to ask ourselves to what extent they describe the way we think:

• Truth-Seeking—Do you try to understand how things really are? Are you interested in finding out the truth?
• Open-Mindedness—How receptive are you to new ideas, even when you do not intuitively agree with them? Do you give new concepts a fair hearing?
• Analyticity—Do you try to understand the reasons behind things? Do you act impulsively or do you evaluate the pros and cons of your decisions?
• Systematicity—Are you systematic in your thinking? Do you break down a complex problem into parts?
• Confidence in Reasoning—Do you always defer to other people? How confident are you in your own judgment? Do you have reasons for your confidence? Do you have a way to evaluate your own thinking?
• Inquisitiveness—Are you curious about unfamiliar topics and resolving complicated problems? Will you chase down an answer until you find it?
• Maturity of Judgment—Do you jump to conclusions? Do you try to see things from different perspectives? Do you take other people’s experiences into account?

Finally, as mentioned earlier, psychologists have discovered over the years that human reasoning can be easily affected by a variety of cognitive biases. For example, people tend to be over-confident of their abilities and focus too much on evidence that supports their pre-existing opinions. We should be alert to these biases in our attitudes towards our own thinking.

IV. Defining Critical Thinking

There are many different definitions of critical thinking. Here we list some of the well-known ones. You might notice that they all emphasize the importance of clarity and rationality. Here we will look at some well-known definitions in chronological order.

1) Many people trace the importance of critical thinking in education to the early twentieth-century American philosopher John Dewey. But Dewey did not make very extensive use of the term “critical thinking.” Instead, in his book  How We Think (1910), he argued for the importance of what he called “reflective thinking”:

…[when] the ground or basis for a belief is deliberately sought and its adequacy to support the belief examined. This process is called reflective thought; it alone is truly educative in value…

Active, persistent and careful consideration of any belief or supposed form of knowledge in light of the grounds that support it, and the further conclusions to which it tends, constitutes reflective thought.

There is however one passage from How We Think where Dewey explicitly uses the term “critical thinking”:

The essence of critical thinking is suspended judgment; and the essence of this suspense is inquiry to determine the nature of the problem before proceeding to attempts at its solution. This, more than any other thing, transforms mere inference into tested inference, suggested conclusions into proof.

2) The  Watson-Glaser Critical Thinking Appraisal  (1980) is a well-known psychological test of critical thinking ability. The authors of this test define critical thinking as:

…a composite of attitudes, knowledge and skills. This composite includes: (1) attitudes of inquiry that involve an ability to recognize the existence of problems and an acceptance of the general need for evidence in support of what is asserted to be true; (2) knowledge of the nature of valid inferences, abstractions, and generalizations in which the weight or accuracy of different kinds of evidence are logically determined; and (3) skills in employing and applying the above attitudes and knowledge.

3) A very well-known and influential definition of critical thinking comes from philosopher and professor Robert Ennis in his work “A Taxonomy of Critical Thinking Dispositions and Abilities” (1987):

Critical thinking is reasonable reflective thinking that is focused on deciding what to believe or do.

4) The following definition comes from a statement written in 1987 by the philosophers Michael Scriven and Richard Paul for the  National Council for Excellence in Critical Thinking (link), an organization promoting critical thinking in the US:

Critical thinking is the intellectually disciplined process of actively and skillfully conceptualizing, applying, analyzing, synthesizing, and/or evaluating information gathered from, or generated by, observation, experience, reflection, reasoning, or communication, as a guide to belief and action. In its exemplary form, it is based on universal intellectual values that transcend subject matter divisions: clarity, accuracy, precision, consistency, relevance, sound evidence, good reasons, depth, breadth, and fairness. It entails the examination of those structures or elements of thought implicit in all reasoning: purpose, problem, or question-at-issue, assumptions, concepts, empirical grounding; reasoning leading to conclusions, implications and consequences, objections from alternative viewpoints, and frame of reference.

The following excerpt from Peter A. Facione’s “Critical Thinking: A Statement of Expert Consensus for Purposes of Educational Assessment and Instruction” (1990) is quoted from a report written for the American Philosophical Association:

We understand critical thinking to be purposeful, self-regulatory judgment which results in interpretation, analysis, evaluation, and inference, as well as explanation of the evidential, conceptual, methodological, criteriological, or contextual considerations upon which that judgment is based. CT is essential as a tool of inquiry. As such, CT is a liberating force in education and a powerful resource in one’s personal and civic life. While not synonymous with good thinking, CT is a pervasive and self-rectifying human phenomenon. The ideal critical thinker is habitually inquisitive, well-informed, trustful of reason, open-minded, flexible, fairminded in evaluation, honest in facing personal biases, prudent in making judgments, willing to reconsider, clear about issues, orderly in complex matters, diligent in seeking relevant information, reasonable in the selection of criteria, focused in inquiry, and persistent in seeking results which are as precise as the subject and the circumstances of inquiry permit. Thus, educating good critical thinkers means working toward this ideal. It combines developing CT skills with nurturing those dispositions which consistently yield useful insights and which are the basis of a rational and democratic society.

V. Two F eatures of C ritical T hinking

A. how not what .

Critical thinking is concerned not with what you believe, but rather how or why you believe it. Most classes, such as those on biology or chemistry, teach you what to believe about a subject matter. In contrast, critical thinking is not particularly interested in what the world is, in fact, like. Rather, critical thinking will teach you how to form beliefs and how to think. It is interested in the type of reasoning you use when you form your beliefs, and concerns itself with whether you have good reasons to believe what you believe. Therefore, this class isn’t a class on the psychology of reasoning, which brings us to the second important feature of critical thinking.

B. Ought N ot Is ( or Normative N ot Descriptive )

There is a difference between normative and descriptive theories. Descriptive theories, such as those provided by physics, provide a picture of how the world factually behaves and operates. In contrast, normative theories, such as those provided by ethics or political philosophy, provide a picture of how the world should be. Rather than ask question such as why something is the way it is, normative theories ask how something should be. In this course, we will be interested in normative theories that govern our thinking and reasoning. Therefore, we will not be interested in how we actually reason, but rather focus on how we ought to reason.

In the introduction to this course we considered a selection task with cards that must be flipped in order to check the validity of a rule. We noted that many people fail to identify all the cards required to check the rule. This is how people do in fact reason (descriptive). We then noted that you must flip over two cards. This is how people ought to reason (normative).

• Section I-IV are taken from http://philosophy.hku.hk/think/ and are in use under the creative commons license. Some modifications have been made to the original content. ↵

Critical Thinking Copyright © 2019 by Brian Kim is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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2024 Theses Doctoral

The Implementation of Emerging Knowledge in K-12 Schools: The Challenge of Computational Thinking

Azeka, Steven

This dissertation examines the response of a group of educators to a state mandate to integrate computational thinking (CT) into all levels of the curriculum. It explores the historical development of CT and its significance within the broader context of Science, Technology, Engineering, and Mathematics education, emphasizing the rapid growth and evolving nature of this interdisciplinary field. By examining the challenges and potential strategies for incorporating CT into K-12 curricula, the research highlights the critical role of school leadership in navigating the complexities associated with this integration. Utilizing Everett Rogers’s Diffusion of Innovation theory, the dissertation explores how new knowledge is integrated into schools and examines the pivotal role of educational leaders in steering this endeavor. A mixed-methods research design was used to gather the attitudes and perceptions of school leaders toward CT, identifying key factors that influence the adoption and implementation of CT in schools. The study reveals that leadership awareness, involvement, and support are pivotal in overcoming obstacles to CT integration. It also underscores the importance of developing a shared understanding of CT among educators and administrators, aligning CT initiatives with school priorities, and providing adequate resources and professional development opportunities to ensure effective implementation. The findings of the dissertation offer valuable insights for policymakers, educators, and educational leaders, suggesting that a comprehensive approach to integrating CT into K-12 education requires strategic planning, collaboration, and sustained support. By addressing the gaps in current research and practice, this dissertation contributes to the discourse on effective strategies for embedding CT within the educational curriculum, with the goal of enhancing students’ preparedness for an increasingly computational world. This research sheds light on the challenges and opportunities of CT integration and contributes to the development of a roadmap for future efforts to integrate new bodies of knowledge into the K-12 curriculum.

• Problem solving--Study and teaching
• Problem solving--Methodology
• Mathematics--Study and teaching
• Science--Study and teaching
• Education--Curricula

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Toward critical spatial thinking in the social sciences and humanities

• Published: 30 January 2010
• Volume 75 , pages 3–13, ( 2010 )

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• Michael F. Goodchild 1 &
• Donald G. Janelle 1  

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The integration of geographically referenced information into the conceptual frameworks and applied uses of the social sciences and humanities has been an ongoing process over the past few centuries. It has gained momentum in recent decades with advances in technologies for computation and visualization and with the arrival of new data sources. This article begins with an overview of this transition, and argues that the spatial integration of information resources and the cross-disciplinary sharing of analysis and representation methodologies are important forces for the integration of scientific and artistic expression, and that they draw on core concepts in spatial (and spatio-temporal) thinking. We do not suggest that this is akin to prior concepts of unified knowledge systems, but we do maintain that the boundaries to knowledge transfer are disintegrating and that our abilities in problem solving for purposes of artistic expression and scientific development are enhanced through spatial perspectives. Moreover, approaches to education at all levels must recognize the need to impart proficiency in the critical and efficient application of these fundamental spatial concepts, if students and researchers are to make use of expanding access to a broadening range of spatialized information and data processing technologies.

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Goodchild, M.F., Janelle, D.G. Toward critical spatial thinking in the social sciences and humanities. GeoJournal 75 , 3–13 (2010). https://doi.org/10.1007/s10708-010-9340-3

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Toward critical spatial thinking in the social sciences and humanities

The integration of geographically referenced information into the conceptual frameworks and applied uses of the social sciences and humanities has been an ongoing process over the past few centuries. It has gained momentum in recent decades with advances in technologies for computation and visualization and with the arrival of new data sources. This article begins with an overview of this transition, and argues that the spatial integration of information resources and the cross-disciplinary sharing of analysis and representation methodologies are important forces for the integration of scientific and artistic expression, and that they draw on core concepts in spatial (and spatio-temporal) thinking. We do not suggest that this is akin to prior concepts of unified knowledge systems, but we do maintain that the boundaries to knowledge transfer are disintegrating and that our abilities in problem solving for purposes of artistic expression and scientific development are enhanced through spatial perspectives. Moreover, approaches to education at all levels must recognize the need to impart proficiency in the critical and efficient application of these fundamental spatial concepts, if students and researchers are to make use of expanding access to a broadening range of spatialized information and data processing technologies.

Introduction

Why is it that spatial intelligence has not received the same level of interest in education as reading, written communication, and computational reasoning? After all, society has always understood that location and geographical patterns of resource distributions and markets can influence strategic planning in commerce and politics. At a more mundane level, being able to navigate from one place to another is recognized as critical both to the daily survival of individuals at local levels and to the geopolitical fortunes of nations and empires. Perhaps these abilities are instinctive, or acquired at such an early age that they require no attention from our educational system. Or, are society and its approach to education failing to nurture a fundamental element of human intelligence?

What we now take for granted as the known distribution of land and water on the Earth’s surface emerged slowly over centuries as the result of huge investments in navies and expeditions, development of new tools (spatial technologies), compilations of observations, and working from the known through extrapolation and interpolation to approximations of expected distributions. This process has demonstrated a high level of commitment to the development and application of spatial reasoning. But beyond this level of global abstraction lie such topics as the hidden dimensions of human settlement patterns, the correlations that might exist between physical environmental factors and human health, or the prediction of human migratory flows over time. In these settings, the applications of spatial reasoning take a different turn, combining, for example, the need for a census or other detailed geo-referenced assemblies of information about a vast array of phenomena with the recognition that geographical maps and spatial statistics can become descriptive as well as analytic tools, and that they lie at the heart both of scientific investigation and discovery and of creative achievements in the arts and humanities.

Profound accomplishments have emerged through spatial thinking about a large number of applications over the past few centuries. Yet, the systematic development of computational tools for handling spatial data began only in the 1960s, and today GIS (geographic information systems) and software for image processing, pattern recognition, and scientific visualization are in widespread use throughout many disciplines. Functions for the manipulation, analysis, and modeling of spatial data are now available in standard statistical and mathematical packages. The introduction of the Web in the early 1990s helped to make digital images readily sharable, and pictures of the brain, of Earth from space, and of space from Earth are now important tools in the neuro-, Earth, and astronomical sciences, respectively. GPS and GIS have become ubiquitous tools in many career fields and in everyday life. Previously, the complexity of formal GIS made it difficult to use before the high-school level, but now students at the elementary level can access some of its functions through such services as Google Earth.

Spatial turns in the sciences and social sciences

Space appears to have found new theoretical significance in many disciplines in recent years. For example, in ecology, Tilman and Kareiva (1997) observe that, “although the world is unavoidably spatial, and each organism is a discrete entity that exists and interacts only with its immediate neighborhood, these realities have long been ignored by most ecologists because they greatly complicate field research and modeling”. Dealing with such complications has been the subject over the past two decades of research efforts in several disciplines, including statistics, computer science, and geography. Reflecting the fundamental nature of such issues to computer science, the Association for Computing Machinery approved a new Special Interest Group (SIGSPA-TIAL) in 2009 on “issues related to the acquisition, management, and processing of spatially-related information.”

The flow of information from a host of sensors has grown exponentially in recent years to the point where the lead editorial in Nature ( Anonymous 2008 ) urged that all observations in the environmental sciences be georeferenced. The easy availability of GPS enables spatial analysis and modeling, and addresses the issues of importance to the sciences and humanities that are posed by the vast legacy of museum and herbarium artifacts for which the location of collection is poorly recorded (Liu et al. in press). The ability to reason about, and to draw inferences from spatial pattern has been critical in numerous breakthroughs in epidemiology, starting perhaps with Snow’s midnineteenth century work on cholera ( Johnson 2006 ). Scholten et al. (2009) provide a recent overview of how location has achieved central significance in science owing to developments and applications of geospatial technologies.

Coupled with the development of new exploratory tools for mapping and analysis, the momentum of applications in the social sciences has been especially evident, documented as a spatial turn in recent compilations of research by Anselin et al. (2004) on applications of spatial econometrics; and by Goodchild and Janelle (2004) , Scholten et al. (2009) , and Nyerges et al. (in press) on broad-based applications of spatial methodologies in a range of social sciences. Paul Krugman’s 2008 Nobel Prize in Economics, for example, was based in part on his reintroduction of the importance of location in understanding economic activity ( Krugman 1991 ), attesting to the significant potential that spatial understanding adds to traditional approaches to the sciences and social sciences. Contemporary advances in the uses of spatial reasoning in demography and sociology are profiled by Castro (2007) , Voss et al. (2006) ; and Voss (2007) . Cromley and McLafferty (2002) explore applications of GIS in public health research; and the journal Political Analysis released a special issue on spatial methods in political science (Vol 10(3), 2002).

Spatial thinking in the humanities

In the humanities, the Electronic Cultural Atlas Initiative ( http://www.ecai.org ) has for the past decade been a significant agent of dissemination of spatial thinking, bringing together scholars from such disciplines as archaeology, anthropology, history, and religious studies, and from library and museum archives, to map human cultural heritage and to document the role of place in society. Another equally important demonstration in the humanities of the value of spatial perspective is the Spatial History Project at Stanford University (see http://www.stanford.edu/group/spatialhistory ). It brings together scholars at the intersection of geography and history who use GIS in their research, but also focuses on the harvesting of large datasets of maps, images, and texts, and their integration to create dynamic, digital visualizations of change over space and time. The Social Science History Association ( http://www.ssha.org ) also regularly features sessions on spatial perspectives in its conferences.

Highlighting the enhanced recent attention to spatial dimensions of scholarship in the humanities, two important conferences took place in 2009—the Spatial Technologies and Humanities Conference , sponsored and hosted by the Scholarly Communication Institute (SCI) at the University of Virginia in Charlottesville, and the GIS in the Humanities and Social Sciences International Conference 2009 (GISHSS), hosted by the Research Center for Humanities and Social Sciences of Academia Sinica, in Taipei, with support from Queen’s University Belfast, and Indiana University-Purdue University at Indianapolis.

The SCI conference resulted in an online report ( Rumsey 2009 ) that clearly articulates on potential applications of geospatial and mapping technologies, concept mapping, and library technologies to create virtual worlds for scholarly communication in the arts and humanities. The conference brought together scholars and academic leaders from different disciplines, academic libraries, higher education, and information technologies to explore the full life-cycle of scholarly communication, from research and discovery to analysis, presentation, dissemination, and persistent access.

The SCI report addresses the importance of a space–time perspective and argumentation in the interpretations of spatial data, acknowledging that interest in digital forms of space–time patterns has grown as the accessibility of location-aware devices and services has proliferated. Mapping services on the Web, especially Google Earth, have become popular gateways for visualizations of information. Nonetheless, as noted by Jessop (2007) , although information about place and location is an essential part of research in the humanities, sophisticated analytical programs such as GIS remain slow to accommode the specific concerns of the humanities, a theme developed more explicitly with respect to history by Boonstra (2009) . In general, the “… humanities disciplines most influenced by the linguistic and visual turns in scholarship over the past few decades have not given priority to critical spatial reasoning” ( Rumsey 2009 , p. 4), possibly reflecting the importance attached to both the real and the imaginary in human culture, to concern for the qualitative attributes of place, and to representations of nonwestern perspectives on space and time. Interestingly, geographers at the SCI conference noted a convergence of interest with the humanities, both in examining how subjective and qualitative elements of spatial patterns are represented within current GIS applications, and in recognizing the need for ways to reflect uncertainty and ambiguity.

The GISHSS conference was similarly focused on the integration of cultural and social nuance in the humanities and social sciences with GIS and other spatial analytic frameworks. Harris (2009) proposed a general conceptualization of the role of GIS within spatial humanities, while Gregory (2009) described how both quantitative and qualitative approaches might be integrated within GIS for interpreting literature and news reports from prior eras. Ayers (2009) illustrated the potential of dynamic mapping for interpreting historical changes, and Fotheringham (2009) demonstrated how advanced spatial-analytic methods contribute to the understanding of population dynamics. Janelle (2009) explored trends and ambiguities in the space–time documentation of human behavior and social organization. The importance of spatial thinking in the social sciences was addressed by Phoenix (2009) , while Goodchild (2009) made the case that critical spatial thinking should be a central theme in education for a world where information is increasingly seen through geographical filters, is broadly accessibly to the general population, and is both generated and disseminated voluntarily through digital media.

Spatially integrative knowledge systems

Although disciplines have demonstrated and continue to play a critical role in the advancement of scientific and humanistic understanding, the SCI and GISHSS conferences may represent the seeds of a fundamental shift from disciplinary to integrative knowledge systems. Examples of integrative knowledge systems arise from the application of concepts of space and place, and space and time , having near universal relevance to scholarship in diverse knowledge domains, with the focus here on their meaning in the humanities and social sciences.

Integration through concepts of space and place

Throughout our discussion we have seen instances of how space and place are important elements in social science and humanities interpretations of human well-being and changing environments. But a recently announced White House place-based initiative ( Orszag et al. 2009 ) ups attention to the importance of place understanding in formulating sound policy development and plan implementation. The initiative advocates place-based policies to leverage “… investments by focusing resources in targeted places and drawing on the compounding effect of well-coordinated action…” to influence the development of rural and metropolitan areas and their “… function as places to live, work, operate a business, preserve heritage, and more.”

Cummins et al. (2007) provide a case for place-based policy formulation in health research and health-policy interventions. Arguing that conventional approaches underestimate the contribution of place to disease risk, they call for relational views to help identify reciprocal relationships between people and place. Matthews (2008) reinforces this view, documenting how neighborhood context is an important conditioner of human well-being. Indeed, place has emerged as an important contextual frame-work for considering a number of critical societal issues, noted for example by Janelle and Hodge (2000) in Information, Place, and Cyberspace: Issues in Accessibility or in the recent Place, Health, and Equity Conference hosted by the University of Washington ( http://courses.washington.edu/phequity/Equity_annoucement_combo.pdf ). Whereas the former drew attention to fundamental linkages between virtual and geographical processes, the latter was grounded in “… place as a social context that is deeply connected to larger patterns of social advantage and disadvantage [and that] calls for multifaceted conceptions of place as well as methods that can flexibly encompass geographic location, material form, the meaning-making of diverse groups, and the dynamics of rapidly changing rural and urban environments.”

Representation and search

There are, of course, other aspects of space and place that link to the research practices of scholars in the social sciences and humanities. For example, owing to the multidimensional nature of spatial data, there are technical issues regarding search for digital place-based information. The Alexandria Digital Library (ADL), developed at the University of California Santa Barbara (UCSB), was one of the first remotely accessible libraries to support indexing and search across massive repositories of spatial data. The ADL contributed to the development of indexing structures for large-scale retrieval and to the development of privacy-secure methods for querying public spatial data. Further advances in the science of search will entail resolving problems in managing large volumes of data, tracking data provenance (a key issue when spatial data are shared and processed across scientific communities), understanding the semantics of spatial data, and designing methods for achieving interoperability across diverse information communities ( Goodchild et al. 1999 ).

Volunteered geographic information

From the perspective of the humanities and social sciences, the Web itself is seen as searchable repository of potential data sources, some of a traditional nature (e.g., images, works of art, literature, speeches, maps, census data, or newspapers), and others of a more informal nature, such as the user-generated content found on social network sites. Today there are opportunities for anyone to contribute resources (pictures, blogs, etc.) that are automatically geo-referenced by latitude and longitude and available to be retrieved by others. Goodchild (2007) has termed the result volunteered geographic information (VGI). Current research on VGI is featured in a special GeoJournal issue ( Elwood 2008 ), which explored questions surrounding the uses of such repositories as keys to social process, environmental understanding, and place-based knowledge. This followed a late 2007 research workshop that brought together researchers from industry, government, and academia to grapple with the complexities of acquisition, validation, distribution, display, and analysis of VGI data sources, calling attention to both the opportunities and the challenges that confront scholars in their use ( http://ncgia.ucsb.edu/projects/vgi/ ).

Spatialization and visualization

Spatialization refers to the construction of abstract spaces of knowledge that can aid in visualization, pattern detection, and the accumulation of scientific insight ( Skupin and Fabrikant, 2003 ). Thus, things that are not explicitly spatial (e.g., social and kinship networks) may be rendered graphically for spatial visualization. At the 2009 SCI conference, participants recognized that “… there is an unexplored universe of spatial information implicit in existing sources, both digital and analog. When ‘liberated’ from a static analog medium and made legible to geospatial technologies, a whole new reservoir of information will be available to nourish new fields of inquiry” (p. 3). For example, historians and literary scholars might explore the locational and spatial information embedded in nineteenth century novels, railroad timetables, sound media, or old maps. Another important aspect of this is the issue of respatialization , defined as the transformation of spatially referenced data from their original geographic representation to an alternative geographic framework ( Goodchild et al. 1993 ). For example, data gathered for politically defined units such as counties, states or provinces, or nations are typically based on a particular spatial representation of the political units. This representation is often not suitable for simple integration with data collected using different underlying geographies (e.g., administratively defined regions, watersheds, swaths, or pixels). Political and administrative representations may also change over time as boundaries change, units split or merge, or data-gathering organizations change their techniques. If not appropriately taken into account, such changes can seriously affect the continuity and quality of time-series data. Because respatialization requires a spatial model, it is an instance of model-based integration to distinguish it from more general semantic integration based on an ontology. Respatialization is yet another dimension to the representation of space and place that requires research and algorithm development to define, for example, relationships between placenames and coordinates, or to address the shifting reporting zones of censuses. In the case of CIESIN’s Gridded Population of the World (GPW; Tobler et al. 1997 ), respatialization transforms data collected for national and subnational administrative units into population totals and densities on a grid of spherical quadrilaterals, essentially a set of pixels defined by lines of latitude and longitude ( Tobler et al. 1997 ), allowing researchers to integrate GPW with other gridded datasets (e.g., remote sensing data), to reaggregate GPW to alternative spatial units (e.g., watersheds, biomes, or metropolitan regions), and to weight other variables by population characteristics.

Integration through concepts of space and time

While the term spatial tends to dominate in the literature, the processes that modify systems are dynamic, and should more correctly be described as spatio-temporal. Moreover, the issues raised by data embedded in space are similar to those encountered in data embedded in time. In this context, spatial is used as an umbrella term to include spatio-temporal, as well as geospatial and geographic when the relevant space is the surface and near-surface of the Earth.

In recent years GPS, video, and other technologies have created a potential wealth of information about the spatial dynamics of movement by individuals, animals, vehicles, and other objects through various spaces. Developing theory and associated analytic techniques has proven more problematic, however, as it has for spatial data more generally. The problems of extracting useful information from networks of video cameras in human-built environments need to be resolved before we can understand how buildings and other structures constrain and channel human spatial behavior or before we can build models to predict the behavior of crowds and to improve urban designs.

Other aspects of spatial dynamics call for integrative space–time perspectives ( Hornsby and Yuan 2008 ; Lin and Batty 2009 ), including the diffusion of ideas and innovations, and the geographical spread of peoples and cultures. In the humanities, the focus turns to relativistic as opposed to absolute spaces, and to the representation of spaces as they might have been in historical times or under the influence of cultures with different world views. Three-dimensional animations of movements through space can enhance understanding of architecture on human behavior or of archaeological structures and their human uses in ancient times. Virtual models, visualizations, and moving objects provide a rich approach to sensing changing forms and roles of landscapes through time. However, there are issues that require extended research if we are to validate the value of investments in multidimensional and dynamic visualizations and models.

Multidimensional visualizations provide spatial “eye candy,” but we know far too little about the processes by which humans extract meaning and learn from such visualizations and from spatial data more generally, and about ways to improve those processes. Attention is needed to discover research-based principles for how to design multimedia material (i.e., the science of instruction) and to formulate a research-based theory of how people learn from words and pictures (i.e., the science of learning concerned with the nature of spatial thinking in complex cognitive activities such as comprehension, reasoning, and problem solving). Research is needed to determine how people learn about spaces through direct experiences, maps, and other visualizations, and about how individual and group differences impact spatial thinking and spatial abilities more generally. The work of the Spatial Intelligence and Learning Center (SILC), a multi-campus and multi-disciplinary research team from Temple University, Northwestern University, the University of Pennsylvania, the University of Chicago, and Chicago Public Schools, is especially important in documenting the learning outcomes from different ways of visualizing spatial information (see http://spatiallearning.org/ ). SILC is funded by the U.S. National Science Foundation as one of six Science of Learning Centers.

Moving beyond spatially intuitive thinking

Students are familiar with virtual spaces and the power of imaging through video games and digital movies. Whereas such experience may add to one’s spatial skill set, it does not obviate a need for formal exposure and in-depth understanding. Yet, although one would insist that a student learn something of statistical theory before using statistical software, the same is not true of spatial software—in the spatial arena, the development of relevant theory and concepts has lagged far behind, and it is clear that a wide gap exists between the power and accessibility of tools on the one hand and the ability of researchers, students, and the general public to make effective use of them on the other. Examples abound.

To give one simple case, the NRC (2006) report on Learning to Think Spatially documents a 2003 article in The Economist (5/3/2003) in which mapping software was used to create a colorful illustration for a news story about the North Korean missile threat. Different missile ranges were depicted as concentric circles on a Mercator projection, despite that projection’s severe distortions at high latitudes (on the Mercator projection the Poles are at infinity). When the map was corrected in a subsequent issue (5/17/2003), the 10,000-km missiles that according to the first map could barely reach the western Aleutians were shown to reach of the North Pole and to have Minneapolis comfortably in range. The distortions introduced by flattening curved spaces are but one of a host of spatial concepts that affect the use of these powerful technologies, and must be part of the training of researchers and educators across the full range of disciplines, something that is rarely seen in traditional approaches to curricula.

The growing body of literature on spatial concepts, in disciplines as diverse as cognitive psychology, mathematics, geography, and philosophy, identifies and enumerates basic elements of a spatial perspective. Some of these concepts, such as distance and containment, are acquired informally in early childhood, whereas others are encountered or formalized much later, or remain problematic even to graduate students. Several researchers have published lists of such concepts. Gersmehl (2005) , for example, lists 13 spatial concepts as fundamental to a geographical perspective, while Newcombe and Huttenlocher (2000) list 11 spatial concepts as fundamental to their work at SILC on the development of spatial cognition. Mitchell ( 1999 , 2005 ) and de Smith et al. (2009) organized their introductions to the analysis of spatial data using GIS around spatial concepts. On the other hand, the actual design of GIS user interfaces continues to be driven more by legacy and implementation than by any fundamental conceptual organization—which may explain why much GIS software has a reputation for being difficult to use.

At the Center for Spatial Studies, University of California, Santa Barbara, we have developed and published online a basic ontology of spatial concepts ( http://www.teachspatial.org ), linking each entry to its original sources. We have scanned the literature of many disciplines from geography and psychology to architecture in this effort, and have to date documented 186 such concepts. We have developed several organizing schemata to give structure to the collection, including hierarchical relationships (some concepts are subsets of others), semantic similarity (some concepts have different names in different disciplines), and formality (some concepts are formalizations of other intuitive concepts). As we continue to develop the site, we plan to include instructional materials focused on advanced concepts for critical spatial thinking. We use the word critical in the sense of reflective, skeptical, or analytic, implying that the successful application of spatial perspectives can never be rote, but must always involve the mind of the researcher in an active questioning and examination of assumptions, techniques, and data if it is to meet the rigorous standards of good scholarship.

One way to define critical spatial thinking is in relation to the use of spatial tools and data—as the mental processes that accompany the use of these technologies. Critical spatial thinking is in sharp contrast to rote button-pushing, and implies that the processes of data manipulation, analysis, data mining, and modeling provoke and require critical thinking, about such comparatively profound issues as scale, accuracy, uncertainty, ontology, representation, complexity, projection, and ethics. We see spatial technologies as an essential, integrating element that cuts across disciplines through common language and concepts.

The remainder of this section discusses a selection of these spatial concepts, focusing on three that are of an advanced nature, and are typically acquired during senior undergraduate or graduate education, if ever. We discuss them here as examples of the concepts that are needed to underpin the critical spatial thinking skills that we might expect of spatially aware scholars.

Anselin (1989) identified two properties as particularly important in the analysis of spatial data, but likely to cause conceptual difficulty even at the graduate level. Spatial heterogeneity refers to the tendency for phenomena distributed in many spaces, notably the space of the Earth’s surface, to be statistically non-stationary. Spatial heterogeneity confounds attempts to generalize from spatial samples, because results of an analysis of a limited area will change when the boundaries of the area are shifted. Instead specially adapted methods have been developed in recent years that are place-based and local, yielding results such as model parameter values that vary spatially ( Anselin 1995 ; Fotheringham et al. 2002 ). These techniques represent a radical rethinking of the traditional nomothetic demand of science that gives greatest significance to results that are true everywhere, at all times. Anselin’s second concept, spatial dependence, refers to the tendency for spatial data to exhibit short-run spatial autocorrelation, a property that forms the basis of the fields of geostatistics and spatial statistics ( Cressie 1993 ; Haining 2003 ). Unless it is addressed explicitly, spatial dependence can lead to artificially inflated degrees of freedom and the enhanced possibility of Type I statistical errors (rejection of the null hypothesis when it is true). These concepts are also well recognized in the analysis of time series.

Although the origins of statistical theory lie in the controlled experiments of pioneers such as R. A. Fisher, disciplines across the social and environmental sciences frequently deal with data gathered from experiments that are natural, relying on data over which the investigator has little or no control. Because these sciences frequently deal with data that are framed in space and time, they encounter the issues associated with spatial dependence and spatial heterogeneity. Yet students in these disciplines learn essentially the same introductory perspective on statistical theory as those in experimental psychology, and little attention is given to the special properties of spatial data. The concepts addressed by Anselin’s two properties ensure that an analysis of social data from the census tracts of a city, or ecological data from field plots, will be unlikely to justify the traditional assumptions of random and independent sampling from some real or imagined population.

One of the most problematic spatial concepts is scale, in both of its dual meanings of extent and resolution. Dependence of results on extent, as well as the difficulties of generalization from any limited area, have already been addressed in the context of spatial heterogeneity. Resolution addresses the impossibility of a perfect representation of the infinite complexity of many spatially distributed phenomena, and the consequent necessity for generalization, approximation, sampling, or other mechanisms to remove detail. Scale issues tend to be compounded by the spatial resolution of acquisition systems, which may have little to do with the spatial resolution needed for accurate analysis and modeling, or for effective decision making (for discussions of the common issues of scale across diverse contexts see, for example, Levin 1992 ; Quattrochi and Goodchild 1997 ; Tate and Atkinson 2001 ; Mandelbrot 1982 ). In spatial analysis in the social sciences, scale and related issues are recognized in the form of the ecological fallacy ( King 1997 ; Robinson 1950 ) and the modifiable areal unit problem ( Openshaw 1983 ). We consider it essential that scale-related issues be part of the critical frameworks of researchers in any discipline working with spatially aggregated data.

The education challenge

We find that students are inadequately trained in the challenges of working with phenomena embedded in space and time, and that there is a need to engage them both in research on advancing the theory and technique of critical spatial thinking, and in applying critical thinking to research in a range of disciplines if they are to develop as leaders of a spatially enabled scholarship that is better prepared to use the evolving technologies, and better equipped to exploit the growing flood of spatially referenced data.

The problems of statistical inference from spatial data provide an example of the need for critical spatial thinking. Issues abound in the use of directional statistics, and in directional anisotropy in spatial covariances. Critical spatial thinkers understand the assumptions underlying spatial data and the effects of scale and non-stationarity on research outcomes. They appreciate the difficulties of inference from multidimensional data when they are subject to dimensionality reduction and the problems and implications of uncertainty in spatial data that might leave their users uncertain about the true nature of the world they represent. In addition, critical spatial thinkers can use geostatistical theory to provide a more rigorous basis for interpolation in spatiotemporal data.

Reference has already been made to a general lack of preparation in critical spatial thinking in our education system. Although spatial tasks such as block manipulation are common features of intelligence tests, it is rare to find students being prepared for them in any systematic way. One can speculate about the reasons for this. Perhaps spatial thinking is regarded as innate, an unmalleable skill possessed by some and not others (there are documented links between some spatial skills and gender, for example; Voyer et al. 1995 ); or perhaps spatial skills are regarded as trivial, acquired in early childhood, and in no way comparable to mathematical, logical, or verbal skills.

A recent report of the National Research Council ( NRC 2006 ) defined spatial thinking as “a cognitive skill that can be used in everyday life, the workplace, and science to structure problems, find answers, and express solutions using the properties of space. It can be learned and taught formally to students using appropriately designed tools, technologies, and curricula.” The report documented the lack of attention to spatial thinking in formal curricula, despite assertions that it is a primary form of intelligence ( Eliot 1987 ; Gardner 1983 ), and called for “a national initiative to integrate spatial thinking into existing standards-based instruction across the school curriculum, such as in mathematics, history, and science classes… to create a generation of students who learn to think spatially in an informed way.” The report viewed spatial thinking as “an amalgam of three elements: concepts of space, tools of representation, and processes of reasoning” (p. 12). Although focused on K-12 education, the report includes a series of rich and compelling examples of the application of spatial perspectives whose applicability extends across disciplines and all levels of education. A recent article in Science ( Holden 2009 ) quotes David Lubinski of Vanderbilt University from a presentation to a National Science Board workshop on innovation: “(D)espite their importance in science, particularly in fields such as engineering, robotics, or astronomy, spatial abilities are getting short shrift both in school curricula and in programs trying to spot precocious youths.”

Dating back to the work of Smith (1964) , findings continue to show that individuals who are more spatially adept have greater success in higher-level problem solving (Kozhevnikov et al. 1999 , 2002 , 2005 ). SILC has assembled evidence that spatial intelligence can be enhanced, and that heightened spatial abilities among adolescents can be a predictor of future science career paths ( Shea et al. 2001 ). Legé (1999) has argued that lack of spatial awareness and skills hinders students’ ability to perform many tasks that are essential in the science and engineering disciplines, while Wheatley (1997) argues that advancing teachers’ knowledge of the efficacy of such spatial skills as visualization can help students to become better solvers of math problems.

We live in a global academic world that is dominated by the need to solve complex problems that are embedded in space and time, and to bring spatial perspectives to scholarship. This twenty-first century world is collaborative, enabled by cyber infrastructure, and is highly interdisciplinary. It is evident that students should be trained to the standards of a critical spatial thinker, including:

• the potential to contribute critical spatial understanding to research at the interface between disciplines;
• the ability to work in a team;
• the ability to explain the space–time context of research to non-experts;
• the ability to develop new and highly original spatially informed research ideas;
• the experience to enable sustained and successful research dialog within an international community of spatially aware scientists;
• the ability to disseminate spatial understanding of research through teaching and curriculum development at K-12 and undergraduate levels; and
• the ability to transfer spatial technologies and spatial concepts for research across different knowledge domains and problem sets.

Achieving these goals will require a combination of conventional course-based curriculum, intensive peer-to-peer interaction, project-based learning, and engagement with activities across the educational spectrum (in the community and region, on campuses, nationally, and internationally).

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• Published: 24 May 2024

Beyond probability-impact matrices in project risk management: A quantitative methodology for risk prioritisation

• F. Acebes   ORCID: orcid.org/0000-0002-4525-2610 1 ,
• J. M. González-Varona 2 ,
• A. López-Paredes 2 &
• J. Pajares 1  

Humanities and Social Sciences Communications volume  11 , Article number:  670 ( 2024 ) Cite this article

Metrics details

• Business and management

The project managers who deal with risk management are often faced with the difficult task of determining the relative importance of the various sources of risk that affect the project. This prioritisation is crucial to direct management efforts to ensure higher project profitability. Risk matrices are widely recognised tools by academics and practitioners in various sectors to assess and rank risks according to their likelihood of occurrence and impact on project objectives. However, the existing literature highlights several limitations to use the risk matrix. In response to the weaknesses of its use, this paper proposes a novel approach for prioritising project risks. Monte Carlo Simulation (MCS) is used to perform a quantitative prioritisation of risks with the simulation software MCSimulRisk. Together with the definition of project activities, the simulation includes the identified risks by modelling their probability and impact on cost and duration. With this novel methodology, a quantitative assessment of the impact of each risk is provided, as measured by the effect that it would have on project duration and its total cost. This allows the differentiation of critical risks according to their impact on project duration, which may differ if cost is taken as a priority objective. This proposal is interesting for project managers because they will, on the one hand, know the absolute impact of each risk on their project duration and cost objectives and, on the other hand, be able to discriminate the impacts of each risk independently on the duration objective and the cost objective.

Introduction

The European Commission ( 2023 ) defines a project as a temporary organizational structure designed to produce a unique product or service according to specified constraints, such as time, cost, and quality. As projects are inherently complex, they involve risks that must be effectively managed (Naderpour et al. 2019 ). However, achieving project objectives can be challenging due to unexpected developments, which often disrupt plans and budgets during project execution and lead to significant additional costs. The Standish Group ( 2022 ) notes that managing project uncertainty is of paramount importance, which renders risk management an indispensable discipline. Its primary goal is to identify a project’s risk profile and communicate it by enabling informed decision making to mitigate the impact of risks on project objectives, including budget and schedule adherence (Creemers et al. 2014 ).

Several methodologies and standards include a specific project risk management process (Axelos, 2023 ; European Commission, 2023 ; Project Management Institute, 2017 ; International Project Management Association, 2015 ; Simon et al. 1997 ), and there are even specific standards and guidelines for it (Project Management Institute, 2019 , 2009 ; International Organization for Standardization, 2018 ). Despite the differences in naming each phase or process that forms part of the risk management process, they all integrate risk identification, risk assessment, planning a response to the risk, and implementing this response. Apart from all this, a risk monitoring and control process is included. The “Risk Assessment” process comprises, in turn, risk assessments by qualitative methods and quantitative risk assessments.

A prevalent issue in managing project risks is identifying the significance of different sources of risks to direct future risk management actions and to sustain the project’s cost-effectiveness. For many managers busy with problems all over the place, one of the most challenging tasks is to decide which issues to work on first (Ward, 1999 ) or, in other words, which risks need to be paid more attention to avoid deviations from project objectives.

Given the many sources of risk and the impossibility of comprehensively addressing them, it is natural to prioritise identified risks. This process can be challenging because determining in advance which ones are the most significant factors, and how many risks merit detailed monitoring on an individual basis, can be complicated. Any approach that facilitates this prioritisation task, especially if it is simple, will be welcomed by those willing to use it (Ward, 1999 ).

Risk matrices emerge as established familiar tools for assessing and ranking risks in many fields and industry sectors (Krisper, 2021 ; Qazi et al. 2021 ; Qazi and Simsekler, 2021 ; Monat and Doremus, 2020 ; Li et al. 2018 ). They are now so commonplace that everyone accepts and uses them without questioning them, along with their advantages and disadvantages. Risk matrices use the likelihood and potential impact of risks to inform decision making about prioritising identified risks (Proto et al. 2023 ). The methods that use the risk matrix confer higher priority to those risks in which the product of their likelihood and impact is the highest.

However, the probability-impact matrix has severe limitations (Goerlandt and Reniers, 2016 ; Duijm, 2015 ; Vatanpour et al. 2015 ; Ball and Watt, 2013 ; Levine, 2012 ; Cox, 2008 ; Cox et al. 2005 ). The main criticism levelled at this methodology is its failure to consider the complex interrelations between various risks and use precise estimates for probability and impact levels. Since then, increasingly more academics and practitioners are reluctant to resort to risk matrices (Qazi et al. 2021 ).

Motivated by the drawbacks of using risk matrices or probability-impact matrices, the following research question arises: Is it possible to find a methodology for project risk prioritisation that overcomes the limitations of the current probability-impact matrix?

To answer this question, this paper proposes a methodology based on Monte Carlo Simulation that avoids using the probability-impact matrix and allows us to prioritise project risks by evaluating them quantitatively, and by assessing the impact of risks on project duration and the cost objectives. With the help of the ‘MCSimulRisk’ simulation software (Acebes et al. 2024 ; Acebes et al. 2023 ), this paper determines the impact of each risk on project duration objectives (quantified in time units) and cost objectives (quantified in monetary units). In this way, with the impact of all the risks, it is possible to establish their prioritisation based on their absolute (and not relative) importance for project objectives. The methodology allows quantified results to be obtained for each risk by differentiating between the project duration objective and its cost objective.

With this methodology, it also confers the ‘Risk Assessment’ process cohesion and meaning. This process forms part of the general Risk Management process and is divided into two subprocesses: qualitative and quantitative risk analyses (Project Management Institute, 2017 ). Although Monte Carlo simulation is widely used in project risk assessments (Tong et al. 2018 ; Taroun, 2014 ), as far as we know, the literature still does not contain references that use the data obtained in a qualitative analysis (data related to the probability and impact of each identified risk) to perform a quantitative risk analysis integrated into the project model. Only one research line by A. Qazi (Qazi et al. 2021 ; Qazi and Dikmen, 2021 ; Qazi and Simsekler, 2021 ) appears, where the authors propose a risk indicator with which they determine the level of each identified risk that concerns the established threshold. Similarly, Krisper ( 2021 ) applies the qualitative data of risk factors to construct probability functions, but once again falls in the error of calculating the expected value of the risk for risk prioritisation. In contrast, the novelty proposed in this study incorporates into the project simulation model all the identified risks characterised by their probability and impact values, as well as the set of activities making up the project.

In summary, instead of the traditional risk prioritisation method to qualitatively estimate risk probabilities and impacts, we model probabilities and impacts (duration and cost) at the activity level as distribution functions. When comparing both methods (traditional vs. our proposal), the risk prioritisation results are entirely different and lead to a distinct ranking.

From this point, and to achieve our purpose, the article comes as follows. Literature review summarises the relevant literature related to the research. Methodology describes the suggested methodology. Case study presents the case study used to show how to apply the presented method before discussing the obtained results. Finally, Conclusions draws conclusions about the proposed methodology and identifies the research future lines that can be developed from it.

Literature review

This section presents the literature review on risk management processes and probability-impact matrices to explain where this study fits into existing research. This review allows us to establish the context where our proposal lies in integrated risk management processes. Furthermore, it is necessary to understand the reasons for seeking alternatives to the usual well-known risk matrices.

Risk management methodologies and standards

It is interesting to start with the definition of ‘Risk’ because it is a term that is not universally agreed on, even by different standards and norms. Thus, for example, the International Organization for Standardization ( 2018 ) defines it as “the effect of uncertainty on objectives”, while the Project Management Institute ( 2021 ) defines it as “an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives”. This paper adopts the definition of risk proposed by Hillson ( 2014 ), who uses a particular concept: “risk is uncertainty that matters”. It matters because it affects project objectives and only the uncertainties that impact the project are considered a ‘risk’.

Other authors (Elms, 2004 ; Frank, 1999 ) identify two uncertainty categories: aleatoric, characterised by variability and the presence of a wide range of possible values; epistemic, which arises due to ambiguity or lack of complete knowledge. Hillson ( 2014 ) classifies uncertainties into four distinct types: aleatoric, due to the reliability of activities; stochastic, recognised as a risk event or a possible future event; epistemic, also due to ambiguity; ontological, that which we do not know (black swan). Except for ontological uncertainty, which cannot be modelled due to absolute ignorance of risk, the other identified uncertainties are incorporated into our project model. For this purpose, the probability and impact of each uncertainty are modelled as distribution functions to be incorporated into Monte Carlo simulation.

A risk management process involves analysing the opportunities and threats that can impact project objectives, followed by planning appropriate actions for each one. This process aims to maximise the likelihood of opportunities occurring and to minimise the likelihood of identified threats materialising.

Although it is true that different authors have proposed their particular way of understanding project risk management (Kerzner, 2022 ; Hillson and Simon, 2020 ; Chapman and Ward, 2003 ; Chapman, 1997 ), we wish to look at the principal methodologies, norms and standards in project management used by academics and practitioners to observe how they deal with risk (Axelos, 2023 ; European Commission, 2023 ; International Organization for Standardization, 2018 ; Project Management Institute, 2017 ; International Project Management Association, 2015 ) (Table 1 ).

Table 1 shows the main subprocesses making up the overall risk management process from the point of view of each different approach. All the aforementioned approaches contain a subprocess related to risk assessment. Some of these approaches develop the subprocess by dividing it into two parts: qualitative assessment and quantitative assessment. Individual project risks are ranked for further analyses or action with a qualitative assessment by evaluating the probability of their occurrence and potential impact. A quantitative assessment involves performing a numerical analysis of the joint effect of the identified individual risks and additional sources of uncertainty on the overall project objectives (Project Management Institute, 2017 ). In turn, all these approaches propose the probability-impact or risk matrix as a technique or tool for prioritising project risks.

Within this framework, a ranking of risks by a quantitative approach applies as opposed to the qualitative assessment provided by the risk matrix. To do so, we use estimates of the probability and impact associated with each identified risk. The project model includes these estimates to determine the absolute value of the impact of each risk on time and cost objectives.

Probability-impact matrix

The risk matrix, or probability-impact matrix, is a tool included in the qualitative analysis for risk management and used to analyse, visualise and prioritise risks to make decisions on the resources to be employed to combat them (Goerlandt and Reniers, 2016 ; Duijm, 2015 ). Its well-established use appears in different sectors, ranging from the construction industry (Qazi et al. 2021 ), oil and gas industries (Thomas et al. 2014 ), to the healthcare sector (Lemmens et al. 2022 ), engineering projects (Koulinas et al. 2021 ) and, of course, project management (International Organization for Standardization, 2019 ; Li et al. 2018 ).

In a table, the risk matrix represents the probability (usually on the vertical axis of the table) and impact (usually on the horizontal axis) categories (Ale et al. 2015 ). These axes are further divided into different levels so that risk matrices of 3×3 levels are found with three levels set for probability and three others to define impact, 5 × 5, or even more levels (Duijm, 2015 ; Levine, 2012 ; Cox, 2008 ). The matrix classifies risks into different risk categories, normally labelled with qualitative indicators of severity (often colours like “Red”, “Yellow” and “Green”). This classification combines each likelihood level with every impact level in the matrix (see an example of a probability-impact matrix in Fig. 1 ).

Probability – impact matrix. An example of use.

There are three different risk matrix typologies based on the categorisation of likelihood and impact: qualitative, semiquantitative, and quantitative. Qualitative risk matrices provide descriptive assessments of probability and consequence by establishing categories as “low,” “medium” or “high” (based on the matrix’s specific number of levels). In contrast, semiquantitative risk matrices represent the input categories by ascending scores, such as 1, 2, or 3 (in a 3×3 risk matrix), where higher scores indicate a stronger impact or more likelihood. Finally, in quantitative risk matrices, each category receives an assignment of numerical intervals corresponding to probability or impact estimates. For example, the “Low” probability level is associated with a probability interval [0.1 0.3] (Li et al. 2018 ).

Qualitative matrices classify risks according to their potential hazard, depending on where they fit into the matrix. The risk level is defined by the “colour” of the corresponding cell (in turn, this depends on the probability and impact level), with risks classified with “red” being the most important and the priority ones to pay attention to, but without distinguishing any risks in the different cells of the same colour. In contrast, quantitative risk matrices allow to classify risks according to their risk level (red, yellow, or green) and to prioritise each risk in the same colour by indicating which is the most important. Each cell is assigned a colour and a numerical value, and the product of the value is usually assigned to the probability level and the value assigned to the impact level (Risk = probability × impact).

Risk matrix use is frequent, partly due to its simple application and easy construction compared to alternative risk assessment methods (Levine, 2012 ). Risk matrices offer a well-defined structure for carrying out a methodical risk assessment, provide a practical justification for ranking and prioritising risks, visually and attractively inform stakeholders, among other reasons (Talbot, 2014 ; Ball and Watt, 2013 ).

However, many authors identify problems in using risk matrices (Monat and Doremus, 2020 ; Peace, 2017 ; Levine, 2012 ; Ni et al. 2010 ; Cox, 2008 ; Cox et al. 2005 ), and even the International Organization for Standardization ( 2019 ) indicates some drawbacks. The most critical problems identified in using risk matrices for strategic decision-making are that risk matrices can be inaccurate when comparing risks and they sometimes assign similar ratings to risks with significant quantitative differences. In addition, there is the risk of giving excessively high qualitative ratings to risks that are less serious from a quantitative perspective. This can lead to suboptimal decisions, especially when threats have negative correlations in frequency and severity terms. Such lack of precision can result in inefficient resource allocation because they cannot be based solely on the categories provided by risk matrices. Furthermore, the categorisation of the severity of consequences is subjective in uncertainty situations, and the assessment of probability, impact and risk ratings very much depends on subjective interpretations, which can lead to discrepancies between different users when assessing the same quantitative risks.

Given this background, several authors propose solutions to the posed problems. Goerlandt and Reniers ( 2016 ) review previous works that have attempted to respond to the problems identified with risk matrices. For example, Markowski and Mannan ( 2008 ) suggest using fuzzy sets to consider imprecision in describing ordinal linguistic scales. Subsequently, Ni et al. ( 2010 ) propose a methodology that employs probability and consequence ranks as independent score measures. Levine ( 2012 ) puts forward the use of logarithmic scales on probability and impact axes. Menge et al. (2018) recommend utilising untransformed values as scale labels due to experts’ misunderstanding of logarithmic scales. Ruan et al. ( 2015 ) suggest an approach that considers decision makers’ risk aversion by applying the utility theory.

Other authors, such as Duijm ( 2015 ), propose a continuous probability consequence diagram as an alternative to the risk matrix, and employing continuous scales instead of categories. They also propose utilising more comprehensive colour ranges in risk matrices whenever necessary to prioritise risks and to not simply accept them. In contrast, Monat and Doremus ( 2020 ) put forward a new risk prioritisation tool. Alternatively, Sutherland et al. ( 2022 ) suggest changing matrix size by accommodating cells’ size to the risk’s importance. Even Proto et al. ( 2023 ) recommend avoiding colour in risk matrices so that the provided information is unbiased due to the bias that arises when using coloured matrices.

By bearing in mind the difficulties presented by the results offered by risk matrices, we propose a quantitative method for risk prioritisation. We use qualitative risk analysis data by maintaining the estimate of the probability of each risk occurring and its potential impact. Nevertheless, instead of entering these data into the risk matrix, our project model contains them for Monte Carlo simulation. As a result, we obtain a quantified prioritisation of each risk that differentiates the importance of each risk according to the impact on cost and duration objectives.

Methodology

Figure 2 depicts the proposed method for prioritising project risks using quantitative techniques. At the end of the process, and with the prioritised risks indicating the absolute value of the impact of each risk on the project, the organisation can efficiently allocate resources to the risks identified as the most critical ones.

Quantitative Risk Assessment Flow Chart.

The top of the diagram indicates the risk phases that belong to the overall risk management process. Below them it reflects the steps of the proposed model that would apply in each phase.

The first step corresponds to the project’s “ risk identification ”. Using the techniques or tools established by the organisation (brainstorming, Delphi techniques, interviews, or others), we obtain a list of the risks ( R ) that could impact the project objectives (Eq. 1 ), where m is the number of risks identified in the project.

Next we move on to the “ risk estimation ” phase, in which a distribution function must be assigned to the probability that each identified risk will appear. We also assign the distribution function associated with the risk’s impact. Traditionally, the qualitative risk analysis defines semantic values (low, medium, high) to assign a level of probability and risk impact. These semantic values are used to evaluate the risk in the probability-impact matrix. Numerical scales apply in some cases, which help to assign a semantic level to a given risk (Fig. 3 ).

Source: Project Management Institute ( 2017 ).

Our proposed model includes the three uncertainty types put forward by Hillson ( 2014 ), namely aleatoric, stochastic and epistemic, to identify and assess different risks. Ontological uncertainty is not considered because it goes beyond the limits of human knowledge and cannot, therefore, be modelled (Alleman et al. 2018a ).

A risk can have aleatoric uncertainty as regards the probability of its occurrence, and mainly for its impact if its value can fluctuate over a set range due to its variability. This aleatoric risk uncertainty can be modelled using a probability distribution function (PDF), exactly as we do when modelling activity uncertainty (Acebes et al. 2015 , 2014 ). As the risk management team’s (or project management team’s) knowledge of the project increases, and as more information about the risk becomes available, the choice of the PDF (normal, triangular, beta, among others) and its parameters become more accurate.

A standard definition of risk is “an uncertain event that, if it occurs, may impact project objectives” (Project Management Institute, 2017 ). A risk, if defined according to the above statement, perfectly matches the stochastic uncertainty definition proposed by Hillson ( 2014 ). Moreover, one PDF that adequately models this type of uncertainty is a Bernoulli distribution function (Vose, 2008 ). Thus for deterministic risk probability estimates (the same as for risk impact), we model this risk (probability and impact) with a Bernoulli-type PDF that allows us to introduce this type of uncertainty into our simulation model.

Finally, epistemic uncertainties remain to be modelled, such as those for which we do not have absolute information about and that arise from a lack of knowledge (Damnjanovic and Reinschmidt, 2020 ; Alleman et al. 2018b ). In this case, risks (in likelihood and impact terms) are classified into different levels, and all these levels are assigned a numerical scale (as opposed to the methodology used in a qualitative risk analysis, where levels are classified with semantic values: “high”, “medium” and “low”).

“ Epistemic uncertainty is characterised by not precisely knowing the probability of occurrence or the magnitude of a potential impact. Traditionally, this type of risk has been identified with a qualitative term: “Very Low”, “Low”, “Medium”, “High” and “Very High” before using the probability-impact matrix. Each semantic category has been previously defined numerically by identifying every numerical range with a specific semantic value (Bae et al. 2004 ). For each established range, project managers usually know the limits (upper and lower) between which the risk (probability or impact) can occur. However, they do not certainly know the value it will take, not even the most probable value within that range. Therefore, we employ a uniform probability function to model epistemic uncertainty (i.e., by assuming that the probability of risk occurrence lies within an equiprobable range of values). Probabilistic representations of uncertainty have been successfully employed with uniform distributions to characterise uncertainty when knowledge is sparse or absent (Curto et al. 2022 ; Vanhoucke, 2018 ; Helton et al. 2006 ).

The choice of the number and range of each level should be subject to a thorough analysis and consideration by the risk management team. As each project is unique, there are ranges within which this type of uncertainty can be categorised. Different ranges apply to assess likelihood and impact. Furthermore for impact, further subdivision helps to distinguish between impact on project duration and impact on project costs. For example, when modelling probability, we can set five probability levels corresponding to intervals: [0 0.05], [0.05 0.2], [0.2 0.5], and so on. With the time impact, for example, on project duration, five levels as follows may apply: [0 1], [1 4], [4 12], …. (measured in weeks, for example).

Modelling this type of uncertainty requires the risk management team’s experience, the data stored on previous projects, and constant consultation with project stakeholders. The more project knowledge available, the more accurate the proposed model is for each uncertainty, regardless of it lying in the number of intervals, their magnitude or the type of probability function (PDF) chosen to model that risk.

Some authors propose using uniform distribution functions to model this type of epistemic uncertainty because it perfectly reflects lack of knowledge about the expected outcome (Eldosouky et al. 2014 ; Vose, 2008 ). On the contrary, others apply triangular functions, which require more risk knowledge (Hulett, 2012 ). Following the work by Curto et al. ( 2022 ), we employ uniform distribution functions.

As a result of this phase, we obtain the model and the parameters that model the distribution functions of the probability ( P ) and impact ( I ) of each identified risk in the previous phase (Eq. 2 ).

Once the risks identified in the project have been defined and their probabilities and impacts modelled, we move on to “ quantitative risk prioritisation ”. We start by performing MCS on the planned project model by considering only the aleatoric uncertainty of activities. In this way, we learn the project’s total duration and cost, which is commonly done in a Monte Carlo analysis. In Monte Carlo Methods (MCS), expert judgement and numerical methods are combined to generate a probabilistic result through simulation routine (Ammar et al. 2023 ). This mathematical approach is noted for its ability to analyse uncertain scenarios from a probabilistic perspective. MCS have been recognised as outperforming other methods due to their accessibility, ease of use and simplicity. MCS also allow the analysis of opportunities, uncertainties, and threats (Al-Duais and Al-Sharpi, 2023 ). This technique can be invaluable to risk managers and helpful for estimating project durations and costs (Ali Elfarra and Kaya, 2021 ).

As inputs to the simulation process, we include defining project activities (duration, cost, precedence relationship). We also consider the risks identified in the project, which are those we wish to prioritise and to obtain a list ordered by importance (according to their impact on not only duration, but also on project cost). The ‘MCSimulRisk’ software application (Acebes, Curto, et al. 2023 ; Acebes, De Antón, et al. 2023 ) allows us to perform MCS and to obtain the main statistics that result from simulation (including percentiles) that correspond to the total project duration ( Tot_Dur ) and to its total cost ( Tot_Cost ) (Eq. 3 ).

Next, we perform a new simulation by including the first of the identified risks ( R 1 ) in the project model, for which we know its probability ( P 1 ) and its Impact ( I 1 ). After MCS, we obtain the statistics corresponding to this simulation ([ Tot_Dur 1 Tot_Cost 1 ]). We repeat the same operation with each identified risk ( R i , i  =  1, …, m ) and obtain the main statistics corresponding to each simulation (Eq. 4 ).

Once all simulations (the same number as risks) have been performed, we must choose a confidence percentile to calculate risk prioritisation (Rezaei et al. 2020 ; Sarykalin et al. 2008 ). Given that the total duration and cost results available to us, obtained by MCS, are stochastic and have variability (they are no longer constant or deterministic), we must choose a percentile (α) that conveys the risk appetite that we are willing to assume when calculating. Risk appetite is “ the amount and type of risk that an organisation is prepared to pursue, retain or take ” (International Organization for Standardization, 2018 ).

A frequently employed metric for assessing risk in finance is the Value at Risk (VaR) (Caron, 2013 ; Caron et al. 2007 ). In financial terms, it is traditional to choose a P95 percentile as risk appetite (Chen and Peng, 2018 ; Joukar and Nahmens, 2016 ; Gatti et al. 2007 ; Kuester et al. 2006 ; Giot and Laurent, 2003 ). However in project management, the P80 percentile is sometimes chosen as the most appropriate percentile to measure risk appetite (Kwon and Kang, 2019 ; Traynor and Mahmoodian, 2019 ; Lorance and Wendling, 2001 ).

Finally, after choosing the risk level we are willing to assume, we need to calculate how each risk impacts project duration ( Imp_D Ri ) and costs ( Imp_C Ri ). To do so, we subtract the original value of the total project expected duration and costs (excluding all risks) from the total duration and costs of the simulation in which we include the risk we wish to quantify (Eq. 5 ).

Finally, we present these results on two separate lists, one for the cost impact and one for the duration impact, by ranking them according to their magnitude.

In this section, we use a real-life project to illustrate how to apply the proposed method for quantitative risk prioritisation purposes. For this purpose, we choose an engineering, procurement and construction project undertaken in South America and used in the literature by Votto et al. ( 2020a , 2020b ).

Project description

The project used as an application example consists of the expansion of an industrial facility. It covers a wide spectrum of tasks, such as design and engineering work, procurement of machinery and its components, civil construction, installation of all machinery, as well as commissioning and starting up machines (Votto et al. 2020a , 2020b ).

Table 2 details the parameters that we use to define activities. The project comprises 32 activities, divided into three groups: engineering, procurement and construction (EPC). A fictitious initial activity ( Ai ) and a fictitious final activity ( Af ) are included. We employ triangular distribution functions, whose parameters are the minimum value ( Min ), the most probable value ( Mp ) and the maximum value ( Max ), to model the random duration of activities, expressed as days. We divide the cost of each activity in monetary units into a fixed cost ( FC ), independently of activity duration, and the variable cost ( VC ), which is directly proportional to project duration. As activity duration can vary, and the activity cost increases directly with its duration, the total project cost also exhibits random variations.

Under these conditions, the planned project duration is 300 days and has a planned cost of 30,000 (x1000) monetary units. Figure 4 shows the Planned Value Curve of the project.

Planned value curve of the real-life project.

The next step in the methodology (Fig. 2 ) is to identify the project risks. To do this, the experts’ panel meets, analyses all the project documentation. Based on their personal experience with other similar projects and after consulting all the involved stakeholders, it provides a list of risks (see Table 3 ).

It identifies 11 risks, of which nine have the potential to directly impact the project duration objective (R1 to R9), while six may impact the cost objective (R10 to R15). The risks that might impact project duration and cost have two assigned codes. We identify the project phase and activity on which all the identified risks may have an impact (Table 3 ).

The next step is to estimate the likelihood and impact of the identified risks (qualitative analysis). Having analysed the project and consulted the involved stakeholders, the team determines the project’s different probability and impact levels (duration and cost). The estimation of these ranges depends on the project budget, the estimated project duration, and the team’s experience in assigning the different numerical values to each range. As a result, the project team is able to construct the probability-impact matrix shown in Fig. 5 .

Estimation of the probability and impact ranges.

Each probability range for risk occurrence in this project is defined. Thus for a very low probability (VL), the assigned probability range is between 0 and 3% probability, for a low level (L), the assigned range lies between 3% and 10% probability of risk occurrence, and so on with the other established probability ranges (medium, high, very high).

The different impact ranges are also defined by differentiating between impacts in duration and cost terms. Thus a VL duration impact is between 0 and 5 days, while the same range (VL) in cost is between 0 and 100 (x1000) monetary units. Figure 5 shows the other ranges and their quantification in duration and cost terms.

The combination of each probability level and every impact level coincides in a cell of the risk matrix (Fig. 5 ) to indicate the risk level (“high”, “medium”, and “low”) according to the qualitative analysis. Each cell is assigned a numerical value by prioritising the risks at the same risk level. This work uses the matrix to compare the risk prioritisation results provided by this matrix to those provided by the proposed quantitative method.

A probability and impact value are assigned to each previously identified risk (Table 3 ). Thus, for example, for the risk called “Interruptions in the supply chain”, coded as R3 for impacting activity 13 duration, we estimate an L probability and a strong impact on duration (H). As this same risk might impact the activity 13 cost, it is also coded as R12, and its impact on cost is estimated as L (the probability is the same as in R3; Table 3 ).

Finally, to conclude the proposed methodology and to prioritise the identified risks, we use the “MCSimulRisk” software application by incorporating MCS (in this work, we employ 20,000 iterations in each simulation). Activities are modelled using triangular distribution functions to incorporate project information into the simulation application. Costs are modelled with fixed and variable costs depending on the duration of the corresponding activity. Furthermore, risks (probability and impact) are modelled by uniform distribution functions. Figure 6 depicts the project network and includes the identified risks that impact the corresponding activities.

Network diagram of the project together with the identified risks.

Results and discussion

In order to obtain the results of prioritising the identified risks, we must specify a percentile that determines our risk aversion. This is the measure by which we quantify the risk. Figure 7 graphically justifies the choice of P95 as a risk measure, as opposed to a lower percentile, which corroborates the view in the literature and appears in Methodology . In Fig. 7 , we plot the probability distribution and cumulative distribution functions corresponding to the total project planned cost, together with the cost impact of one of the risks. The impact caused by the risk on the total cost corresponds to the set of iterations whose total cost is higher than that planned (bottom right of the histogram).

Source: MCSimulRisk.

By choosing P95 as VaR, we can consider the impact of a risk on the project in the measure. In this example, for P95 we obtain a total cost value of 3.12 × 10 7 monetary units. Choosing a lower percentile, e.g. P80, means that the value we can obtain with this choice can be considerably lower (3.03 × 10 7 monetary units), and might completely ignore the impact of the risk on the total project cost. However, project managers can choose the percentile that represents their risk aversion.

Once the percentile on which to quantify the risk is chosen, the “MCSimulRisk” application provides us with the desired results for prioritising project risks (Fig. 8 ). For the chosen percentile (P95), which represents our risk appetite for this project, the planned project duration is 323.43 days. In other words, with a 95% probability the planned project will be completed before 323.43 days. Similarly, the P95 corresponding to cost is 30,339 ×1000 monetary units. The application also provides us with the project duration in the first column of Fig. 8 after incorporating all the identified risks (corresponding to a P95 risk appetite) into the planned project. Column 2 of the same figure shows the project cost after incorporating the corresponding risk into the model.

The first column corresponds to the risks identified. Columns Duration_with_Ri and Cost_with_Ri represent the simulation values, including the corresponding risk. Columns Difference_Duration_with_Ri and Difference_Cost_with_Ri represent the difference in duration and cost of each simulation concerning the value obtained for the chosen percentile. Finally, Ranking_Dur and Ranking_Cost represent the prioritisation of risks in duration and cost, respectively.

With the results in the first two columns (total project duration and cost after incorporating the corresponding risks), and by knowing the planned total project duration and cost (without considering risks) for a given percentile (P95), we calculate the values of the following columns in Fig. 8 . Thus column 3 represents the difference between the planned total project duration value (risk-free) and project duration by incorporating the corresponding risk that we wish to quantify. Column 4 prioritises the duration risks by ranking according to the duration that each risk contributes to the project. Column 5 represents the difference between the planned total project cost (risk-free) and the total project cost by incorporating the corresponding risk. Finally, Column 6 represents the ranking or prioritisation of the project risks according to their impact on cost.

To compare the results provided by this methodology in this paper we propose quantitative risk prioritisation, based on MCS. We draw up Table 4 with the results provided by the probability-impact matrix (Fig. 5 ).

The first set of columns in Table 4 corresponds to the implementation of the risk matrix (probability-impact matrix) for the identified risks. The second group of columns represents the prioritisation of risks according to their impact on duration (data obtained from Fig. 8 ). The third group corresponds to the risk prioritisation according to their impact on cost (data obtained from Fig. 8 ).

For the project proposed as an example, we find that risk R3 is the most important one if we wish to control the total duration because it corresponds to the risk that contributes the most duration to the project if it exists. We note that risks R10 to R15 do not impact project duration. If these risks materialise, their contribution to increase (or decrease, as the case may be) project duration is nil.

On the impact on project costs, we note that risk R15 is the most important. It is noteworthy that risk R5 is the fourth most important risk in terms of impact on the total project costs, even though it is initially identified as a risk that impacts project duration. Unlike cost risks (which do not impact the total project duration), the risks that can impact project duration also impact total costs.

We can see that the order of importance of the identified risks differs depending on our chosen method (risk matrix versus quantitative prioritisation). We independently quantify each risk’s impact on the cost and duration objectives. We know not only the order of importance of risks (R3, R5, etc.) but also the magnitude of their impact on the project (which is the absolute delay caused by a risk in duration terms or what is the absolute cost overrun generated by a risk in cost terms). It seems clear that one risk is more important than another, not only because of the estimation of its probability and impact but also because the activity on which it impacts may have a high criticality index or not (probability of belonging to the project’s critical path).

As expected, the contribution to the total duration of the identified risks that impact only cost is zero. The same is not valid for the risks identified to have an impact on duration because the latter also impacts the cost objective. We also see how the risks that initially impact a duration objective are more critical for their impact on cost than others that directly impact the project’s cost (e.g. R5).

Conclusions

The probability-impact matrix is used in project management to identify the risk to which the most attention should be paid during project execution. This paper studies how the risk matrix is adopted by a large majority of standards, norms and methodologies in project management and, at the same time, practitioners and academics recognise it as a fundamental tool in the qualitative risks analysis.

However, we also study how this risk matrix presents particular problems and offers erroneous and contradictory results. Some studies suggest alternatives to its use. Notwithstanding, it continues to be a widely employed tool in the literature by practitioners and academics. Along these lines, with this work we propose an alternative to the probability-impact matrix as a tool to know the most critical risk for a project that can prevent objectives from being fulfilled.

For this purpose, we propose a quantitative method based on MCS, which provides us with numerical results of the importance of risks and their impact on total duration and cost objectives. This proposed methodology offers significant advantages over other risk prioritisation methods and tools, especially the traditional risk matrix. The proposed case study reveals that risk prioritisation yields remarkably different results depending on the selected method, as our findings confirm.

In our case, we obtain numerical values for the impact of risks on total duration and cost objectives, and independently of one another. This result is interesting for project managers because they can focus decision-making on the priority order of risks and the dominant project objective (total duration or total cost) if they do not coincide.

From the obtained results, we find that the risks with an impact on the cost of activities do not influence the total duration result. The risks that impact project duration also impact the total cost target. This impact is more significant than that of a risk that impacts only the activity’s cost. This analysis leads us to believe that this quantitative prioritisation method has incredible potential for academics to extend their research on project risks and for practitioners to use it in the day-to-day implementation of their projects.

The proposed methodology will allow project managers to discover the most relevant project risks so they can focus their control efforts on managing those risks. Usually, implementing risk response strategies might be expensive (control efforts, insurance contracts, preventive actions, or others). Therefore, it is relevant to concentrate only on the most relevant risks. The proposed methodology allows project managers to select the most critical risks by overcoming the problems exhibited by previous methodologies like the probability-impact matrix.

In addition to the above, the risk prioritisation achieved by applying the proposed methodology is based on quantifying the impacts that risks may have on the duration and cost objectives of the project. Finally, we achieve an independent risk prioritisation in duration impact and project cost impact terms. This is important because the project manager can attach more importance to one risk or other risks depending on the priority objective that predominates in the project, the schedule or the total cost.

Undoubtedly, the reliability of the proposed method depends mainly on the accuracy of estimates, which starts by identifying risks and ends with modelling the probability and impact of each risk. The methodology we propose in this paper overcomes many of the problems of previous methodologies, but still has some limitations for future research to deal with. First of all, the results of simulations depend on the estimations of variables (probability distributions and their parameters, risk aversion parameters, etc.). Methodologies for improving estimations are beyond the scope of this research; we assume project teams are sufficient experts to make rational estimationsbased on experience and previous knowledge. Secondly, as risks are assumed to be independent, the contribution or effect of a particular risk can be estimated by including it in simulation and by computing its impact on project cost and duration. This is a reasonable assumption for most projects. In some very complex projects, however, risks can be related to one another. Further research should be done to face this situation.

As an additional research line, we plan to conduct a sensitivity study by simulating many different projects to analyse the robustness of the proposed method.

Finally, it is desirable to implement this methodology in real projects and see how it responds to the reality of a project in, for example, construction, industry, or any other sector that requires a precise and differentiated risk prioritisation.

Data availability

Data will be made available on request.

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Acknowledgements

This research has been partially funded by the Regional Government of Castile and Leon (Spain) and the European Regional Development Fund (ERDF, FEDER) with grant VA180P20.

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FA developed the conceptualisation and the methodology. JMG contributed to the literature review and interpretations of the results for the manuscript. FA and JP collected the experimental data and developed all the analyses and simulations. AL supervised the project. FA and JP wrote the original draft, while AL and JMG conducted the review and editing. All authors have read and agreed to the published version of the manuscript.

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Acebes, F., González-Varona, J.M., López-Paredes, A. et al. Beyond probability-impact matrices in project risk management: A quantitative methodology for risk prioritisation. Humanit Soc Sci Commun 11 , 670 (2024). https://doi.org/10.1057/s41599-024-03180-5

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Published : 24 May 2024

DOI : https://doi.org/10.1057/s41599-024-03180-5

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The Importance of Art Class

Janelle cox.

• May 24, 2024

In  today’s   technology-driven classrooms ,  art remains an  important  component of student development.  Despite often being the first to  be cut  from the curriculum in some schools, dismissed as a luxury, or merely a source of fridge-worthy projects, art education holds profound benefits.

From fostering cognitive abilities and emotional resilience to enhancing academic performance and learning lifelong skills, art class provides much more than  just  a creative outlet. Here,  we’ll  explore why art class is  so  essential and how to make it more accessible to all students. 

Cognitive Skills 

Art classes play a critical role in developing a  student’s cognitive skills. They encourage creativity, allowing students to express themselves in a different way other than writing. This freedom promotes innovative thinking. It also helps to develop students’ critical thinking skills.

As students look at their work and that of their classmates, they learn to observe, analyze, and make judgments,  which are  all valuable skills students will use in all aspects of their lives. Art classes can also enhance  students’  visual-spatial skills.  When students are drawing, painting, or creating  sculptures   they need to understand space and perspective  which  are skills they need if they ever go into fields like architecture or engineering.  

Social-Emotional Learning

Art class extends beyond a  student’s cognitive development, it can also impact their social- emotional learning . Artistic activities can tap into students’ feelings so if they have a hard time vocalizing their feelings, they may be better able to express themselves through art. 

This  can feel therapeutic and help to build their self-confidence. It can also release any anxiety and stress they may be feeling. Art can also promote empathy.  When students explore different art forms and learn different cultural and personal perspectives, they  have a better understanding of  other  people’s  experiences.  

Academic Achievement

Various studies conducted over the years have shown a correlation between art education and academic achievement. Reports from organizations like  the Arts Education Partnership  and the  National Endowment for the Arts in the United States  suggest that the arts are linked to improved test scores, enhanced reading and language skills, and higher rates of going to and completing college. Additional findings show artistic activities enhance memory and attention to detail. Integrating art with other subjects, referred to now as STEAM (Science, Technology, Engineering, Arts, and Mathematics) can help make learning more relatable and deepen students’ understanding and retention. 

Lifelong Skills

The skills learned in art class extend far beyond the classroom.  In today’s job market creativity is valued. Employers are seeking individuals who are innovative, creative, and who think outside of the box. This need for creative thinking is ranked as a top skill for future professionals. Additionally, art class teaches risk-taking and resilience. By continually taking creative risks students are developing resilience which can help them with any challenges they may face in the future. 

Cultural Awareness and Appreciation

When students are engaged with art forms from different cultures , they gain a deeper understanding of global cultures. They learn to respect and value different viewpoints and traditions. By creating and discussing art from various backgrounds, students dispel stereotypes and prejudices, promoting a society that is more inclusive and empathetic to others.  

Making Art Class Accessible 

Art classes are not always accessible to all students.  This may be driven by socioeconomic status, school funding, or geographic location. Ensuring that every student has access to art education is crucial for a student’s well-rounded academic experience. Here are a few approaches to achieve this goal. 

Invest in Art 

One way to make art classes universally accessible is to invest in art programs. Allocate funds for basic supplies and materials that will inspire students to create  as well as invest in professional development for teachers. Teachers who have a background in art education will help foster a greater appreciation for the arts among students. 

Integrate Art

Art can be integrated  into the core curriculum to ensure all students have access to art education.  STEAM education   combining  art with other core curricula can become fundamental to every  child’s  educational experience.  

Utilize Technology 

Art education can be made  more accessible through technology. Digital tools can bring art classes to children across the globe. Virtual classes mean students can learn, create, and share their work with anyone worldwide. 

Form Partnerships within the Community 

Partnerships with local art galleries and artists can provide schools with additional resources.  These partnerships might involve professional artists working with students, or collaborations with local museums that offer field trips or workshops. Community involvement enhances the school’s art program and strengthens the community culture. 

Art class is a vital part of a  child’s educational experience. It nurtures cognitive, social, and emotional skills, boosts academic achievements, makes them more culturally aware, and prepares students with skills they will use throughout their lives. Making art education accessible for all students should be a priority for all leaders and administrators . 

• #ArtClass , #Classroom Management

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