metacognitive process of critical thinking

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Metacognitive Strategies and Development of Critical Thinking in Higher Education

Silvia f. rivas.

1 Departamento de Psicología Básica, Psicobiología y Metodología de CC, Facultad de Psicología, Universidad de Salamanca, Salamanca, Spain

Carlos Saiz

Carlos ossa.

2 Departamento de Ciencias de la Educación, Facultad de Educación y Humanidades, Universidad del Bío-Bío, Sede Chillán, Chile

Associated Data

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

More and more often, we hear that higher education should foment critical thinking. The new skills focus for university teaching grants a central role to critical thinking in new study plans; however, using these skills well requires a certain degree of conscientiousness and its regulation. Metacognition therefore plays a crucial role in developing critical thinking and consists of a person being aware of their own thinking processes in order to improve them for better knowledge acquisition. Critical thinking depends on these metacognitive mechanisms functioning well, being conscious of the processes, actions, and emotions in play, and thereby having the chance to understand what has not been done well and correcting it. Even when there is evidence of the relation between metacognitive processes and critical thinking, there are still few initiatives which seek to clarify which process determines which other one, or whether there is interdependence between both. What we present in this study is therefore an intervention proposal to develop critical thinking and meta knowledge skills. In this context, Problem-Based Learning is a useful tool to develop these skills in higher education. The ARDESOS-DIAPROVE program seeks to foment critical thinking via metacognition and Problem-Based Learning methodology. It is known that learning quality improves when students apply metacognition; it is also known that effective problem-solving depends not only on critical thinking, but also on the skill of realization, and of cognitive and non-cognitive regulation. The study presented hereinafter therefore has the fundamental objective of showing whether instruction in critical thinking (ARDESOS-DIAPROVE) influences students’ metacognitive processes. One consequence of this is that critical thinking improves with the use of metacognition. The sample was comprised of first-year psychology students at Public University of the North of Spain who were undergoing the aforementioned program; PENCRISAL was used to evaluate critical thinking skills and the Metacognitive Activities Inventory (MAI) for evaluating metacognition. We expected an increase in critical thinking scores and metacognition following this intervention. As a conclusion, we indicate actions to incentivize metacognitive work among participants, both individually via reflective questions and decision diagrams, and at the interactional level with dialogues and reflective debates which strengthen critical thinking.

Introduction

One of the principal objectives which education must cover is helping our students become autonomous and effective. Students’ ability to use strategies which help them direct their motivation toward action in the direction of the meta-proposal is a central aspect to keep at the front of our minds when considering education. This is where metacognition comes into play—knowledge about knowledge itself, a component which is in charge of directing, monitoring, regulating, organizing, and planning our skills in a helpful way, once these have come into operation. Metacognition helps form autonomous students, increasing consciousness about their own cognitive processes and their self-regulation so that they can regulate their own learning and transfer it to any area of their lives. As we see, it is a conscious activity of high-level thinking which allows us to look into and reflect upon how we learn and to control our own strategies and learning processes. We must therefore approach a problem which is increasing in our time, that of learning and knowledge from the perspective of active participation by students. To achieve these objectives of “learning to learn” we must use adequate cognitive learning strategies, among which we can highlight those oriented toward self-learning, developing metacognitive strategies, and critical thinking.

Metacognition is one of the research areas, which has contributed the most to the formation of the new conceptions of learning and teaching. In this sense, it has advanced within the constructivist conceptions of learning, which have attributed an increasing role to student consciousness and to the regulation which they exercise over their own learning ( Glaser, 1994 ).

Metacognition was initially introduced by John Flavell in the early 1970s. He affirmed that metacognition, on one side, refers to “the knowledge which one has about his own cognitive processes products, or any other matter related with them” and on the other, “to the active supervision and consequent regulation and organization of these processes in relation with the objects or cognitive data upon which they act” ( Flavell, 1976 ; p. 232). Based on this, we can differentiate two components of metacognition: one of a declarative nature, which is metacognitive knowledge, referring to knowledge of the person and the task, and another of a procedural nature, which is metacognitive control or self-regulated learning, which is always directed toward a goal and controlled by the learner.

Different authors have pointed out that metacognition presents these areas of thought or skills, aimed knowledge or toward the regulation of thought and action, mainly proposing a binary organization in which attentional processes are oriented, on occasions, toward an object or subject, and the other hand, toward to interact with objects and/or subjects ( Drigas and Mitsea, 2021 ). However, it is possible to understand metacognition from another approach that establishes more levels of use of metacognitive thinking to promote knowledge, awareness, and intelligence, known as the eight pillars of metacognition model ( Drigas and Mitsea, 2020 ). These pillars allow thought to promote the use of deep knowledge, cognitive processes, self-regulation, functional adaptation to society, pattern recognition and operations, and even meaningful memorization ( Drigas and Mitsea, 2020 ).

In addition to the above, Drigas and Mitsea’s model establishes different levels where metacognition could be used, in a complex sequence from stimuli to transcendental ideas, in which each of the pillars could manifest a different facet of the process metacognitive, thus establishing a dialectical and integrative approach to learning and knowledge, allowing it to be understood as an evolutionary and complex process in stages ( Drigas and Mitsea, 2021 ).

All this clarifies the importance of and need for metacognition, not only in education but also in our modern society, since this need to “teach how to learn” and the capacity to “learn how to learn” in order to achieve autonomous learning and transfer it to any area of our lives will let us face problems more successfully. This becomes a relevant challenge, especially today where it is required to have a broad view regarding reflection and consciousness, and to transcend simplistic and reductionist models that seek to center the problem of knowledge only around the neurobiological or the phenomenological scope ( Sattin et al., 2021 ).

Critical thinking depends largely on these mechanisms functioning well and being conscious of the processes used, since this gives us the opportunity to understand what has not been done well and correct it in the future. Consciousness for critical thinking would imply a continuous process of reuse of thought, in escalations that allow thinking to be oriented both toward the objects of the world and toward the subjective interior, allowing to determine the ideas that give greater security to the person, and in that perspective, the metacognitive process, represents this use of Awareness, also allowing the generation of an identity of knowing being ( Drigas and Mitsea, 2021 ).

We know that thinking critically involves reasoning and deciding to effectively solve a problem or reach goals. However, effective use of these skills requires a certain degree of consciousness and regulation of them. The ARDESOS-DIAPROVE program seeks precisely to foment critical thinking, in part, via metacognition ( Saiz and Rivas, 2011 , 2012 , 2016 ).

However, it is not only centered on developing cognitive components, as this would be an important limitation. Since the 1990s, it has been known that non-cognitive components play a crucial role in developing critical thinking. However, there are few studies focusing on this relation. This intervention therefore considers both dimensions, where metacognitive processes play an essential role by providing evaluation and control mechanisms over the cognitive dimension.

Metacognition and Critical Thinking

Critical Thinking is a concept without a firm consensus, as there have been and still are varying conceptions regarding it. Its nature is so complex that it is hard to synthesize all its aspects in a single definition. While there are numerous conceptions about critical thinking, it is necessary to be precise about which definition we will use. We understand that “ critical thinking is a knowledge-seeking process via reasoning skills to solve problems and make decisions which allows us to more effectively achieve our desired results” ( Saiz and Rivas, 2008 , p. 131). Thinking effectively is desirable in all areas of individual and collective action. Currently, the background of the present field of critical thinking is also based in argumentation. Reasoning is used as the fundamental basis for all activities labeled as thinking. In a way, thinking cannot easily be decoupled from reasoning, at least if our understanding of it is “deriving something from another thing.” Inference or judgment is what we essentially find behind the concept of thinking. The question, though, is whether it can be affirmed that thinking is only reasoning. Some defend this concept ( Johnson, 2008 ), while others believe the opposite, that solving problems and making decisions are activities which also form part of thinking processes ( Halpern, 2003 ; Halpern and Dunn, 2021 , 2022 ). To move forward in this sense, we will return to our previous definition. In that definition, we have specified intellectual activity with a goal intrinsic to all mental processes, namely, seeking knowledge. Achieving our ends depends not only on the intellectual dimension, as we may need our motor or perceptive activities, so it contributes little to affirm that critical thinking allows us to achieve our objectives as we can also achieve them by doing other activities. It is important for us to make an effort to identify the mental processes responsible for thinking and distinguish them from other things.

Normally, we think to solve our problems. This is the second important activity of thought. A problem can be solved by reasoning, but also by planning course of action or selecting the best strategy for the situation. Apart from reasoning, we must therefore also make decisions to resolve difficulties. Choosing is one of the most frequent and important activities which we do. Because of this, we prefer to give it the leading role it deserves in a definition of thinking. Solving problems demands multiple intellectual activities, including reasoning, deciding, planning, etc. The final characteristic goes beyond the mechanisms peculiar to inference. What can be seen at the moment of delineating what it means to think effectively is that concepts are grouped together which go beyond the nuclear ideas of what has to do with inferring or reasoning. The majority of theoreticians in the field ( APA, 1990 ; Ennis, 1996 ; Halpern, 1998 , 2003 ; Paul and Elder, 2001 ; Facione, 2011 ; Halpern and Dunn, 2021 , 2022 ) consider that, in order to carry out this type of thinking effectively, apart from having this skill set, the intervention of other types of components is necessary, such as metacognition and motivation. This is why we consider it necessary to speak about the components of critical thinking, as we can see in Figure 1 :

Components of critical thinking ( Saiz, 2020 ).

In the nature of thinking, there are two types of components: the cognitive and the non-cognitive. The former include perception, learning, and memory processes. Learning is any knowledge acquisition mechanism, the most important of which is thinking. The latter refer to motivation and interests (attitudes tend to be understood as dispositions, inclinations…something close to motives); with metacognition remaining as a process which shares cognitive and non-cognitive aspects as it incorporates aspects of both judgment (evaluation) and disposition (control/efficiency) about thoughts ( Azevedo, 2020 ; Shekhar and Rahnev, 2021 ). Both the cognitive and non-cognitive components are essential to improve critical thinking, as one component is incomplete without the other, that is, neither cognitive skills nor dispositions on their own suffice to train a person to think critically. In general, relations are bidirectional, although for didactic reasons only unidirectional relations appear in Figure 1 ( Rivas et al., 2017 ). This is because learning is a dynamic process which is subject to all types of influence. For instance, if a student is motivated, they will work more and better—or at least, this is what is hoped for. If they can achieve good test scores as well, it can be supposed that motivation is reinforced, so that they will continue existing behaviors in the same direction that is, working hard and well on their studies. This latter point appears to arise at least because of an adjustment between expectations and reality which the student achieves thanks to metacognition, which allows them to effectively attribute their achievements to their efforts ( Ugartetxea, 2001 ).

Metacognition, which is our interest in this paper, should also have bidirectional relations with critical thinking. Metacognition tends to be understood as the degree of consciousness which we have about our own mental processes and similar to the capacity for self-regulation, that is, planning and organization ( Mayor et al., 1993 ). We observe that these two ideas have very different natures. The former is simpler, being the degree of consciousness which we reach about an internal mechanism or process. The latter is a less precise idea, since everything which has to do with self-regulation is hard to differentiate from a way of understanding motivation, such as the entire tradition of intrinsic motivation and self-determination from Deci, his collaborators, and other authors of this focus (see, e.g., Deci and Ryan, 1985 ; Ryan and Deci, 2000 ). The important thing is to emphasize the executive dimension of metacognition, more than the degree of consciousness, for practical reasons. It can be expected that this dimension has a greater influence on the learning process than that of consciousness, although there is little doubt that we have to establish both as necessary and sufficient conditions. However, the data must speak in this regard. Due to all of this, and as we shall see hereinafter, the intervention designed incorporates both components to improve critical thinking skills.

We can observe, though, that the basic core of critical thinking continues to be topics related to skills, in our case, reasoning, problem-solving, and decision-making. The fact that we incorporate concepts of another nature, such as motivation, in a description of critical thinking is justified because it has been proven that, when speaking about critical thinking, the fact of centering solely on skills does not allow for fully gathering its complexity. The purpose of the schematic in Figure 2 is to provide conceptual clarity to the adjective “critical” in the expression critical thinking . If we understand critical to refer to effective , we should also consider that effectiveness is not, as previously mentioned, solely achieved with skills. They must be joined together with other mechanisms during different moments. Intellectual skills alone cannot achieve the effectiveness assumed within the term “critical.” First, for said skills to get underway, we must want to do so. Motivation therefore comes into play before skills and puts them into operation. For its part, metacognition allows us to take advantage of directing, organizing, and planning our skills and act once they have begun to work. Motivation thus activates our abilities, while metacognition lets them be more effective. The final objective should always be to gain proper knowledge of reality to resolve our problems.

Purpose of critical thinking ( Saiz, 2020 , p.27).

We consider that the fact of referring to components of critical thinking while differentiating the skills of motivation and metacognition aids with the conceptual clarification we seek. On one side, we specify the skills which we discuss, and on another, we mention which other components are related to, and even overlap with them. We must be conscious of how difficult it is to find “pure” mental processes. Planning a course of action, an essential trait of metacognition, demands reflection, prediction, choice, comparison, and evaluation… And this, evidently, is thinking. The different levels or dimensions of our mental activity must be related and integrated. Our aim is to be able to identify what is substantial in thinking to know what we are able to improve and evaluate.

It is widely known that for our personal and professional functioning, thinking is necessary and useful. When we want to change a situation or gain something, all our mental mechanisms go into motion. We perceive the situation, identify relevant aspects of the problem, analyze all the available information, and appraise everything we analyze. We make judgments about the most relevant matters, decide about the options or pathways for resolution, execute the plan, obtain results, evaluate the results, estimate whether we have achieved our purpose and, according to the level of satisfaction following this estimation, consider our course of action good, or not.

The topic we must pose now is what things are teachable. It is useful to specify that what is acquired is clearly cognitive and some of the non-cognitive, because motivation can be stimulated or promoted, but not taught. The concepts of knowledge and wisdom are its basis. Mental representation and knowledge only become wisdom when we can apply it to reality, when we take it out of our mind and adequately situate it in the world. For our teaching purposes, we only have to take a position about whether knowledge is what makes critical thinking develop, or vice versa. For us, skills must be directly taught, and dominion is secondary. Up to now, we have established the components of critical thinking, but these elements still have to be interrelated properly. What we normally find are skills or components placed side by side or overlapping, but not the ways in which they influence each other. Lipman (2003) may have developed the most complete theory of critical and creative thinking, along Paul and his group, in second place, with their universal thought structures ( Paul and Elder, 2006 ). However, a proposal for the relation between the elements is lacking.

To try to explain the relation between the components of thought, we will use Figure 2 as an aid.

The ultimate goal of critical thinking is change that is, passing from one state of wellbeing into a better state. This change is only the fruit of results, which must be the best. Effectiveness is simple achieving our goals in the best way possible. There are many possible results, but for our ends, there are always some which are better than others. Our position must be for effectiveness, the best response, the best solution. Reaching a goal is resolving or achieving something, and for this, we have mechanisms available which tell us which are the best course of action. Making decisions and solving problems are fundamental skills which are mutually interrelated. Decision strategies come before a solution. Choosing a course of action always comes before its execution, so it is easy to understand that decisions contribute to solutions.

Decisions must not come before reflection, although this often can and does happen. As we have already mentioned, the fundamental skills of critical thinking, in most cases, have been reduced to reasoning, and to a certain degree, this is justified. There is an entire important epistemological current behind this, within which the theory of argumentation makes no distinction, at least syntactically, between argumentation and explanation. However, for us this distinction is essential, especially in practice ( Saiz, 2020 ). We will only center on an essential difference for our purpose. Argumentation may have to do with values and realities, but explanation only has to do with the latter. We can argue about beliefs, convictions, and facts, but we can only explain realities. Faced with an explanation of reality, any argumentation would be secondary. Thus, explanation will always be the central skill in critical thinking.

The change which is sought is always expressed in reality. Problems always are manifested and resolved with actions, and these are always a reality. An argument about realities aids in explaining them. An argument about values upholds a belief or a conviction. However, beliefs always influence behavior; thus, indirectly, the argument winds up being about realities. One may argue, for example, only for or against the death penalty, and reach the conviction that it is good or bad and ultimately take a position for or against allowing it. This is why we say that deciding always comes before resolving; furthermore, resolution always means deciding about something in a particular direction—it always means choosing and taking an option; furthermore, deciding is often only from two possibilities, the better or that which is not better, or which is not as good. Decisions are made based on the best option possible of all those which can be presented. Resolution is a dichotomy. Since our basic end lies within reality, explanation must be constituted as the basic pillar to produce change. Argumentation must therefore be at the service of causality (explanation), and both must be in the service of solid decisions leading us to the best solution or change of situation. We now believe that the relation established in Figure 2 can be better understood. From this relation, we propose that thinking critically means reaching the best explanation for an event, phenomenon, or problem in order to know how to effectively resolve it ( Saiz, 2017 , p.19). This idea, to our judgment, is the best summary of the nature of critical thinking. It clarifies details and makes explicit the components of critical thinking.

Classroom Activities to Develop Metacognition

We will present a set of strategies to promote metacognitive work in the classroom in this section, aimed at improving critical thinking skills. These strategies can be applied both at the university level and the secondary school level; we will thus focus on these two levels, although metacognitive strategies can be worked on from an earlier age ( Jaramillo and Osses, 2012 ; Tamayo-Alzate et al., 2019 ) and some authors have indicated that psychological maturity has a greater impact on effectively achieving metacognition ( Sastre-Riba, 2012 ; García et al., 2016 ).

At the individual level, metacognition can be worked on via applying questions aimed at the relevant tasks which must be undertaken regarding a task (meta-knowledge questions), for example:

• Do I know how much I know about this subject?
• Do I have clear instructions and know what action is expected from me?
• How much time do I have?
• Am I covering the proper and necessary subjects, or is there anything important left out?
• How do I know that my work is right?
• Have I covered every point of the rubric for the work to gain a good grade or a sufficient level?

These reflective questions facilitate supervising knowledge level, resource use, and the final product achieved, so that the decisions taken for said activities are the best and excellent learning results are achieved.

Graphs or decision diagrams can also be used to aid in organizing these questions during the different phases of executing a task (planning, progress, and final evaluation), which is clearly linked with the knowledge and control processes of metacognition ( Mateos, 2001 ). These diagrams are more complex and elaborate strategies than the questions, but are effective when monitoring the steps considered in the activity ( Ossa et al., 2016 ). Decision diagrams begin from a question or task, detailing the principal steps to take, and associating an alternative (YES or NO) to each step, which leads to the next step whenever the decision is affirmative, or to improve or go further into the step taken if the decision is negative.

Finally, we can work on thinking aloud, a strategy which facilitates making the thoughts explicit and conscious, allowing us to monitor their knowledge, decisions, and actions to promote conscious planning, supervision and evaluation ( Ávila et al., 2017 ; Dahik et al., 2019 ). For example:

• While asking a question, the student thinks aloud: I am having problems with this part of the task, and I may have to ask the teacher to know whether I am right.

Thinking aloud can be done individually or in pairs, allowing for active monitoring of decisions and questions arising from cognitive and procedural work done by the student.

Apart from the preceding strategies, it is also possible to fortify metacognitive development via personal interactions based on dialogue between both the students themselves and between the teacher and individual students. One initial strategy, similar to thinking out loud in pairs, is reflective dialogue between teacher and student, a technique which allows for exchanging deep questions and answers, where the student becomes conscious of their knowledge and practice thanks to dialogical interventions by the teacher ( Urdaneta, 2014 ).

Reflective dialogue can also be done via reflective feedback implemented by the teacher for the students to learn by themselves about the positive and negative aspects of their performance on a task.

Finally, another activity based on dialogue and interaction is related to metacognitive argumentation ( Sánchez-Castaño et al., 2015 ), a strategy which uses argumentative resources to establish a valid argumentative structure to facilitate responding to a question or applying it to a debate. While argumentative analysis is based on logic and the search for solid reasons, these can have higher or lower confidence and reliability as a function of the data which they provide. Thus, if a reflective argumentative process is performed, via questioning reasons or identifying counterarguments, there is more depth and density in the argumentative structure, achieving greater confidence and validity.

We can note that metacognition development strategies are based on reflective capacity, which allow thought to repeatedly review information and decisions to consider, without immediately taking sides or being carried away by superficial or biased ideas or data. Critical thought benefits strongly from applying this reflective process, which guides both data management and cognitive process use. These strategies can also be developed in various formats (written, graphic, oral, individual, and dialogical), providing teachers a wide range of tools to strengthen learning and thinking.

Metacognitive Strategies to Improve Critical Thinking

In this section, we will describe the fundamental metacognitive strategies addressed in our critical thinking skills development program ARDESOS-DIAPROVE.

First, one of the active learning methodologies applied is Problem-Based Learning (PBL). This pedagogical strategy is student-centered and encourages autonomous and participative learning, orienting students toward more active and decisive learning. In PBL each situation must be approached as a problem-solving task, making it necessary to investigate, understand, interpret, reason, decide, and resolve. It is presented as a methodology which facilitates joint knowledge acquisition and skill learning. It is also good for working on daily problems via relevant situations, considerably reducing the distance between learning context and personal/professional life and aiding the connection between theory and practice, which promote the highly desired transference. It favors organization and the capacity to decide about problem-solving, which also improves performance and knowledge about the students’ own learning processes. Because of all this, this methodology aids in reflection and analysis processes, which in turn promotes metacognitive skill development.

The procedure which we carried out in the classroom with all the activities is based on the philosophy of gradual learning control transference ( Mateos, 2001 ). During instruction, the teacher takes on the role of model and guide for students’ cognitive and metacognitive activity, gradually bringing them into participating in an increasing level of competency, and slowly withdrawing support in order to attain control over the students’ learning process. This methodology develops in four phases: (1) explicit instruction, where the teacher directly explains the skills which will be worked on; (2) guided practice, where the teacher acts as a collaborator to guide and aid students in self-regulation; and (3) cooperative practice, where cooperative group work facilitates interaction with a peer group collaborating to resolve the problem. By explaining, elaborating, and justifying their own points of view and alternative solutions, greater consciousness, reflection, and control over their own cognitive processes is promoted. Finally, (4) individual practice is what allows students to place their learning into practice in individual evaluation tasks.

Regarding the tasks, it is important to highlight that the activities must be aimed not only at acquiring declarative knowledge, but also at procedural knowledge. The objective of practical tasks, apart from developing fundamental knowledge, is to develop CT skills among students in both comprehension and expression in order to favor their learning and its transference. The problems used must be common situations, close to our students’ reality. The important thing in our task of teaching critical thinking is its usefulness to our students, which can only be achieved during application since we only know something when we are capable of applying it. We are not interested in students merely developing critical skills; they must also be able to generalize their intellectual skills, for which they must perceive them as useful in order to want to acquire them. Finally, they will have to actively participate to apply them to solving problems. Furthermore, if we study the different ways of reasoning without context, via overly academic problems, their application to the personal sphere becomes impossible, leading them to be considered hardly useful. This makes it important to contextualize skills within everyday problems or situations which help us get students to use them regularly and understand their usefulness.

Reflecting on how one carries things out in practice and analyzing mistakes are ways to encourage success and autonomy in learning. These self-regulation strategies are the properly metacognitive part of our study. The teacher has various resources to increase these strategies, particularly feedback oriented toward task resolution. Similarly, one of the most effective instruments to achieve it is using rubrics, a central tool for our methodology. These guides, used in student performance evaluations, describe the specific characteristics of a task at various performance levels, in order to clarify expectations for students’ work, evaluate their execution, and facilitate feedback. This type of technique also allows students to direct their own activity. We use them with this double goal in mind; on the one hand, they aid students in carrying out tasks, since they help divide the complex tasks they have to do into simpler jobs, and on the other, they help evaluate the task. Rubrics guide students in the skills and knowledge they need to acquire as well as facilitating self-evaluation, thereby favoring responsibility in their learning. Task rubrics are also the guide for evaluation which teachers carry out in classrooms, where they specify, review, and correctly resolve the tasks which students do according to the rubric criteria. Providing complete feedback to students is a crucial aspect for the learning process. Thus, in all sessions time is dedicated to carrying it out. This is what will allow them to move ahead in self-regulated skill learning.

According to what we have seen, there is a wide range of positions when it comes to defining critical thinking. However, there is consensus in the fact that critical thinking involves cognitive, attitudinal, and metacognitive components, which together favor proper performance in critical thinking ( Ennis, 1987 ; Facione, 1990 ). This important relation between metacognition and critical thinking has been widely studied in the literature ( Berardi-Coletta et al., 1995 ; Antonietti et al., 2000 ; Kuhn and Dean, 2004 ; Black, 2005 ; Coutinho et al., 2005 ; Orion and Kali, 2005 ; Schroyens, 2005 ; Akama, 2006 ; Choy and Cheah, 2009 ; Magno, 2010 ; Arslan, 2014 ) although not always in an applied way. Field studies indicate the existence of relations between teaching metacognitive strategies and progress in students’ higher-order thinking processes ( Schraw, 1998 ; Kramarski et al., 2002 ; Van der Stel and Veenman, 2010 ). Metacognition is thus considered one of the most relevant predictors of achieving a complex higher-order thought process.

Along the same lines, different studies show the importance of developing metacognitive skills among students as it is related not only with developing critical thinking, but also with academic achievement and self-regulated learning ( Klimenko and Alvares, 2009 ; Magno, 2010 ; Doganay and Demir, 2011 ; Özsoy, 2011 ). Klimenko and Alvares (2009) indicated that one way for students to acquire necessary tools to encourage autonomous learning is making cognitive and metacognitive strategies explicit and well-used and that teachers’ role is to be mediators and guides. Inspite of this evidence, there is less research about the use of metacognitive strategies in encouraging critical thinking. The principal reason is probably that it is methodologically difficult to gather direct data about active metacognitive processes which are complex by nature. Self-reporting is also still very common in metacognition evaluation, and there are few studies which have included objective measurements aiding in methodological precision for evaluating metacognition.

However, in recent years, greater importance has been assigned to teaching metacognitive skills in the educational system, as they aid students in developing higher-order thinking processes and improving their academic success ( Flavell, 2004 ; Larkin, 2009 ). Because of this, classrooms have seen teaching and learning strategies emphasizing metacognitive knowledge and regulation. Returning to our objective, which is to improve critical thinking via the ARDESOS-DIAPROVE program, we have achieved our goal in an acceptable way ( Saiz and Rivas, 2011 , 2012 , 2016 ).

However, we need to know which specific factors contribute to this improvement. We have covered significant ground through different studies, one of which we present here. In this one, we attempt to find out the role of metacognition in critical thinking. This is the central objective of the study. Our program includes motivational and metacognitive variables. Therefore, we seek to find out whether metacognition improves after this instruction program focused on metacognition. Therefore, our hypothesis is simple: we expect that the lesson will improve our students’ metacognition. The idea is to know whether applying metacognition helps us achieve improved critical thinking and whether after this change metaknowledge itself improves. In other words, improved critical thinking performance will make us think better about thinking processes themselves. If this can be improved, we can expect that in the future it will have a greater influence on critical thinking. The idea is to be able to demonstrate that applying specifically metacognitive techniques, the processes themselves will subsequently improve in quality and therefore contribute better volume and quality to reasoning tasks, decision-making and problem-solving.

Materials and Methods

Participants.

In the present study, we used a sample of 89 students in a first-year psychology course at Public University of the North of Spain. 82% (73) were women, and the other 18% (16) were men. Participants’ median age was 18.93 ( SD 1.744).

Instruments

Critical thinking test.

To measure critical thinking skills, we applied the PENCRISAL test ( Saiz and Rivas, 2008 ; Rivas and Saiz, 2012 ). The PENCRISAL is a battery consisting of 35 production problem situations with an open-answer format, composed of five factors: Deductive Reasoning , Inductive Reasoning , Practical Reasoning , Decision-Making , and Problem-Solving , with seven items per factor. Items for each factor gather the most representative structures of fundamental critical thinking skills.

The items’ format is open, so that the person has to answer a concrete question, adding a justification for the reasons behind their answer. Because of this, there are standardized correction criteria assigning values between 0 and 2 points as a function of answer quality. This test offers us a total score of critical thinking skills and another five scores referring to the five factors. The value range is located between 0 and 72 points as a maximum limit for total test scoring, and between 0 and 14 for each of the five scales. The reliability measures present adequate precision levels according to the scoring procedures, with the lowest Cronbach’s alpha values at 0.632, and the test–retest correlation at 0.786 ( Rivas and Saiz, 2012 ). PENCRISAL administration was done over the Internet via the evaluation platform SelectSurvey.NET V5: http://24.selectsurvey.net/pensamiento-critico/Login.aspx .

Metacognitive Skill Inventory

Metacognitive skill evaluation was done via the metacognitive awareness inventory from Schraw and Dennison (1994) (MAI; Huertas Bustos et al., 2014 ). This questionnaire has 52 Likert scale-type items with five points. The items are distributed in two general dimensions: cognitive knowledge (C) and regulation of cognition (R). This provides ample coverage for the two aforementioned ideas about metaknowledge. There are also eight defined subcategories within each general dimension. For C, these are: declarative knowledge (DK), procedural knowledge (PK), and conditional knowledge (CK). In R, we find: organization (O), monitoring (M), and evaluation (E). This instrument comprehensively, and fairly clearly, brings together essential aspects of metacognition. On one side, there is the level of consciousness, containing types of knowledge—declarative, procedural, and strategic. On the other, it considers everything important in the processes of self-regulation, planning, organization, direction or control (monitoring), adjustment (troubleshooting), and considering the results achieved (evaluation). It provides a very complete vision of everything important in this dimension. Cronbach’s alpha for this instrument is 0.94, showing good internal consistency.

Intervention Program

As previously mentioned, in this study, we applied the third version of the ARDESOS_DIAPROVE program ( Saiz and Rivas, 2016 ; Saiz, 2020 ), with the objective of improving thinking skills. This program is centered on directly teaching the skills which we consider essential to develop critical thinking and for proper performance in our daily affairs. For this, we must use reasoning and good problem-solving and decision-making strategies, with one of the most fundamental parts of our intervention being the use of everyday situations to develop these abilities.

DIAPROVE methodology incorporates three new and essential aspects: developing observation, the combined use of facts and deduction, and effective management of de-confirmation procedures, or discarding hypotheses. These are the foundation of our teaching, which requires specific teaching–learning techniques.

The intervention took place over 16 weeks and is designed to be applied in classrooms over a timeframe of 55–60 h. The program is applied in classes of around 30–35 students divided into groups of four for classwork in collaborative groups, and organized into six activity blocks: (1) nature of critical thinking, (2) problem-solving and effectiveness, (3) explanation and causality, (4) deduction and explanation, (5) argumentation and deduction, and (6) problem-solving and decision-making. These blocks are assembled maintaining homogeneity, facilitating a global integrated skill focus which helps form comprehension and use of the different structures in any situation as well as a greater degree of ability within the domain of each skill.

Our program made an integrated use of problem-based learning (PBL) and cooperative learning (CL) as didactic teaching and learning strategies in the critical thinking program. These methodologies jointly exert a positive influence on the students, allowing them to participate more actively in the learning process, achieve better results in contextualizing content and developing skills and abilities for problem-solving, and improve motivation.

To carry out our methodology in the classrooms, we have designed a teaching system aligned with these directives. Two types of tasks are done: (1) comprehension and (2) production. The materials we used to carry out these activities are the same for all the program blocks. One key element in our aim of teaching how to think critically must be its usefulness to our students, which is only achieved through application. This makes it important to contextualize reasoning types within common situations or problems, aiding students to use them regularly and understand their usefulness. Our intention with the materials we use is to face the problems of transference, usefulness, integrated skills, and how to produce these things. Accordingly, the materials used for the tasks are: (1) common situations and (2) professional/personal problems.

The tasks which the students perform take place over a week. They work in cooperative groups in class, and then review, correct, and clarify together, promoting reflection on their achievements and errors, which fortifies metacognition. Students get the necessary feedback on the work performed which will help them progressively acquire fundamental procedural contents. Our goal here is that students become conscious of their own thought processes in order to improve them. In this way, via the dialogue achieved between teachers and students as well as between the students themselves in their cooperative work, metacognition is developed. For conscious performance of tasks, the students will receive rubrics for each and every task to guide them in their completion.

Application of the ARDESOS-DIAPROVE program was done across a semester in the Psychology Department of the Public University of the North of Spain. One week before teaching began; critical thinking and metacognition evaluations were done. This was also done 1 week after the intervention ended, in order to gather the second measurement for PENCRISAL and MAI. The timelapse between the pre-treatment and post-treatment measurements was 4 months. The intervention was done by instructors with training and good experience in the program.

To test our objective, we used a quasi-experimental pre-post design with repeated measurements.

Statistical Analysis

For statistical analysis, we used the IBM SPSS Statistics 26 statistical packet. The statistical tools and techniques used were: frequency and percentage tables for qualitative variables, exploratory and descriptive analysis of quantitative variables with a goodness of fit test to the normal Gaussian model, habitual descriptive statistics (median, SD, etc.) for numerical variables, and Student’s t -tests for significance of difference.

To begin, a descriptive analysis of the study variables was carried out. Tables 1 , ​ ,2 2 present the summary of descriptions for the scores obtained by students in the sample, as well as the asymmetry and kurtosis coefficients for their distribution.

Description of critical thinking measurement (PENCRISAL).

TOT_PRE, PENCRISAL pre-test; RD_PRE, Deductive reasoning pre-test; RI_PRE, Inductive reasoning pre-test; RP_PRE, Practical reasoning pre-test; TD_PRE, Decision making pre-test; SP_PRE, Problem solving pre-test; TOT_POST, PENCRISAL post-test; RD_ POST, Deductive reasoning post-test; RI_ POST, Inductive reasoning post-test; RP_ POST, Practical reasoning post-test; TD_ POST, Decision making post-test; SP_ POST, Problem solving post-test; Min, minimum, Max, maximum, Asym, asymmetry; and Kurt, kurtosis.

Description of metacognition measurement (MAI).

TOT_MAI_PRE, MAI pre-test; Decla_PRE, Declarative pre-test; Proce_PRE, Procedural pre-test; Condi_PRE, Conditional pre-test; CONO_PRE, Knowledge pre-test; Plani_PRE, Planning pre-test; Orga_PRE, Organization pre-test; Moni_PRE, Monitoring pre-test; Depu_PRE, Troubleshooting pre-test; Eva_PRE, Evaluation pre-test; REGU_PRE, Regulation pre-test; TOT_MAI_POST, MAI post-test; Decla_ POST, Declarative post-test; Proce_ POST, Procedural post-test; Condi_ POST, Conditional post-test; CONO_ POST, Knowledge post-test; Plani_ POST, Planning post-test; Orga_POST, Organization post-test; Moni_ POST, Monitoring post-test; Depu_ POST, Troubleshooting post-test; Eva_ POST, Evaluation post-test; and REGU_ POST, Regulation post-test;

As we see in the description of all study variables, the evidence is that the majority of them adequately fit the normal model, although some present significant deviations which can be explained by sample size.

Next, to verify whether there were significant differences in the metacognition variable based on measurements before and after the intervention, we contrasted medians for samples related with Student’s t -test (see Table 3 ).

Comparison of the METAKNOWLEDGE variable as a function of PRE-POST measurements.

The results show that there are significant differences in the metaknowledge scale total and in most of its dimensions, where all the post medians for both the scale overall and for the three dimensions of the knowledge factor (declarative, procedural, and conditional) are higher than the pre-medians. However, in the cognition regulation dimension, there are only significant differences in the total and in the planning, organization, and monitoring dimensions. The medians are also greater in the post-test than the pre-test. However, the troubleshooting and evaluation dimensions do not differ significantly after intervention.

Finally, for critical thinking skills, the results show significant differences in the scale total and in the five factors regarding the measurement time, where performance medians rise after intervention (see Table 4 ).

Comparison of the CRITICAL THINKING variable as a function of PRE-POST measurements.

These results show how metacognition improves due to CT intervention, as well as how critical thinking also improves with metacognitive intervention and CT skills intervention. Thus, it improves how people think about thinking as well as about the results achieved, since metacognition supports decision-making and final evaluation about proper strategies to solve problems.

Discussion and Conclusions

The general aim of our study was to know whether a critical thinking intervention program can also influence metacognitive processes. We know that our teaching methodology improves cross-sectional skills in argumentation, explanation, decision-making, and problem-solving, but we do not know if this intervention also directly or indirectly influences metacognition. In our study, we sought to shed light on this little-known point. If we bear in mind the centrality of how we think about thinking for our cognitive machinery to function properly and reach the best results possible in the problems we face, it is hard to understand the lack of attention given to this theme in other research. Our study aimed to remedy this deficiency somewhat.

As said in the introduction, metacognition has to do with consciousness, planning, and regulation of our activities. These mechanisms, as understood by many authors, have a blended cognitive and non-cognitive nature, which is a conceptual imprecision; what is known, though, is the enormous influence they exert on fundamental thinking processes. However, there is a large knowledge gap about the factors which make metacognition itself improve. This second research lacuna is what we have partly aimed to shrink here as well with this study. Our guide has been the idea of knowing how to improve metacognition from a teaching initiative and from the improvement of fundamental critical thinking skills.

Our study has shed light in both directions, albeit in a modest way, since its design does not allow us to unequivocally discern some of the results obtained. However, we believe that the data provide relevant information to know more about existing relations between skills and metacognition, something which has seen little contrast. These results allow us to better describe these relations, guiding the design of future studies which can better discern their roles. Our data have shown that this relation is bidirectional, so that metacognition improves thinking skills and vice versa. It remains to establish a sequence of independent factors to avoid this confusion, something which the present study has aided with to be able to design future research in this area.

As the results show, total differences in almost all metaknowledge dimensions are higher after intervention; specifically, we see how in the knowledge factor the declarative, procedural, and conditional dimensions improve in post-measurements. This improvement moves in the direction we predicted. However, the cognitive regulation dimension only shows differences in the total, and in the planning, organization, and regulation dimensions. We can see how the declarative knowledge dimensions are more sensitive than the procedural ones to change, and within the latter, the dimensions over which we have more control are also more sensitive. With troubleshooting and evaluation, no changes are seen after intervention. We may interpret this lack of effects as being due to how everything referring to evaluating results is highly determined by calibration capacity, which is influenced by personality factors not considered in our study. Regarding critical thinking, we found differences in all its dimensions, with higher scores following intervention. We can tentatively state that this improved performance can be influenced not only by interventions, but also by the metacognitive improvement observed, although our study was incapable of separating these two factors, and merely established their relation.

As we know, when people think about thinking they can always increase their critical thinking performance. Being conscious of the mechanisms used in problem-solving and decision-making always contributes to improving their execution. However, we need to go into other topics to identify the specific determinants of these effects. Does performance improve because skills are metacognitively benefited? If so, how? Is it only the levels of consciousness which aid in regulating and planning execution, or do other factors also have to participate? What level of thinking skills can be beneficial for metacognition? At what skill level does this metacognitive change happen? And finally, we know that teaching is always metacognitive to the extent that it helps us know how to proceed with sufficient clarity, but does performance level modify consciousness or regulation level of our action? Do bad results paralyze metacognitive activity while good ones stimulate it? Ultimately, all of these open questions are the future implications which our current study has suggested. We believe them to be exciting and necessary challenges, which must be faced sooner rather than later. Finally, we cannot forget the implications derived from specific metacognitive instruction, as presented at the start of this study. An intervention of this type should also help us partially answer the aforementioned questions, as we cannot obviate what can be modified or changed by direct metacognition instruction.

Data Availability Statement

Ethics statement.

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

SR and CS contributed to the conception and design of the study. SR organized the database, performed the statistical analysis, and wrote the first draft of the manuscript. SR, CS, and CO wrote sections of the manuscript. All authors contributed to the article and approved the submitted version.

This study was partly financed by the Project FONDECYT no. 11220056 ANID-Chile.

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

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

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Center for Teaching

Metacognition.

Thinking about One’s Thinking |   Putting Metacognition into Practice

Thinking about One’s Thinking

Initially studied for its development in young children (Baker & Brown, 1984; Flavell, 1985), researchers soon began to look at how experts display metacognitive thinking and how, then, these thought processes can be taught to novices to improve their learning (Hatano & Inagaki, 1986).  In How People Learn , the National Academy of Sciences’ synthesis of decades of research on the science of learning, one of the three key findings of this work is the effectiveness of a “‘metacognitive’ approach to instruction” (Bransford, Brown, & Cocking, 2000, p. 18).

Metacognitive practices increase students’ abilities to transfer or adapt their learning to new contexts and tasks (Bransford, Brown, & Cocking, p. 12; Palincsar & Brown, 1984; Scardamalia et al., 1984; Schoenfeld, 1983, 1985, 1991).  They do this by gaining a level of awareness above the subject matter : they also think about the tasks and contexts of different learning situations and themselves as learners in these different contexts.  When Pintrich (2002) asserts that “Students who know about the different kinds of strategies for learning, thinking, and problem solving will be more likely to use them” (p. 222), notice the students must “know about” these strategies, not just practice them.  As Zohar and David (2009) explain, there must be a “ conscious meta-strategic level of H[igher] O[rder] T[hinking]” (p. 179).

Metacognitive practices help students become aware of their strengths and weaknesses as learners, writers, readers, test-takers, group members, etc.  A key element is recognizing the limit of one’s knowledge or ability and then figuring out how to expand that knowledge or extend the ability. Those who know their strengths and weaknesses in these areas will be more likely to “actively monitor their learning strategies and resources and assess their readiness for particular tasks and performances” (Bransford, Brown, & Cocking, p. 67).

The absence of metacognition connects to the research by Dunning, Johnson, Ehrlinger, and Kruger on “Why People Fail to Recognize Their Own Incompetence” (2003).  They found that “people tend to be blissfully unaware of their incompetence,” lacking “insight about deficiencies in their intellectual and social skills.”  They identified this pattern across domains—from test-taking, writing grammatically, thinking logically, to recognizing humor, to hunters’ knowledge about firearms and medical lab technicians’ knowledge of medical terminology and problem-solving skills (p. 83-84).  In short, “if people lack the skills to produce correct answers, they are also cursed with an inability to know when their answers, or anyone else’s, are right or wrong” (p. 85).  This research suggests that increased metacognitive abilities—to learn specific (and correct) skills, how to recognize them, and how to practice them—is needed in many contexts.

Putting Metacognition into Practice

In “ Promoting Student Metacognition ,” Tanner (2012) offers a handful of specific activities for biology classes, but they can be adapted to any discipline. She first describes four assignments for explicit instruction (p. 116):

• Preassessments—Encouraging Students to Examine Their Current Thinking: “What do I already know about this topic that could guide my learning?”

• Retrospective Postassessments—Pushing Students to Recognize Conceptual Change: “Before this course, I thought evolution was… Now I think that evolution is ….” or “How is my thinking changing (or not changing) over time?”
• Reflective Journals—Providing a Forum in Which Students Monitor Their Own Thinking: “What about my exam preparation worked well that I should remember to do next time? What did not work so well that I should not do next time or that I should change?”

Next are recommendations for developing a “classroom culture grounded in metacognition” (p. 116-118):

• Giving Students License to Identify Confusions within the Classroom Culture:  ask students what they find confusing, acknowledge the difficulties
• Integrating Reflection into Credited Course Work: integrate short reflection (oral or written) that ask students what they found challenging or what questions arose during an assignment/exam/project
• Metacognitive Modeling by the Instructor for Students: model the thinking processes involved in your field and sought in your course by being explicit about “how you start, how you decide what to do first and then next, how you check your work, how you know when you are done” (p. 118)

To facilitate these activities, she also offers three useful tables:

• Questions for students to ask themselves as they plan, monitor, and evaluate their thinking within four learning contexts—in class, assignments, quizzes/exams, and the course as a whole (p. 115)
• Prompts for integrating metacognition into discussions of pairs during clicker activities, assignments, and quiz or exam preparation (p. 117)
• Questions to help faculty metacognitively assess their own teaching (p. 119)

Weimer’s “ Deep Learning vs. Surface Learning: Getting Students to Understand the Difference ” (2012) offers additional recommendations for developing students’ metacognitive awareness and improvement of their study skills:

“[I]t is terribly important that in explicit and concerted ways we make students aware of themselves as learners. We must regularly ask, not only ‘What are you learning?’ but ‘How are you learning?’ We must confront them with the effectiveness (more often ineffectiveness) of their approaches. We must offer alternatives and then challenge students to test the efficacy of those approaches. ” (emphasis added)

She points to a tool developed by Stanger-Hall (2012, p. 297) for her students to identify their study strategies, which she divided into “ cognitively passive ” (“I previewed the reading before class,” “I came to class,” “I read the assigned text,” “I highlighted the text,” et al) and “ cognitively active study behaviors ” (“I asked myself: ‘How does it work?’ and ‘Why does it work this way?’” “I wrote my own study questions,” “I fit all the facts into a bigger picture,” “I closed my notes and tested how much I remembered,” et al) .  The specific focus of Stanger-Hall’s study is tangential to this discussion, 1 but imagine giving students lists like hers adapted to your course and then, after a major assignment, having students discuss which ones worked and which types of behaviors led to higher grades. Even further, follow Lovett’s advice (2013) by assigning “exam wrappers,” which include students reflecting on their previous exam-preparation strategies, assessing those strategies and then looking ahead to the next exam, and writing an action plan for a revised approach to studying. A common assignment in English composition courses is the self-assessment essay in which students apply course criteria to articulate their strengths and weaknesses within single papers or over the course of the semester. These activities can be adapted to assignments other than exams or essays, such as projects, speeches, discussions, and the like.

As these examples illustrate, for students to become more metacognitive, they must be taught the concept and its language explicitly (Pintrich, 2002; Tanner, 2012), though not in a content-delivery model (simply a reading or a lecture) and not in one lesson. Instead, the explicit instruction should be “designed according to a knowledge construction approach,” or students need to recognize, assess, and connect new skills to old ones, “and it needs to take place over an extended period of time” (Zohar & David, p. 187).  This kind of explicit instruction will help students expand or replace existing learning strategies with new and more effective ones, give students a way to talk about learning and thinking, compare strategies with their classmates’ and make more informed choices, and render learning “less opaque to students, rather than being something that happens mysteriously or that some students ‘get’ and learn and others struggle and don’t learn” (Pintrich, 2002, p. 223).

• What to Expect (when reading philosophy)
• The Ultimate Goal (of reading philosophy)
• Basic Good Reading Behaviors
• Important Background Information, or discipline- and course-specific reading practices, such as “reading for enlightenment” rather than information, and “problem-based classes” rather than historical or figure-based classes
• A Three-Part Reading Process (pre-reading, understanding, and evaluating)
• Flagging, or annotating the reading
• Linear vs. Dialogical Writing (Philosophical writing is rarely straightforward but instead “a monologue that contains a dialogue” [p. 365].)

What would such a handout look like for your discipline?

Students can even be metacognitively prepared (and then prepare themselves) for the overarching learning experiences expected in specific contexts . Salvatori and Donahue’s The Elements (and Pleasures) of Difficulty (2004) encourages students to embrace difficult texts (and tasks) as part of deep learning, rather than an obstacle.  Their “difficulty paper” assignment helps students reflect on and articulate the nature of the difficulty and work through their responses to it (p. 9).  Similarly, in courses with sensitive subject matter, a different kind of learning occurs, one that involves complex emotional responses.  In “ Learning from Their Own Learning: How Metacognitive and Meta-affective Reflections Enhance Learning in Race-Related Courses ” (Chick, Karis, & Kernahan, 2009), students were informed about the common reactions to learning about racial inequality (Helms, 1995; Adams, Bell, & Griffin, 1997; see student handout, Chick, Karis, & Kernahan, p. 23-24) and then regularly wrote about their cognitive and affective responses to specific racialized situations.  The students with the most developed metacognitive and meta-affective practices at the end of the semester were able to “clear the obstacles and move away from” oversimplified thinking about race and racism ”to places of greater questioning, acknowledging the complexities of identity, and redefining the world in racial terms” (p. 14).

Ultimately, metacognition requires students to “externalize mental events” (Bransford, Brown, & Cocking, p. 67), such as what it means to learn, awareness of one’s strengths and weaknesses with specific skills or in a given learning context, plan what’s required to accomplish a specific learning goal or activity, identifying and correcting errors, and preparing ahead for learning processes.

————————

1 Students who were tested with short answer in addition to multiple-choice questions on their exams reported more cognitively active behaviors than those tested with just multiple-choice questions, and these active behaviors led to improved performance on the final exam.

• Adams, Maurianne, Bell, Lee Ann, and Griffin, Pat. (1997). Teaching for diversity and social justice: A sourcebook . New York: Routledge.
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• Baker, Linda, and Brown, Ann L. (1984). Metacognitive skills and reading.  In Paul David Pearson, Michael L. Kamil, Rebecca Barr, & Peter Mosenthal (Eds.), Handbook of research in reading: Volume III (pp. 353–395).  New York: Longman.
• Brown, Ann L. (1980). Metacognitive development and reading. In Rand J. Spiro, Bertram C. Bruce, and William F. Brewer, (Eds.), Theoretical issues in reading comprehension: Perspectives from cognitive psychology, linguistics, artificial intelligence, and education (pp. 453-482). Hillsdale, NJ: Erlbaum.
• Chick, Nancy, Karis, Terri, and Kernahan, Cyndi. (2009). Learning from their own learning: how metacognitive and meta-affective reflections enhance learning in race-related courses . International Journal for the Scholarship of Teaching and Learning, 3(1). 1-28.
• Commander, Nannette Evans, and Valeri-Gold, Marie. (2001). The learning portfolio: A valuable tool for increasing metacognitive awareness . The Learning Assistance Review, 6 (2), 5-18.
• Concepción, David. (2004). Reading philosophy with background knowledge and metacognition . Teaching Philosophy , 27 (4). 351-368.
• Dunning, David, Johnson, Kerri, Ehrlinger, Joyce, and Kruger, Justin. (2003) Why people fail to recognize their own incompetence . Current Directions in Psychological Science, 12 (3). 83-87.
• Flavell,  John H. (1985). Cognitive development. Englewood Cliffs, NJ: Prentice Hall.
• Hatano, Giyoo and Inagaki, Kayoko. (1986). Two courses of expertise. In Harold Stevenson, Azuma, Horishi, and Hakuta, Kinji (Eds.), Child development and education in Japan, New York: W.H. Freeman.
• Helms, Janet E. (1995). An update of Helms’ white and people of color racial identity models . In J.G. Ponterotto, Joseph G., Casas, Manuel, Suzuki, Lisa A., and Alexander, Charlene M. (Eds.), Handbook of multicultural counseling (pp. 181-198) . Thousand Oaks, CA: Sage.
• Lovett, Marsha C. (2013). Make exams worth more than the grade. In Matthew Kaplan, Naomi Silver, Danielle LaVague-Manty, and Deborah Meizlish (Eds.), Using reflection and metacognition to improve student learning: Across the disciplines, across the academy . Sterling, VA: Stylus.
• Palincsar, Annemarie Sullivan, and Brown, Ann L. (1984). Reciprocal teaching of comprehension-fostering and comprehension-monitoring activities . Cognition and Instruction, 1 (2). 117-175.
• Pintrich, Paul R. (2002). The Role of metacognitive knowledge in learning, teaching, and assessing . Theory into Practice, 41 (4). 219-225.
• Salvatori, Mariolina Rizzi, and Donahue, Patricia. (2004). The Elements (and pleasures) of difficulty . New York: Pearson-Longman.
• Scardamalia, Marlene, Bereiter, Carl, and Steinbach, Rosanne. (1984). Teachability of reflective processes in written composition . Cognitive Science , 8, 173-190.
• Schoenfeld, Alan H. (1991). On mathematics as sense making: An informal attack on the fortunate divorce of formal and informal mathematics. In James F. Voss, David N. Perkins, and Judith W. Segal (Eds.), Informal reasoning and education (pp. 311-344). Hillsdale, NJ: Erlbaum.
• Stanger-Hall, Kathrin F. (2012). Multiple-choice exams: An obstacle for higher-level thinking in introductory science classes . Cell Biology Education—Life Sciences Education, 11(3), 294-306.
• Tanner, Kimberly D.  (2012). Promoting student metacognition . CBE—Life Sciences Education, 11, 113-120.
• Weimer, Maryellen.  (2012, November 19). Deep learning vs. surface learning: Getting students to understand the difference . Retrieved from the Teaching Professor Blog from http://www.facultyfocus.com/articles/teaching-professor-blog/deep-learning-vs-surface-learning-getting-students-to-understand-the-difference/ .
• Zohar, Anat, and David, Adi Ben. (2009). Paving a clear path in a thick forest: a conceptual analysis of a metacognitive component . Metacognition Learning , 4 , 177-195.

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• Published: 08 June 2021

Metacognition: ideas and insights from neuro- and educational sciences

• Damien S. Fleur   ORCID: orcid.org/0000-0003-4836-5255 1 , 2 ,
• Bert Bredeweg   ORCID: orcid.org/0000-0002-5281-2786 1 , 3 &
• Wouter van den Bos 2 , 4  

npj Science of Learning volume  6 , Article number:  13 ( 2021 ) Cite this article

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Metacognition comprises both the ability to be aware of one’s cognitive processes (metacognitive knowledge) and to regulate them (metacognitive control). Research in educational sciences has amassed a large body of evidence on the importance of metacognition in learning and academic achievement. More recently, metacognition has been studied from experimental and cognitive neuroscience perspectives. This research has started to identify brain regions that encode metacognitive processes. However, the educational and neuroscience disciplines have largely developed separately with little exchange and communication. In this article, we review the literature on metacognition in educational and cognitive neuroscience and identify entry points for synthesis. We argue that to improve our understanding of metacognition, future research needs to (i) investigate the degree to which different protocols relate to the similar or different metacognitive constructs and processes, (ii) implement experiments to identify neural substrates necessary for metacognition based on protocols used in educational sciences, (iii) study the effects of training metacognitive knowledge in the brain, and (iv) perform developmental research in the metacognitive brain and compare it with the existing developmental literature from educational sciences regarding the domain-generality of metacognition.

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Introduction

Metacognition is defined as “thinking about thinking” or the ability to monitor and control one’s cognitive processes 1 and plays an important role in learning and education 2 , 3 , 4 . For instance, high performers tend to present better metacognitive abilities (especially control) than low performers in diverse educational activities 5 , 6 , 7 , 8 , 9 . Recently, there has been a lot of progress in studying the neural mechanisms of metacognition 10 , 11 , yet it is unclear at this point how these results may inform educational sciences or interventions. Given the potential benefits of metacognition, it is important to get a better understanding of how metacognition works and of how training can be useful.

The interest in bridging cognitive neuroscience and educational practices has increased in the past two decades, spanning a large number of studies grouped under the umbrella term of educational neuroscience 12 , 13 , 14 . With it, researchers have brought forward issues that are viewed as critical for the discipline to improve education. Recurring issues that may impede the relevance of neural insights for educational practices concern external validity 15 , 16 , theoretical discrepancies 17 and differences in terms of the domains of (meta)cognition operationalised (specific or general) 15 . This is important because, in recent years, brain research is starting to orient itself towards training metacognitive abilities that would translate into real-life benefits. However, direct links between metacognition in the brain and metacognition in domains such as education have still to be made. As for educational sciences, a large body of literature on metacognitive training is available, yet we still need clear insights about what works and why. While studies suggest that training metacognitive abilities results in higher academic achievement 18 , other interventions show mixed results 19 , 20 . Moreover, little is known about the long-term effects of, or transfer effects, of these interventions. A better understanding of the cognitive processes involved in metacognition and how they are expressed in the brain may provide insights in these regards.

Within cognitive neuroscience, there has been a long tradition of studying executive functions (EF), which are closely related to metacognitive processes 21 . Similar to metacognition, EF shows a positive relationship with learning at school. For instance, performance in laboratory tasks involving error monitoring, inhibition and working memory (i.e. processes that monitor and regulate cognition) are associated with academic achievement in pre-school children 22 . More recently, researchers have studied metacognition in terms of introspective judgements about performance in a task 10 . Although the neural correlates of such behaviour are being revealed 10 , 11 , little is known about how behaviour during such tasks relates to academic achievement.

Educational and cognitive neuroscientists study metacognition in different contexts using different methods. Indeed, while the latter investigate metacognition via behavioural task, the former mainly rely on introspective questionnaires. The extent to which these different operationalisations of metacognition match and reflect the same processes is unclear. As a result, the external validity of methodologies used in cognitive neuroscience is also unclear 16 . We argue that neurocognitive research on metacognition has a lot of potential to provide insights in mechanisms relevant in educational contexts, and that theoretical and methodological exchange between the two disciplines can benefit neuroscientific research in terms of ecological validity.

For these reasons, we investigate the literature through the lenses of external validity, theoretical discrepancies, domain generality and metacognitive training. Research on metacognition in cognitive neuroscience and educational sciences are reviewed separately. First, we investigate how metacognition is operationalised with respect to the common framework introduced by Nelson and Narens 23 (see Fig. 1 ). We then discuss the existing body of evidence regarding metacognitive training. Finally, we compare findings in both fields, highlight gaps and shortcomings, and propose avenues for research relying on crossovers of the two disciplines.

Meta-knowledge is characterised as the upward flow from object-level to meta-level. Meta-control is characterised as the downward flow from meta-level to object-level. Metacognition is therefore conceptualised as the bottom-up monitoring and top-down control of object-level processes. Adapted from Nelson and Narens’ cognitive psychology model of metacognition 23 .

In cognitive neuroscience, metacognition is divided into two main components 5 , 24 , which originate from the seminal works of Flavell on metamemory 25 , 26 . First, metacognitive knowledge (henceforth, meta-knowledge) is defined as the knowledge individuals have of their own cognitive processes and their ability to monitor and reflect on them. Second, metacognitive control (henceforth, meta-control) consists of someone’s self-regulatory mechanisms, such as planning and adapting behaviour based on outcomes 5 , 27 . Following Nelson and Narens’ definition 23 , meta-knowledge is characterised as the flow and processing of information from the object level to the meta-level, and meta-control as the flow from the meta-level to the object level 28 , 29 , 30 (Fig. 1 ). The object-level encompasses cognitive functions such as recognition and discrimination of objects, decision-making, semantic encoding, and spatial representation. On the meta-level, information originating from the object level is processed and top-down regulation on object-level functions is imposed 28 , 29 , 30 .

Educational researchers have mainly investigated metacognition through the lens of Self-Regulated Learning theory (SRL) 3 , 4 , which shares common conceptual roots with the theoretical framework used in cognitive neuroscience but varies from it in several ways 31 . First, SRL is constrained to learning activities, usually within educational settings. Second, metacognition is merely one of three components, with “motivation to learn” and “behavioural processes”, that enable individuals to learn in a self-directed manner 3 . In SRL, metacognition is defined as setting goals, planning, organising, self-monitoring and self-evaluating “at various points during the acquisition” 3 . The distinction between meta-knowledge and meta-control is not formally laid down although reference is often made to a “self-oriented feedback loop” describing the relationship between reflecting and regulating processes that resembles Nelson and Narens’ model (Fig. 1 ) 3 , 23 . In order to facilitate the comparison of operational definitions, we will refer to meta-knowledge in educational sciences when protocols operationalise self-awareness and knowledge of strategies, and to meta-control when they operationalise the selection and use of learning strategies and planning. For an in-depth discussion on metacognition and SRL, we refer to Dinsmore et al. 31 .

Metacognition in cognitive neuroscience

Operational definitions.

In cognitive neuroscience, research in metacognition is split into two tracks 32 . One track mainly studies meta-knowledge by investigating the neural basis of introspective judgements about one’s own cognition (i.e., metacognitive judgements), and meta-control with experiments involving cognitive offloading. In these experiments, subjects can perform actions such as set reminders, making notes and delegating tasks 33 , 34 , or report their desire for them 35 . Some research has investigated how metacognitive judgements can influence subsequent cognitive behaviour (i.e., a downward stream from the meta-level to the object level), but only one study so far has explored how this relationship is mapped in the brain 35 . In the other track, researchers investigate EF, also referred to as cognitive control 30 , 36 , which is closely related to metacognition. Note however that EF are often not framed in metacognitive terms in the literature 37 (but see ref. 30 ). For the sake of concision, we limit our review to operational definitions that have been used in neuroscientific studies.

Metacognitive judgements

Cognitive neuroscientists have been using paradigms in which subjects make judgements on how confident they are with regards to their learning of some given material 10 . These judgements are commonly referred to as metacognitive judgements , which can be viewed as a form of meta-knowledge (for reviews see Schwartz 38 and Nelson 39 ). Historically, researchers mostly resorted to paradigms known as Feelings of Knowing (FOK) 40 and Judgements of Learning (JOL) 41 . FOK reflect the belief of a subject to knowing the answer to a question or a problem and being able to recognise it from a list of alternatives, despite being unable to explicitly recall it 40 . Here, metacognitive judgement is thus made after retrieval attempt. In contrast, JOL are prospective judgements during learning of one’s ability to successfully recall an item on subsequent testing 41 .

More recently, cognitive neuroscientists have used paradigms in which subjects make retrospective metacognitive judgements on their performance in a two-alternative Forced Choice task (2-AFC) 42 . In 2-AFCs, subjects are asked to choose which of two presented options has the highest criterion value. Different domains can be involved, such as perception (e.g., visual or auditory) and memory. For example, subjects may be instructed to visually discriminate which one of two boxes contains more dots 43 , identify higher contrast Gabor patches 44 , or recognise novel words from words that were previously learned 45 (Fig. 2 ). The subjects engage in metacognitive judgements by rating how confident they are relative to their decision in the task. Based on their responses, one can evaluate a subject’s metacognitive sensitivity (the ability to discriminate one’s own correct and incorrect judgements), metacognitive bias (the overall level of confidence during a task), and metacognitive efficiency (the level of metacognitive sensitivity when controlling for task performance 46 ; Fig. 3 ). Note that sensitivity and bias are independent aspects of metacognition, meaning that two subjects may display the same levels of metacognitive sensitivity, but one may be biased towards high confidence while the other is biased towards low confidence. Because metacognitive sensitivity is affected by the difficulty of the task (one subject tends to display greater metacognitive sensitivity in easy tasks than difficult ones and different subjects may find a task more or less easy), metacognitive efficiency is an important measure as it allows researchers to compare metacognitive abilities between subjects and between domains. The most commonly used methods to assess metacognitive sensitivity during retrospective judgements are the receiver operating curve (ROC) and meta- d ′. 46 Both derive from signal detection theory (SDT) 47 which allows Type 1 sensitivity, or d’ ′ (how a subject can discriminate between stimulus alternatives, i.e. object-level processes) to be differentiated from metacognitive sensitivity (a judgement on the correctness of this decision) 48 . Importantly, only comparing meta- d ′ to d ′ seems to give reliable assessments metacognitive efficiency 49 . A ratio of 1 between meta- d’ ′ and d’ ′, indicates that a subject was perfectly able to discriminate between their correct and incorrect judgements. A ratio of 0.8 suggests that 80% of the task-related sensory evidence was available for the metacognitive judgements. Table 1 provides an overview of the different types of tasks and protocols with regards to the type of metacognitive process they operationalise. These operationalisations of meta-knowledge are used in combination with brain imaging methods (functional and structural magnetic resonance imaging; fMRI; MRI) to identify brain regions associated with metacognitive activity and metacognitive abilities 10 , 50 . Alternatively, transcranial magnetic stimulation (TMS) can be used to temporarily deactivate chosen brain regions and test whether this affects metacognitive abilities in given tasks 51 , 52 .

a Visual perception task: subjects choose the box containing the most (randomly generated) dots. Subjects then rate their confidence in their decision. b Memory task: subjects learn a list of words. In the next screen, they have to identify which of two words shown was present on the list. The subjects then rate their confidence in their decision.

The red and blue curves represent the distribution of confidence ratings for incorrect and correct trials, respectively. A larger distance between the two curves denotes higher sensitivity. Displacement to the left and right denote biases towards low confidence (low metacognitive bias) and high confidence (high metacognitive bias), respectively (retrieved from Fig. 1 in Fleming and Lau 46 ). We repeat the disclaimer of the original authors that this figure is not a statistically accurate description of correct and incorrect responses, which are typically not normally distributed 46 , 47 .

A recent meta-analysis analysed 47 neuroimaging studies on metacognition and identified a domain-general network associated with high vs. low confidence ratings in both decision-making tasks (perception 2-AFC) and memory tasks (JOL, FOK) 11 . This network includes the medial and lateral prefrontal cortex (mPFC and lPFC, respectively), precuneus and insula. In contrast, the right anterior dorsolateral PFC (dlPFC) was specifically involved in decision-making tasks, and the bilateral parahippocampal cortex was specific to memory tasks. In addition, prospective judgements were associated with the posterior mPFC, left dlPFC and right insula, whereas retrospective judgements were associated with bilateral parahippocampal cortex and left inferior frontal gyrus. Finally, emerging evidence suggests a role of the right rostrolateral PFC (rlPFC) 53 , 54 , anterior PFC (aPFC) 44 , 45 , 55 , 56 , dorsal anterior cingulate cortex (dACC) 54 , 55 and precuneus 45 , 55 in metacognitive sensitivity (meta- d ′, ROC). In addition, several studies suggest that the aPFC relates to metacognition specifically in perception-related 2-AFC tasks, whereas the precuneus is engaged specifically in memory-related 2-AFC tasks 45 , 55 , 56 . This may suggest that metacognitive processes engage some regions in a domain-specific manner, while other regions are domain-general. For educational scientists, this could mean that some domains of metacognition may be more relevant for learning and, granted sufficient plasticity of the associated brain regions, that targeting them during interventions may show more substantial benefits. Note that rating one’s confidence and metacognitive sensitivity likely involve additional, peripheral cognitive processes instead of purely metacognitive ones. These regions are therefore associated with metacognition but not uniquely per se. Notably, a recent meta-analysis 50 suggests that domain-specific and domain-general signals may rather share common circuitry, but that their neural signature varies depending on the type of task or activity, showing that domain-generality in metacognition is complex and still needs to be better understood.

In terms of the role of metacognitive judgements on future behaviour, one study found that brain patterns associated with the desire for cognitive offloading (i.e., meta-control) partially overlap with those associated with meta-knowledge (metacognitive judgements of confidence), suggesting that meta-control is driven by either non-metacognitive, in addition to metacognitive, processes or by a combination of different domain-specific meta-knowledge processes 35 .

Executive function

In EF, processes such as error detection/monitoring and effort monitoring can be related to meta-knowledge while error correction, inhibitory control, and resource allocation can be related to meta-control 36 . To activate these processes, participants are asked to perform tasks in laboratory settings such as Flanker tasks, Stroop tasks, Demand Selection tasks and Motion Discrimination tasks (Fig. 4 ). Neural correlates of EF are investigated by having subjects perform such tasks while their brain activity is recorded with fMRI or electroencephalography (EEG). Additionally, patients with brain lesions can be tested against healthy participants to evaluate the functional role of the impaired regions 57 .

a Flanker task: subjects indicate the direction to which the arrow in the middle points. b Stroop task: subjects are presented with the name of colour printed in a colour that either matches or mismatches the name. Subjects are asked to give the name of the written colour or the printed colour. c Motion Discrimination task: subjects have to determine in which direction the dots are going with variating levels of noise. d Example of a Demand Selection task: in both options subjects have to switch between two tasks. Task one, subjects determine whether the number shown is higher or lower than 5. Task two, subjects determine whether the number is odd or even. The two options (low and high demand) differ in their degree of task switching, meaning the effort required. Subjects are allowed to switch between the two options. Note, the type of task is solely indicated by the colour of the number and that the subjects are not explicitly told about the difference in effort between the two options (retrieved from Fig. 1c in Froböse et al. 58 ).

In a review article on the neural basis of EF (in which they are defined as meta-control), Shimamura argues that a network of regions composed of the aPFC, ACC, ventrolateral PFC (vlPFC) and dlPFC is involved in the regulations of cognition 30 . These regions are not only interconnected but are also intricately connected to cortical and subcortical regions outside of the PFC. The vlPFC was shown to play an important role in “selecting and maintaining information in working memory”, whereas the dlPFC is involved in “manipulating and updating information in working memory” 30 . The ACC has been proposed to monitor cognitive conflict (e.g. in a Stroop task or a Flanker task), and the dlPFC to regulate it 58 , 59 . In particular, activity in the ACC in conflict monitoring (meta-knowledge) seems to contribute to control of cognition (meta-control) in the dlPFC 60 , 61 and to “bias behavioural decision-making toward cognitively efficient tasks and strategies” (p. 356) 62 . In a recent fMRI study, subjects performed a motion discrimination task (Fig. 4c ) 63 . After deciding on the direction of the motion, they were presented additional motion (i.e. post-decisional evidence) and then were asked to rate their confidence in their initial choice. The post-decisional evidence was encoded in the activity of the posterior medial frontal cortex (pMFC; meta-knowledge), while lateral aPFC (meta-control) modulated the impact of this evidence on subsequent confidence rating 63 . Finally, results from a meta-analysis study on cognitive control identified functional connectivity between the pMFC, associated with monitoring and informing other regions about the need for regulation, and the lPFC that would effectively regulate cognition 64 .

Online vs. offline metacognition

While the processes engaged during tasks such as those used in EF research can be considered as metacognitive in the sense that they are higher-order functions that monitor and control lower cognitive processes, scientists have argued that they are not functionally equivalent to metacognitive judgements 10 , 11 , 65 , 66 . Indeed, engaging in metacognitive judgements requires subjects to reflect on past or future activities. As such, metacognitive judgements can be considered as offline metacognitive processes. In contrast, high-order processes involved in decision-making tasks such as used in EF research are arguably largely made on the fly, or online , at a rapid pace and subjects do not need to reflect on their actions to perform them. Hence, we propose to explicitly distinguish online and offline processes. Other researchers have shared a similar view and some have proposed models for metacognition that make similar distinctions 65 , 66 , 67 , 68 . The functional difference between online and offline metacognition is supported by some evidence. For instance, event-related brain potential (ERP) studies suggest that error negativities are associated with error detection in general, whereas an increased error positivity specifically encodes error that subjects could report upon 69 , 70 . Furthermore, brain-imaging studies suggest that the MFC and ACC are involved in online meta-knowledge, while the aPFC and lPFC seem to be activated when subjects engage in more offline meta-knowledge and meta-control, respectively 63 , 71 , 72 . An overview of the different tasks can be found in Table 1 and a list of different studies on metacognition can be found in Supplementary Table 1 (organised in terms of the type of processes investigated, the protocols and brain measures used, along with the brain regions identified). Figure 5 illustrates the different brain regions associated with meta-knowledge and meta-control, distinguishing between what we consider to be online and offline processes. This distinction is often not made explicitly but it will be specifically helpful when building bridges between cognitive neuroscience and educational sciences.

The regions are divided into online meta-knowledge and meta-control, and offline meta-knowledge and meta-control following the distinctions introduced earlier. Some regions have been reported to be related to both offline and online processes and are therefore given a striped pattern.

Training metacognition

There are extensive accounts in the literature of efforts to improve EF components such as inhibitory control, attention shifting and working memory 22 . While working memory does not directly reflect metacognitive abilities, its training is often hypothesised to improve general cognitive abilities and academic achievement. However, most meta-analyses found that training methods lead only to weak, non-lasting effects on cognitive control 73 , 74 , 75 . One meta-analysis did find evidence of near-transfer following EF training in children (in particular working memory, inhibitory control and cognitive flexibility), but found no evidence of far-transfer 20 . According to this study, training on one component leads to improved abilities in that same component but not in other EF components. Regarding adults, however, one meta-analysis suggests that EF training in general and working memory training specifically may both lead to significant near- and far-transfer effects 76 . On a neural level, a meta-analysis showed that cognitive training resulted in decreased brain activity in brain regions associated with EF 77 . According to the authors, this indicates that “training interventions reduce demands on externally focused attention” (p. 193) 77 .

With regards to meta-knowledge, several studies have reported increased task-related metacognitive abilities after training. For example, researchers found that subjects who received feedback on their metacognitive judgements regarding a perceptual decision-making task displayed better metacognitive accuracy, not only in the trained task but also in an untrained memory task 78 . Related, Baird and colleagues 79 found that a two-week mindfulness meditation training lead to enhanced meta-knowledge in the memory domain, but not the perceptual domain. The authors link these results to evidence of increased grey matter density in the aPFC in meditation practitioners.

Research on metacognition in cognitive science has mainly been studied through the lens of metacognitive judgements and EF (specifically performance monitoring and cognitive control). Meta-knowledge is commonly activated in subjects by asking them to rate their confidence in having successfully performed a task. A distinction is made between metacognitive sensitivity, metacognitive bias and metacognitive efficacy. Monitoring and regulating processes in EF are mainly operationalised with behavioural tasks such as Flanker tasks, Stroop tasks, Motion Discrimination tasks and Demand Selection tasks. In addition, metacognitive judgements can be viewed as offline processes in that they require the subject to reflect on her cognition and develop meta-representations. In contrast, EF can be considered as mostly online metacognitive processes because monitoring and regulation mostly happen rapidly without the need for reflective thinking.

Although there is some evidence for domain specificity, other studies have suggested that there is a single network of regions involved in all meta-cognitive tasks, but differentially activated in different task contexts. Comparing research on meta-knowledge and meta-control also suggest that some regions play a crucial role in both knowledge and regulation (Fig. 5 ). We have also identified a specific set of regions that are involved in either offline or online meta-knowledge. The evidence in favour of metacognitive training, while mixed, is interesting. In particular, research on offline meta-knowledge training involving self-reflection and metacognitive accuracy has shown some promising results. The regions that show structural changes after training, were those that we earlier identified as being part of the metacognition network. EF training does seem to show far-transfer effects at least in adults, but the relevance for everyday life activity is still unclear.

One major limitation of current research in metacognition is ecological validity. It is unclear to what extent the operationalisations reviewed above reflect real-life metacognition. For instance, are people who can accurately judge their performance on a behavioural task also able to accurately assess how they performed during an exam? Are people with high levels of error regulation and inhibitory control able to learn more efficiently? Note that criticism on the ecological validity of neurocognitive operationalisations extends beyond metacognition research 16 . A solution for improving validity may be to compare operationalisations of metacognition in cognitive neuroscience with the ones in educational sciences, which have shown clear links with learning in formal education. This also applies to metacognitive training.

Metacognition in educational sciences

The most popular protocols used to measure metacognition in educational sciences are self-report questionnaires or interviews, learning journals and thinking-aloud protocols 31 , 80 . During interviews, subjects are asked to answer questions regarding hypothetical situations 81 . In learning journals, students write about their learning experience and their thoughts on learning 82 , 83 . In thinking-aloud protocols, subjects are asked to verbalise their thoughts while performing a problem-solving task 80 . Each of these instruments can be used to study meta-knowledge and meta-control. For instance, one of the most widely used questionnaires, the Metacognitive Awareness Inventory (MAI) 42 , operationalises “Flavellian” metacognition and has dedicated scales for meta-knowledge and meta-control (also popular are the MSLQ 84 and LASSI 85 which operate under SRL). The meta-knowledge scale of the MAI operationalises knowledge of strategies (e.g., “ I am aware of what strategies I use when I study ”) and self-awareness (e.g., “ I am a good judge of how well I understand something ”); the meta-control scale operationalises planning (e.g., “ I set a goal before I begin a task ”) and use of learning strategies (e.g., “ I summarize what I’ve learned after I finish ”). Learning journals, self-report questionnaires and interviews involve offline metacognition. Thinking aloud, though not engaging the same degree self-reflection, also involves offline metacognition in the sense that online processes are verbalised, which necessitate offline processing (see Table 1 for an overview and Supplementary Table 2 for more details).

More recently, methodologies borrowed from cognitive neuroscience have been introduced to study EF in educational settings 22 , 86 . In particular, researchers used classic cognitive control tasks such as the Stroop task (for a meta-analysis 86 ). Most of the studied components are related to meta-control and not meta-knowledge. For instance, the BRIEF 87 is a questionnaire completed by parents and teachers which assesses different subdomains of EF: (1) inhibition, shifting, and emotional control which can be viewed as online metacognitive control, and (2) planning, organisation of materials, and monitoring, which can be viewed as offline meta-control 87 .

Assessment of metacognition is usually compared against metrics of academic performance such as grades or scores on designated tasks. A recent meta-analysis reported a weak correlation of self-report questionnaires and interviews with academic performance whereas think-aloud protocols correlated highly 88 . Offline meta-knowledge processes operationalised by learning journals were found to be positively associated with academic achievement when related to reflection on learning activities but negatively associated when related to reflection on learning materials, indicating that the type of reflection is important 89 . EF have been associated with abilities in mathematics (mainly) and reading comprehension 86 . However, the literature points towards contrary directions as to what specific EF component is involved in academic achievement. This may be due to the different groups that were studied, to different operationalisations or to different theoretical underpinnings for EF 86 . For instance, online and offline metacognitive processes, which are not systematically distinguished in the literature, may play different roles in academic achievement. Moreover, the bulk of research focussed on young children with few studies on adolescents 86 and EF may play a role at varying extents at different stages of life.

A critical question in educational sciences is that of the nature of the relationship between metacognition and academic achievement to understand whether learning at school can be enhanced by training metacognitive abilities. Does higher metacognition lead to higher academic achievement? Do these features evolve in parallel? Developmental research provides valuable insights into the formation of metacognitive abilities that can inform training designs in terms of what aspect of metacognition should be supported and the age at which interventions may yield the best results. First, meta-knowledge seems to emerge around the age of 5, meta-control around 8, and both develop over the years 90 , with evidence for the development of meta-knowledge into adolescence 91 . Furthermore, current theories propose that meta-knowledge abilities are initially highly domain-dependent and gradually become more domain-independent as knowledge and experience are acquired and linked between domains 32 . Meta-control is believed to evolve in a similar fashion 90 , 92 .

Common methods used to train offline metacognition are direct instruction of metacognition, metacognitive prompts and learning journals. In addition, research has been done on the use of (self-directed) feedback as a means to induce self-reflection in students, mainly in computer-supported settings 93 . Interestingly, learning journals appear to be used for both assessing and fostering metacognition. Metacognitive instruction consists of teaching learners’ strategies to “activate” their metacognition. Metacognitive prompts most often consist of text pieces that are sent at specific times and that trigger reflection (offline meta-knowledge) on learning behaviour in the form of a question, hint or reminder.

Meta-analyses have investigated the effects of direct metacognitive instruction on students’ use of learning strategies and academic outcomes 18 , 94 , 95 . Their findings show that metacognitive instruction can have a positive effect on learning abilities and achievement within a population ranging from primary schoolers to university students. In particular, interventions lead to the highest effect sizes when they both (i) instructed a combination of metacognitive strategies with an emphasis on planning strategies (offline meta-control) and (ii) “provided students with knowledge about strategies” (offline meta-knowledge) and “illustrated the benefits of applying the trained strategies, or even stimulated metacognitive reasoning” (p.114) 18 . The longer the duration of the intervention, the more effective they were. The strongest effects on academic performance were observed in the context of mathematics, followed by reading and writing.

While metacognitive prompts and learning journals make up the larger part of the literature on metacognitive training 96 , meta-analyses that specifically investigate their effectiveness have yet to be performed. Nonetheless, evidence suggests that such interventions can be successful. Researchers found that metacognitive prompts fostered the use of metacognitive strategies (offline meta-control) and that the combination of cognitive and metacognitive prompts improved learning outcomes 97 . Another experiment showed that students who received metacognitive prompts performed more metacognitive activities inside the learning environment and displayed better transfer performance immediately after the intervention 98 . A similar study using self-directed prompts showed enhanced transfer performance that was still observable 3 weeks after the intervention 99 .

Several studies suggest that learning journals can positively enhance metacognition. Subjects who kept a learning journal displayed stronger high meta-control and meta-knowledge on learning tasks and tended to reach higher academic outcomes 100 , 101 , 102 . However, how the learning journal is used seems to be critical; good instructions are crucial 97 , 103 , and subjects who simply summarise their learning activity benefit less from the intervention than subjects who reflect about their knowledge, learning and learning goals 104 . An overview of studies using learning journals and metacognitive prompts to train metacognition can be found in Supplementary Table 3 .

In recent years, educational neuroscience researchers have tried to determine whether training and improvements in EF can lead to learning facilitation and higher academic achievement. Training may consist of having students continually perform behavioural tasks either in the lab, at home, or at school. Current evidence in favour of training EF is mixed, with only anecdotal evidence for positive effects 105 . A meta-analysis did not show evidence for a causal relationship between EF and academic achievement 19 , but suggested that the relationship is bidirectional, meaning that the two are “mutually supportive” 106 .

A recent review article has identified several gaps and shortcoming in the literature on metacognitive training 96 . Overall, research in metacognitive training has been mainly invested in developing learners’ meta-control rather than meta-knowledge. Furthermore, most of the interventions were done in the context of science learning. Critically, there appears to be a lack of studies that employed randomised control designs, such that the effects of metacognitive training intervention are often difficult to evaluate. In addition, research overwhelmingly investigated metacognitive prompts and learning journals in adults 96 , while interventions on EF mainly focused on young children 22 . Lastly, meta-analyses evaluating the effectiveness of metacognitive training have so far focused on metacognitive instruction on children. There is thus a clear disbalance between the meta-analyses performed and the scope of the literature available.

An important caveat of educational sciences research is that metacognition is not typically framed in terms of online and offline metacognition. Therefore, it can be unclear whether protocols operationalise online or offline processes and whether interventions tend to benefit more online or offline metacognition. There is also confusion in terms of what processes qualify as EF and definitions of it vary substantially 86 . For instance, Clements and colleagues mention work on SRL to illustrate research in EF in relation to academic achievement but the two spawn from different lines of research, one rooted in metacognition and socio-cognitive theory 31 and the other in the cognitive (neuro)science of decision-making. In addition, the MSLQ, as discussed above, assesses offline metacognition along with other components relevant to SRL, whereas EF can be mainly understood as online metacognition (see Table 1 ), which on the neural level may rely on different circuitry.

Investigating offline metacognition tends to be carried out in school settings whereas evaluating EF (e.g., Stroop task, and BRIEF) is performed in the lab. Common to all protocols for offline metacognition is that they consist of a form of self-report from the learner, either during the learning activity (thinking-aloud protocols) or after the learning activity (questionnaires, interviews and learning journals). Questionnaires are popular protocols due to how easy they are to administer but have been criticised to provide biased evaluations of metacognitive abilities. In contrast, learning journals evaluate the degree to which learners engage in reflective thinking and may therefore be less prone to bias. Lastly, it is unclear to what extent thinking-aloud protocols are sensitive to online metacognitive processes, such as on-the-fly error correction and effort regulation. The strength of the relationship between metacognitive abilities and academic achievement varies depending on how metacognition is operationalised. Self-report questionnaires and interviews are weakly related to achievement whereas thinking-aloud protocols and EF are strongly related to it.

Based on the well-documented relationship between metacognition and academic achievement, educational scientists hypothesised that fostering metacognition may improve learning and academic achievement, and thus performed metacognitive training interventions. The most prevalent training protocols are direct metacognitive instruction, learning journals, and metacognitive prompts, which aim to induce and foster offline metacognitive processes such as self-reflection, planning and selecting learning strategies. In addition, researchers have investigated whether training EF, either through tasks or embedded in the curriculum, results in higher academic proficiency and achievement. While a large body of evidence suggests that metacognitive instruction, learning journals and metacognitive prompts can successfully improve academic achievement, interventions designed around EF training show mixed results. Future research investigating EF training in different age categories may clarify this situation. These various degrees of success of interventions may indicate that offline metacognition is more easily trainable than online metacognition and plays a more important role in educational settings. Investigating the effects of different methods, offline and online, on the neural level, may provide researchers with insights into the trainability of different metacognitive processes.

In this article, we reviewed the literature on metacognition in educational sciences and cognitive neuroscience with the aim to investigate gaps in current research and propose ways to address them through the exchange of insights between the two disciplines and interdisciplinary approaches. The main aspects analysed were operational definitions of metacognition and metacognitive training, through the lens of metacognitive knowledge and metacognitive control. Our review also highlighted an additional construct in the form of the distinction between online metacognition (on the fly and largely automatic) and offline metacognition (slower, reflective and requiring meta-representations). In cognitive neuroscience, research has focused on metacognitive judgements (mainly offline) and EF (mainly online). Metacognition is operationalised with tasks carried out in the lab and are mapped onto brain functions. In contrast, research in educational sciences typically measures metacognition in the context of learning activities, mostly in schools and universities. More recently, EF has been studied in educational settings to investigate its role in academic achievement and whether training it may benefit learning. Evidence on the latter is however mixed. Regarding metacognitive training in general, evidence from both disciplines suggests that interventions fostering learners’ self-reflection and knowledge of their learning behaviour (i.e., offline meta-knowledge) may best benefit them and increase academic achievement.

We focused on four aspects of research that could benefit from an interdisciplinary approach between the two areas: (i) validity and reliability of research protocols, (ii) under-researched dimensions of metacognition, (iii) metacognitive training, and (iv) domain-specificity vs. domain generality of metacognitive abilities. To tackle these issue, we propose four avenues for integrated research: (i) investigate the degree to which different protocols relate to similar or different metacognitive constructs, (ii) implement designs and perform experiments to identify neural substrates necessary for offline meta-control by for example borrowing protocols used in educational sciences, (iii) study the effects of (offline) meta-knowledge training on the brain, and (iv) perform developmental research in the metacognitive brain and compare it with the existing developmental literature in educational sciences regarding the domain-generality of metacognitive processes and metacognitive abilities.

First, neurocognitive research on metacognitive judgements has developed robust operationalisations of offline meta-knowledge. However, these operationalisations often consist of specific tasks (e.g., 2-AFC) carried out in the lab. These tasks are often very narrow and do not resemble the challenges and complexities of behaviours associated with learning in schools and universities. Thus, one may question to what extent they reflect real-life metacognition, and to what extent protocols developed in educational sciences and cognitive neuroscience actually operationalise the same components of metacognition. We propose that comparing different protocols from both disciplines that are, a priori, operationalising the same types of metacognitive processes can help evaluate the ecological validity of protocols used in cognitive neuroscience, and allow for more holistic assessments of metacognition, provided that it is clear which protocol assesses which construct. Degrees of correlation between different protocols, within and between disciplines, may allow researchers to assess to what extent they reflect the same metacognitive constructs and also identify what protocols are most appropriate to study a specific construct. For example, a relation between meta- d ′ metacognitive sensitivity in a 2-AFC task and the meta-knowledge subscale of the MAI, would provide external validity to the former. Moreover, educational scientists would be provided with bias-free tools to assess metacognition. These tools may enable researchers to further investigate to what extent metacognitive bias, sensitivity and efficiency each play a role in education settings. In contrast, a low correlation may highlight a difference in domain between the two measures of metacognition. For instance, metacognitive judgements in brain research are made in isolated behaviour, and meta-d’ can thus be viewed to reflect “local” metacognitive sensitivity. It is also unclear to what extent processes involved in these decision-making tasks cover those taking place in a learning environment. When answering self-reported questionnaires, however, subjects make metacognitive judgements on a large set of (learning) activities, and the measures may thus resemble more “global” or domain-general metacognitive sensitivity. In addition, learners in educational settings tend to receive feedback — immediate or delayed — on their learning activities and performance, which is generally not the case for cognitive neuroscience protocols. Therefore, investigating metacognitive judgements in the presence of performance or social feedback may allow researchers to better understand the metacognitive processes at play in educational settings. Devising a global measure of metacognition in the lab by aggregating subjects’ metacognitive abilities in different domains or investigating to what extent local metacognition may affect global metacognition could improve ecological validity significantly. By investigating the neural correlates of educational measures of metacognition, researchers may be able to better understand to what extent the constructs studied in the two disciplines are related. It is indeed possible that, though weakly correlated, the meta-knowledge scale of the MAI and meta-d’ share a common neural basis.

Second, our review highlights gaps in the literature of both disciplines regarding the research of certain types of metacognitive processes. There is a lack of research in offline meta-control (or strategic regulation of cognition) in neuroscience, whereas this construct is widely studied in educational sciences. More specifically, while there exists research on EF related to planning (e.g. 107 ), common experimental designs make it hard to disentangle online from offline metacognitive processes. A few studies have implemented subject reports (e.g., awareness of error or desire for reminders) to pin-point the neural substrates specifically involved in offline meta-control and the current evidence points at a role of the lPFC. More research implementing similar designs may clarify this construct. Alternatively, researchers may exploit educational sciences protocols, such as self-report questionnaires, learning journals, metacognitive prompts and feedback to investigate offline meta-control processes in the brain and their relation to academic proficiency and achievement.

Third, there is only one study known to us on the training of meta-knowledge in the lab 78 . In contrast, meta-knowledge training in educational sciences have been widely studied, in particular with metacognitive prompts and learning journals, although a systematic review would be needed to identify the benefits for learning. Relative to cognitive neuroscience, studies suggest that offline meta-knowledge trained in and outside the lab (i.e., metacognitive judgements and meditation, respectively) transfer to meta-knowledge in other lab tasks. The case of meditation is particularly interesting since meditation has been demonstrated to beneficiate varied aspects of everyday life 108 . Given its importance for efficient regulation of cognition, training (offline) meta-knowledge may present the largest benefits to academic achievement. Hence, it is important to investigate development in the brain relative to meta-knowledge training. Evidence on metacognitive training in educational sciences tends to suggest that offline metacognition is more “plastic” and may therefore benefit learning more than online metacognition. Furthermore, it is important to have a good understanding of the developmental trajectory of metacognitive abilities — not only on a behavioural level but also on a neural level — to identify critical periods for successful training. Doing so would also allow researchers to investigate the potential differences in terms of plasticity that we mention above. Currently, the developmental trajectory of metacognition is under-studied in cognitive neuroscience with only one study that found an overlap between the neural correlates of metacognition in adults and children 109 . On a side note, future research could explore the potential role of genetic factors in metacognitive abilities to better understand to what extent and under what constraints they can be trained.

Fourth, domain-specific and domain-general aspects of metacognitive processes should be further investigated. Educational scientists have studied the development of metacognition in learners and have concluded that metacognitive abilities are domain-specific at the beginning (meaning that their quality depends on the type of learning activity, like mathematics vs. writing) and progressively evolve towards domain-general abilities as knowledge and expertise increase. Similarly, neurocognitive evidence points towards a common network for (offline) metacognitive knowledge which engages the different regions at varying degrees depending on the domain of the activity (i.e., perception, memory, etc.). Investigating this network from a developmental perspective and comparing findings with the existing behavioural literature may improve our understanding of the metacognitive brain and link the two bodies of evidence. It may also enable researchers to identify stages of life more suitable for certain types of metacognitive intervention.

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Acknowledgements

We would like to thank the University of Amsterdam for supporting this research through the Interdisciplinary Doctorate Agreement grant. W.v.d.B. is further supported by the Jacobs Foundation, European Research Council (grant no. ERC-2018-StG-803338), the European Union Horizon 2020 research and innovation programme (grant no. DiGYMATEX-870578), and the Netherlands Organization for Scientific Research (grant no. NWO-VIDI 016.Vidi.185.068).

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Fleur, D.S., Bredeweg, B. & van den Bos, W. Metacognition: ideas and insights from neuro- and educational sciences. npj Sci. Learn. 6 , 13 (2021). https://doi.org/10.1038/s41539-021-00089-5

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TEAL Center Fact Sheet No. 4: Metacognitive Processes

Metacognition is one’s ability to use prior knowledge to plan a strategy for approaching a learning task, take necessary steps to problem solve, reflect on and evaluate results, and modify one’s approach as needed. It helps learners choose the right cognitive tool for the task and plays a critical role in successful learning.

What Is Metacognition?

Metacognition refers to awareness of one’s own knowledge—what one does and doesn’t know—and one’s ability to understand, control, and manipulate one’s cognitive processes (Meichenbaum, 1985). It includes knowing when and where to use particular strategies for learning and problem solving as well as how and why to use specific strategies. Metacognition is the ability to use prior knowledge to plan a strategy for approaching a learning task, take necessary steps to problem solve, reflect on and evaluate results, and modify one’s approach as needed. Flavell (1976), who first used the term, offers the following example: I am engaging in Metacognition if I notice that I am having more trouble learning A than B; if it strikes me that I should double check C before accepting it as fact (p. 232).

Cognitive strategies are the basic mental abilities we use to think, study, and learn (e.g., recalling information from memory, analyzing sounds and images, making associations between or comparing/contrasting different pieces of information, and making inferences or interpreting text). They help an individual achieve a particular goal, such as comprehending text or solving a math problem, and they can be individually identified and measured. In contrast, metacognitive strategies are used to ensure that an overarching learning goal is being or has been reached. Examples of metacognitive activities include planning how to approach a learning task, using appropriate skills and strategies to solve a problem, monitoring one’s own comprehension of text, self-assessing and self-correcting in response to the self-assessment, evaluating progress toward the completion of a task, and becoming aware of distracting stimuli.

Elements of Metacognition

Researchers distinguish between metacognitive knowledge and metacognitive regulation (Flavell, 1979, 1987; Schraw & Dennison, 1994). Metacognitive knowledge refers to what individuals know about themselves as cognitive processors, about different approaches that can be used for learning and problem solving, and about the demands of a particular learning task. Metacognitive regulation refers to adjustments individuals make to their processes to help control their learning, such as planning, information management strategies, comprehension monitoring, de-bugging strategies, and evaluation of progress and goals. Flavell (1979) further divides metacognitive knowledge into three categories:

• Person variables: What one recognizes about his or her strengths and weaknesses in learning and processing information.
• Task variables: What one knows or can figure out about the nature of a task and the processing demands required to complete the task—for example, knowledge that it will take more time to read, comprehend, and remember a technical article than it will a similar-length passage from a novel.
• Strategy variables: The strategies a person has “at the ready” to apply in a flexible way to successfully accomplish a task; for example, knowing how to activate prior knowledge before reading a technical article, using a glossary to look up unfamiliar words, or recognizing that sometimes one has to reread a paragraph several times before it makes sense.

Livingston (1997) provides an example of all three variables: “I know that I ( person variable ) have difficulty with word problems ( task variable ), so I will answer the computational problems first and save the word problems for last ( strategy variable ).”

Why Teach Metacognitive Skills?

Research shows that metacognitive skills can be taught to students to improve their learning (Nietfeld & Shraw, 2002; Thiede, Anderson, & Therriault, 2003).

Constructing understanding requires both cognitive and metacognitive elements. Learners “construct knowledge” using cognitive strategies, and they guide, regulate, and evaluate their learning using metacognitive strategies. It is through this “thinking about thinking,” this use of metacognitive strategies, that real learning occurs. As students become more skilled at using metacognitive strategies, they gain confidence and become more independent as learners.

Individuals with well-developed metacognitive skills can think through a problem or approach a learning task, select appropriate strategies, and make decisions about a course of action to resolve the problem or successfully perform the task. They often think about their own thinking processes, taking time to think about and learn from mistakes or inaccuracies (North Central Regional Educational Laboratory, 1995). Some instructional programs encourage students to engage in “metacognitive conversations” with themselves so that they can “talk” with themselves about their learning, the challenges they encounter, and the ways in which they can self-correct and continue learning.

Moreover, individuals who demonstrate a wide variety of metacognitive skills perform better on exams and complete work more efficiently—they use the right tool for the job, and they modify learning strategies as needed, identifying blocks to learning and changing tools or strategies to ensure goal attainment. Because Metacognition plays a critical role in successful learning, it is imperative that instructors help learners develop metacognitively.

What’s the Research?

Metacognitive strategies can be taught (Halpern, 1996), they are associated with successful learning (Borkowski, Carr, & Pressley, 1987). Successful learners have a repertoire of strategies to select from and can transfer them to new settings (Pressley, Borkowski, & Schneider, 1987). Instructors need to set tasks at an appropriate level of difficulty (i.e., challenging enough so that students need to apply metacognitive strategies to monitor success but not so challenging that students become overwhelmed or frustrated), and instructors need to prompt learners to think about what they are doing as they complete these tasks (Biemiller & Meichenbaum, 1992). Instructors should take care not to do the thinking for learners or tell them what to do because this runs the risk of making students experts at seeking help rather than experts at thinking about and directing their own learning. Instead, effective instructors continually prompt learners, asking “What should you do next?”

McKeachie (1988) found that few college instructors explicitly teach strategies for monitoring learning. They assume that students have already learned these strategies in high school. But many have not and are unaware of the metacognitive process and its importance to learning. Rote memorization is the usual—and often the only—learning strategy employed by high school students when they enter college (Nist, 1993). Simpson and Nist (2000), in a review of the literature on strategic learning, emphasize that instructors need to provide explicit instruction on the use of study strategies. The implication for ABE programs is that it is likely that ABE learners need explicit instruction in both cognitive and metacognitive strategies. They need to know that they have choices about the strategies they can employ in different contexts, and they need to monitor their use of and success with these strategies.

Recommended Instructional Strategies

Instructors can encourage ABE learners to become more strategic thinkers by helping them focus on the ways they process information. Self-questioning, reflective journal writing, and discussing their thought processes with other learners are among the ways that teachers can encourage learners to examine and develop their metacognitive processes.

Fogarty (1994) suggests that Metacognition is a process that spans three distinct phases, and that, to be successful thinkers, students must do the following:

• Develop a plan before approaching a learning task, such as reading for comprehension or solving a math problem.
• Monitor their understanding; use “fix-up” strategies when meaning breaks down.
• Evaluate their thinking after completing the task.

Instructors can model the application of questions, and they can prompt learners to ask themselves questions during each phase. They can incorporate into lesson plans opportunities for learners to practice using these questions during learning tasks, as illustratetd in the following examples:

• During the planning phase, learners can ask, What am I supposed to learn? What prior knowledge will help me with this task? What should I do first? What should I look for in this reading? How much time do I have to complete this? In what direction do I want my thinking to take me?
• During the monitoring phase, learners can ask, How am I doing? Am I on the right track? How should I proceed? What information is important to remember? Should I move in a different direction? Should I adjust the pace because of the difficulty? What can I do if I do not understand?
• During the evaluation phase, learners can ask, H ow well did I do? What did I learn? Did I get the results I expected? What could I have done differently? Can I apply this way of thinking to other problems or situations? Is there anything I don’t understand—any gaps in my knowledge? Do I need to go back through the task to fill in any gaps in understanding? How might I apply this line of thinking to other problems?

Rather than viewing reading, writing, science, social studies, and math only as subjects or content to be taught, instructors can see them as opportunities for learners to reflect on their learning processes. Examples follow for each content area:

• Reading: Teach learners how to ask questions during reading and model “think-alouds.” Ask learners questions during read-alouds and teach them to monitor their reading by constantly asking themselves if they understand what the text is about. Teach them to take notes or highlight important details, asking themselves, “Why is this a key phrase to highlight?” and “Why am I not highlighting this?”
• Writing: Model prewriting strategies for organizing thoughts, such as brainstorming ideas using a word web, or using a graphic organizer to put ideas into paragraphs, with the main idea at the top and the supporting details below it.
• Social Studies and Science: Teach learners the importance of using organizers such as KWL charts, Venn diagrams, concept maps , and anticipation/reaction charts to sort information and help them learn and understand content. Learners can use organizers prior to a task to focus their attention on what they already know and identify what they want to learn. They can use a Venn diagram to identify similarities and differences between two related concepts.
• Math: Teach learners to use mnemonics to recall steps in a process, such as the order of mathematical operations. Model your thought processes in solving problems—for example, “This is a lot of information; where should I start? Now that I know____, is there something else I know?”

The goal of teaching metacognitive strategies is to help learners become comfortable with these strategies so that they employ them automatically to learning tasks, focusing their attention, deriving meaning, and making adjustments if something goes wrong. They do not think about these skills while performing them but, if asked what they are doing, they can usually accurately describe their metacognitive processes.

Biemiller, A., & Meichenbaum, D. (1992). The nature and nurture of the self-directed learner. Educational Leadership, 50, 75–80.

Borkowski, J., Carr, M., & Pressely, M. (1987). “Spontaneous” strategy use: Perspectives from metacognitive theory. Intelligence, 11, 61–75.

Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, 34, 906–911.

Flavell, J. H. (1976). Metacognitive aspects of problem solving. In L. B. Resnick (Ed.), The nature of intelligence (pp. 231–236). Hillsdale, NJ: Lawrence Erlbaum Associates.

Flavell, J. H. (1987). Speculations about the nature and development of metacognition. In F. E. Weinert & R. H. Kluwe (Eds.), Metacognition, motivation, and understanding (pp. 21–29). Hillside, NJ: Lawrence Erlbaum Associates.

Fogarty, R. (1994). How to teach for metacognition. Palatine, IL: IRI/Skylight Publishing.

Halpern, D. F. (1996). Thought and knowledge: An introduction to critical thinking. Mahwah, NJ: Lawrence Erlbaum Associates.

Livingston, J. A. (1997). Metacognition: An overview. Retrieved December 27, 2011 from http://gse.buffalo.edu/fas/shuell/CEP564/Metacog.htm

McKeachie, W. J. (1988). The need for study strategy training. In C. E. Weinstein, E. T. Goetz, & P. A. Alexander (Eds.), Learning and study strategies: Issues in assessment, instruction, and evaluation (pp. 3–9). New York: Academic Press.

Meichenbaum, D. (1985). Teaching thinking: A cognitive-behavioral perspective. In S. F., Chipman, J. W. Segal, & R. Glaser (Eds.), Thinking and learning skills, Vol. 2: Research and open questions. Hillsdale, NJ: Lawrence Erlbaum Associates.

North Central Regional Educational Laboratory. (1995). Strategic teaching and reading project guidebook. Retrieved December 27, 2011

Nietfeld, J. L., & Shraw, G. (2002). The effect of knowledge and strategy explanation on monitoring accuracy. Journal of Educational Research, 95, 131–142.

Nist, S. (1993). What the literature says about academic literacy. Georgia Journal of Reading, Fall-Winter, 11–18.

Pressley, M., Borkowski, J. G., & Schneider, W. (1987). Cognitive strategies: Good strategy users coordinate metacognition and knowledge. In R. Vasta, & G. Whitehurst (Eds.), Annals of child development, 4, 80–129. Greenwich, CT: JAI Press.

Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19, 460–475.

Simpson, M. L., & Nist, S. L. (2000). An update on strategic learning: It’s more than textbook reading strategies. Journal of Adolescent and Adult Literacy, 43 (6) 528–541.

Thiede, K. W., Anderson, M. C., & Therriault, D. (2003). Accuracy of metacognitive monitoring affects learning of texts. Journal of Educational Psychology, 95, 66–73.

Authors: TEAL Center staff

Reviewed by: David Scanlon, Boston College

About the TEAL Center: The Teaching Excellence in Adult Literacy (TEAL) Center is a project of the U.S. Department of Education, Office of Career, Technical, and Adult Education (OCTAE), designed to improve the quality of teaching in adult education in the content areas.

BRIEF RESEARCH REPORT article

Relation between metacognitive strategies, motivation to think, and critical thinking skills.

• 1 Educations Science Department, University of the Bío Bío, Concepción, Chile
• 2 Psychology Faculty, University of Salamanca, Salamanca, Spain

Critical thinking is a complex reasoning skill, and even though it is hard to reach a consensus on its definition, there is agreement on it being an eminently cognitive skill. It is strongly related with reflective and metacognitive skills, as well as attitudinal or motivational aspects, although no model has yet been able to integrate these three elements. We present herein the preliminary results of a study seeking to establish these relations, in a sample of Chilean university students. 435 students from three universities participated, of which 88 were men, 333 were women, and 14 did not indicate their gender. Their ages ranges between 18 and 51 years old ( M  = 21, SD = 3.09). Three instruments were applied, one to measure metacognitive strategies, one to measure motivation to critical thinking, and a third to measure critical thinking skills. The relation was analyzed via structural equations. The results show a positive, strong, and significant relation between metacognition and motivation to think. However, only a weak significant relation was observed between motivation to think and critical thinking, and no direct relation was found between metacognition and critical thinking. We hypothesize a significant but moderate relation between the variables, where metacognition influences motivation to think, which in turn influences critical thinking skills. Factors are discussed which could negatively affect the studied relations, as well as the importance of generating integrated models between the three variables, as they would show a theoretical and empirical link.

Introduction

Critical thinking is a relevant topic for the 21st century, highlighted by Unesco as one of the skills to develop among students to properly face the challenges of this century ( Scott, 2015 ). Despite its importance for human development, its implementation in educational curricula has been difficult to carry out, both at the level of school systems and in higher education systems ( Ossa et al., 2018 ; Silva Pacheco, 2019 ).

This difficulty of incorporating critical thinking into the educational process may be related with the complexity of the task. On one side, there is discussion as to whether the process can be taught as a skill, or whether it is more of a facet of thinking which can only be stimulated in a concrete way ( Saiz, 2017 ). Building on this factor, the complexity of the matter is also expressed in the attempts at defining the process, since there are various definitions of critical thinking. These definitions present different natures, ranging from only cognitive reasoning processes; cognitive and metacognitive processes; cognitive, metacognitive and attitudinal processes; and finally, cognitive, metacognitive, attitudinal, and social agency processes ( Montero, 2010 ; Rivas and Saiz, 2012 ; Ossa and Díaz, 2017 ; Saiz, 2017 ).

As society and socio-cultural challenges have become more complex, it is necessary to adopt more complex perspectives on human processes. Critical thinking perspectives which help integrate diverse processes could be more pertinent for the effective development of this skill among people ( Paul and Elder, 2003 ).

Critical thinking has been linked to different skills, both cognitive and non-cognitive, for example, problem solving, scientific reasoning, motivation, metacognition, and now ultimately creativity ( Saiz and Rivas, 2008 ; Tamayo-Alzate et al., 2019 ; Halpern and Dunn, 2021 ; Muñoz and Ruiz, 2022 ; Santana et al., 2022 ). Of these skills, problem solving has been incorporated as a constituent element of critical thinking in some models; Likewise, motivation and metacognition are closely related factors and it has been proposed that they are satellite skills for critical thinking processes ( Valenzuela and Nieto, 2008 ; Rivas and Saiz, 2011 ; García, 2022 ), although no empirical information has been shown to clearly demonstrate this. The objective of this paper is precisely to show the relationship between motivation to think and metacognition with critical thinking, in order to contribute to what is proposed.

Critical thinking, motivation, and metacognition

Even when critical thinking is a broadly used concept in the academic and educational world, with a wide range of studies in the last decade, it continues to be a difficult phenomenon to conceptualize and to create little consensus ( Ossa et al., 2016 ; Saiz, 2017 ; Díaz et al., 2019 ).

It is conceptualized as a cognitive mechanism which filters information about the ideological intentions accompanying said information, via continual questioning of knowledge production practices, and the recognition of its different perspectives ( Yang and Chung, 2009 ; Montero, 2010 ).

It is a type of thinking oriented toward data and action, in a context of solving problems and interacting with other people ( Daniel and Auriac, 2012 ; López, 2012 ). Critical thinking is self-directed, self-disciplined, self-regulated and self-corrected. It involves undergoing rigorous standards of excellence and a conscious dominion of its use. It also implies effective communication and the development of problem solving skills ( Saiz and Rivas, 2008 , 2012 , 2016 ).

Critical thinking is characterized by generating higher-level cognitive processing in people, centered on the skills of reflecting, comprehension, evaluation and creation. It therefore requires high intellectual development. However, it is also a skill which can be developed, since there are no important differences between people with average and high intellectual levels with regards to developing critical thinking ( Sierra et al., 2010 ).

Since critical thinking is a high-level cognitive process, and the ability to generate an elaborated thought, a close relation has been proposed with elements which are not considered merely cognitive, including metacognition ( Rivas et al., 2022 ). Metacognition is a reflective process which helps deepen thought, regulate, and generate consciousness about thought ( Tamayo-Alzate et al., 2019 ; Drigas and Mitsea, 2020 ). It has been worked on as both a reflective process of self-knowledge, and as a skill which helps develop other cognitive processes including memory, learning, or even intelligence, since different levels of application can be established in its use ( Drigas and Mitsea, 2021 ).

There is evidence that metacognitive strategies can influence critical thinking and its components. For one, it improves the use of metacognitive strategies due to intervention in critical thinking. It also improves the use of critical thinking with metacognitive strategies in interventions done with psychology students at universities ( Ossa et al., 2016 ; Rivas et al., 2022 ). Significant and positive relations have also been found between critical thinking and metacognitive consciousness among medical students, although not for regulation and knowledge tasks ( de la Portilla Maya et al., 2022 ).

In this way, we can observe a relative influence on the way that people think about thinking, since metacognition supports decision making and final evaluation about strategies to resolve problems ( Rivas et al., 2022 ).

Some authors also indicate the presence of another non-cognitive component in critical thinking, which is disposition or motivation ( Facione et al., 2000 ; Saiz and Rivas, 2008 ; Marin and Halpern, 2011 ; Valenzuela et al., 2014 ; Halpern and Dunn, 2023 ). This component is fundamental to achieve this skill, since even when the indicated cognitive functions are available, if people either lack the desire to apply critical thinking or deem it inconvenient to do so, critical thinking will not be adequately manifested ( Valenzuela and Nieto, 2008 ; Valenzuela et al., 2014 ).

This non-cognitive element is based on human attitudes or motivations which complement the use of critical thinking, allowing it to be better developed, since they drive personal improvement ( Boonsathirakul and Kerdsomboon, 2021 ). The factors presented as facets of a disposition toward critical thinking include seeking truth, open-mindedness, being analytical, systematicity, curiosity, self-confidence and maturity (Facione, in Boonsathirakul and Kerdsomboon, 2021 ).

However, considering these non-cognitive elements as dispositions of a being also involves assuming certain personality traits or dimensions of values which cannot always be adequately measured. They should thus be considered more as motivational aspects, since they could be better defined and with a greater possibility of modification, given that they are more related with behavioral and perceptual elements ( Valenzuela et al., 2014 , 2023 ). From this perspective, we understand that non-cognitive components are based on the expectations and value given to the task. In this way, we establish a direct and causal relation between motivation and critical thinking, where the former explains critical thinking development by between 8 and 17%, according to the instrument used to measure it ( Valenzuela et al., 2023 ).

In this way, promoting motivational aspects is a relevant factor for developing cognitive and metacognitive processes, since complex processes are exhausting and require a high and constant investment of cognitive and emotional factors ( Valenzuela and Nieto, 2008 ; Valenzuela and Saiz, 2010 ; Gaviria, 2019 ; Nieto-Márquez et al., 2021 ).

Finally, a relative relation has been noted between motivational processes and metacognitive strategies. Correa et al. (2019) performed an evaluation among Chilean high school students about the use of metacognitive strategies and motivation to critical thinking in bias recognition. They found a positive, significant, and medium-intensity correlation ( r  = 0.50, p  < 0.001) between both variables, which indicates that cognitive and non-cognitive factors have a relevant link for human thought.

With the aforementioned background, we can hypothesize the existence of a significant and positive relation between critical thinking, metacognitive strategies, and motivation to think critically; that motivation to think directly affects critical thinking; and those metacognitive strategies are related with both variables.

In this article it will be showed preliminary results from this relation, presenting a relational model based on structural equations which would allow for establishing direct and mediated relations between said variables.

A correlational study was done via structural equations.

Participants

435 students from pedagogy majors at three Chilean universities participated in the study. Of these, 88 were male (20.2%), 333 were female (76.6%), 7 were students of unidentified gender (1.6%), and 7 did not respond (1.6%). Students’ ages fell between 18 and 51 years ( M  = 21, SD = 3.09). The careers to which the students belong are in the area of pedagogy, in specialties of mathematics (22%), history (8%), science (15%), special education (15%), and early childhood education (40%).

Instruments

For this study, a battery with three instruments was applied:

1. Metacognitive strategy questionnaire from O’Neil and Abedi, adapted into Spanish by Martínez (2007) . This measure metacognitive strategies applied to different academic tasks. There are 20 items organized into three dimensions: self-knowledge (referring to metacognitive consciousness), self-regulation (referring to metacognitive control), and evaluation (referring to global task evaluation). Results are recorded with a Likert-type scale of 5 choices (0 to 4 points). This instrument has been applied to Chilean university students and shown adequate reliability indicators. The global Cronbach’s α was 0.87, and for the dimensions it was between 0.62 and 0.65 ( Correa et al., 2019 ).

2. Critical thinking motivation questionnaire from Valenzuela, measuring the intention of applying thinking to knowledge tasks, based on personal expectations and the value of the task. It contains 19 items organized into 5 dimensions: Expectation ( α  = 0.774), Importance ( α  = 0.770), Cost ( α  = 0.775), Utility ( α  = 0.790) and Interest ( α  = 0.724). Its results are recorded based on a Likert-type scale with 5 alternatives (0–4 points). It has been applied to Chilean university students with strong reliability indicators. The global Cronbach’s α was 0.92, and the values for its dimensions ranged from 0.69 to 0.83 ( Valenzuela and Nieto, 2008 ; Correa et al., 2019 ).

3. Critical thinking task test from Miranda, adapted by Palma Luengo et al. (2021) . This measured the capacity to apply cognitive critical thinking processes to socio-scientific topics. It contains 15 items organized into three dimensions: inquiry (referring to identifying useful information), analysis (referring to the decision to use pertinent and reliable data), and arguing (referring to providing arguments with useful and reliable data). Its results are recorded with a sequence of scores ranging from 0 to 3 points, based on a performance rubric. It has been applied to a sample of Chilean university students with moderately adequate reliability indicators. The overall Cronbach’s α was 0.67, with moderately low values in its dimensions ranging from 0.47 to 0.60 ( Palma Luengo et al., 2021 ).

Three metacognition questions were incorporated into this instrument to reflect on the tasks being done, one for each dimension (e.g., How are you so confident about knowing how to do the activity? ). Two questions about motivation to thinking were also included, in the middle and at the end of the test, seeking to analyze whether there was a disposition to answer a question in a more voluntary form (e.g., Do you want to finish the test here or do you want to continue to delve deeper into the topic? ). The overall Cronbach’s α was 0.78 (five dimensions), and the values were moderately adequate within these dimensions (0.54 for metacognition and 0.73 for motivation).

We made contact with the directors of the pedagogy majors at three different universities, coordinating the process and determining the courses to consider. After this, a talk was carried out in each course, inviting students to participate in the study. Written informed consent was incorporated into the survey, indicating the study objectives and describing the anonymous and voluntary nature of participation. Open consultations were made about participation in applying the surveys, applying the battery of instruments only to those who wished to participate.

After answering the instruments, the data was emptied into a digital database and analyzed with SPSS v.27 and RStudio software. For data analysis, we used inferential and multivariate statistics. For all inference effects, a 5% significance threshold has been considered. In the structural models, we applied formats from Partial Least Squares (SEM-PLS).

We present an application of structural equations based on partial least squares (PLS), designed to model behavioral situations and social sciences. According to Wold (1980) it is fairly flexible, since it is useful for small sample sizes and also does not require distributional assumptions for the variables, along with being useful for predictive analysis as well as theoretical confirmation. With the PLS format, there are three methodological considerations which are relevant for application: (i) choosing variable with items that effectively belong, (ii) valuing items’ reliability and validity, and (iii) properly interpreting the coefficients.

As indicated in this type of modeling, there are two sections. The first is the measurement model, where each dimension is formatively related with its items: i.e., the item contributes to the variable with a certain coefficient called weight ( w ). This factorial weight represents the weighting of the dimension regarding the latent variable which it intends to measure, so that we can expect it to have sufficient magnitude to be statistically significant.

To begin, for the Metacognition variable, the scores for Self-Knowledge ( w  = 0.67, p  < 0.001, 95% IC: 0.41; 0.97) and Evaluation ( w  = 0.34, p  < 0.01, 95% IC: 0.12; 0.56) are relevant for generating the latent indicator. For the Motivation variable, the scores for Expectations ( w  = 0.21, p  < 0.05, 95% IC: 0.25; 0.62), Importance ( w  = 0.43, p  < 0.001, 95% IC: 0.14; 0.60), and Usefulness ( w  = 0.39, p  < 0.001, 95% IC: 0.19; 0.25) are representative when generating this indicator. For Critical Thinking, only the Metacognition indicator ( w  = 0.71, p  < 0.05, 95% IC: 0.56; 0.86) turned out to be appropriate.

The second section of this type of models is called the structural model. It shows the causality relations between the latent variables. Schematically, we consider that a variable X is the cause of another variable Y, and an arrow will go from X to Y. For this study, the relational schematic between variables is given by the following hypothesis set:

H1 : There is a positive effect of the Metacognition Strategy (ME) on Critical Thinking Motivation (MO). H2 : There is a positive effect of Metacognition Strategy (ME) on Critical Thinking (PC). H3 : There is a positive effect of Critical Thinking Motivation (MO) on Critical Thinking (PC).

Figure 1 shows the hypotheses combined with their respective variables, indicating the measurement and structural models.

Figure 1 . Schematic of hypothesis and effects expected. Structural equation model. Source: authors.

The empirical results from the model appear in Table 1 with their significance level.

Table 1 . Structural equation model results.

Finally, in the structural model ( Figure 2 ), we can see the fulfillment of hypothesis H1 ( B  = 0.56, p  < 0.001, 95% IC: 0.49; 0.63) where a greater perception of Metacognition leads to a greater level of Critical Thinking Motivation. There is also fulfillment for hypothesis H3 ( B  = 0.21, p  < 0.01, 95% IC: 0.06; 0.34) indicating that greater levels of Critical Thinking Motivation lead to a greater level of Critical Thinking.

Figure 2 . Results schematic. Structural equations model. Source: authors.

Our preliminary study results show ties between the three variables, as indicated both in theory ( Facione et al., 2000 ; Valenzuela and Nieto, 2008 ; Tamayo-Alzate et al., 2019 ) and in other studies ( Correa et al., 2019 ; Rivas et al., 2022 ; Valenzuela et al., 2023 ). However, we found some disparate data with regards to the latter points.

For the structural models, hypotheses H1 and H3 have been fulfilled, reporting statistically significant evidence that greater perceived Metacognition explains a greater level of Critical Thinking Motivation, a greater level of Critical Thinking Motivation implies a higher level of Critical Thinking.

One important aspect here is that a significant relation was found between motivation and critical thinking skills, which is supported by Valenzuela et al. (2023) . While the value of the relation is moderate, it can be related, as presented in the aforementioned study, and may be due to the type of instrument used to measure critical thinking. One notable aspect is that the motivation question incorporated into the critical thinking task instrument had little weight within this instrument. However, this could be explained because the questions sought to consider effort for the task. Reviewing the components of the critical thinking motivation survey, the dimensions with the strongest ties were those oriented towards expectations, usefulness and importance, not effort or energy costs.

It is possible that the relationship between metacognition and motivation to think is established because, from the theoretical model used ( Valenzuela and Nieto, 2008 ; Valenzuela et al., 2014 ), the expectation of the task, and its assessment of usefulness (aspects motivation), require an evaluation process (metacognitive aspect); However, this idea must be deepened and reviewed in more detail.

Considering metacognition, no direct relation was observed between the instrument used in this study to measure the metacognitive strategies of self-knowledge, self-regulation and evaluation on one hand, and critical thinking on the other. This situation goes against other studies’ findings ( de la Portilla Maya et al., 2022 ; Rivas et al., 2022 ), and may be explained by the type of instrument used, which may not be sensitive to the critical thinking tasks measured by the test from Palma Luengo et al. (2021) .

The relation discovered about metacognition supporting critical thinking motivation, in order to thus achieve better critical thinking, is one of the key relevant findings in this study. It implies that reflecting on oneself and tasks can generate greater expectations and evaluation for the task, which can drive better performance. These results still need more breadth and depth from further research.

This study is only a preliminary report of results, to account for the relationship between the aforementioned variables and propose that critical thinking benefits from metacognitive and motivational work. Its limitations are the fact that its objective was only empirical, in order to account for the relationship raised in studies ( Valenzuela and Nieto, 2008 ), so the theoretical depth was less. On the other hand, there was a limited number of participating students, and only from some university majors. Likewise, it is considered that the critical thinking test that was used presents adequate reliability values overall, but with less powerful values in some of its dimensions (specifically, inquiry and motivation). It is considered necessary to replicate the study with another instrument and a larger sample to more fully support the results found.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by Pedro Labraña Research Unit of Bio-Bio University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

CO: Conceptualization, Methodology, Project administration, Writing – original draft. SR: Investigation, Supervision, Writing – review & editing. CS: Conceptualization, Methodology, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This article was developed in part with funding from FONDECYT Project 11220056, from the Chilean National Research and Development Agency (ANID).

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

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

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Keywords: critical thinking, structural models, cognition, motivation, pedagogy

Citation: Ossa CJ, Rivas SF and Saiz C (2023) Relation between metacognitive strategies, motivation to think, and critical thinking skills. Front. Psychol . 14:1272958. doi: 10.3389/fpsyg.2023.1272958

Received: 04 August 2023; Accepted: 13 November 2023; Published: 04 December 2023.

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Copyright © 2023 Ossa, Rivas and Saiz. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Carlos J. Ossa, [email protected]

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

3 Core Critical Thinking Skills Every Thinker Should Have

Critically thinking about critical thinking skills..

Posted March 13, 2020 | Reviewed by Ekua Hagan

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I recently received an email from an educator friend, asking me to briefly describe the skills necessary for critical thinking. They were happy to fill in the blanks themselves from outside reading but wanted to know what specific skills they should focus on teaching their students. I took this as a good opportunity to dedicate a post here to such discussion, in order to provide my friend and any other interested parties with an overview.

To understand critical thinking skills and how they factor into critical thinking, one first needs a definition of the latter. Critical thinking (CT) is a metacognitive process, consisting of a number of skills and dispositions, that when used through self-regulatory reflective judgment, increases the chances of producing a logical conclusion to an argument or solution to a problem (Dwyer, 2017; Dwyer, Hogan & Stewart, 2014). On the surface, this definition clarifies two issues. First, critical thinking is metacognitive—simply, it requires the individual to think about thinking; second, its main components are reflective judgment, dispositions, and skills.

Below the surface, this description requires clarification; hence the impetus for this entry—what is meant by reflective judgment, disposition towards CT, and CT skills? Reflective judgment (i.e. an individuals' understanding of the nature, limits, and certainty of knowing and how this can affect their judgments [King & Kitchener, 1994]) and disposition towards CT (i.e. an inclination, tendency or willingness to perform a given thinking skill [Dwyer, 2017; Facione, Facione & Giancarlo, 1997; Ku, 2009; Norris, 1992; Siegel, 1999; Valenzuela, Nieto & Saiz, 2011]) have both already been covered in my posts; so, consistent with the aim of this piece, let’s discuss CT skills.

CT skills allow individuals to transcend lower-order, memorization-based learning strategies to gain a more complex understanding of the information or problems they encounter (Halpern, 2014). Though debate is ongoing over the definition of CT, one list stands out as a reasonable consensus conceptualization of CT skills. In 1988, a committee of 46 experts in the field of CT gathered to discuss CT conceptualisations, resulting in the Delphi Report; within which was overwhelmingly agreement (i.e. 95% consensus) that analysis , evaluation and inference were the core skills necessary for CT (Facione, 1990). Indeed, over 30 years later, these three CT skills remain the most commonly cited.

1. Analysis

Analysis is a core CT skill used to identify and examine the structure of an argument, the propositions within an argument and the role they play (e.g. the main conclusion, the premises and reasons provided to support the conclusion, objections to the conclusion and inferential relationships among propositions), as well as the sources of the propositions (e.g. personal experience, common belief, and research).

When it comes to analysing the basis for a standpoint, the structure of the argument can be extracted for subsequent evaluation (e.g. from dialogue and text). This can be accomplished through looking for propositions that either support or refute the central claim or other reasons and objections. Through analysis, the argument’s hierarchical structure begins to appear. Notably, argument mapping can aid the visual representation of this hierarchical structure and is supported by research as having positive effects on critical thinking (Butchart et al., 2009; Dwyer, 2011; Dwyer, Hogan & Stewart, 2012; van Gelder, Bisset & Cumming, 2004).

2. Evaluation

Evaluation is a core CT skill that is used in the assessment of propositions and claims (identified through the previous analysis ) with respect to their credibility; relevance; balance, bias (and potential omissions); as well as the logical strength amongst propositions (i.e. the strength of the inferential relationships). Such assessment allows for informed judgment regarding the overall strength or weakness of an argument (Dwyer, 2017; Facione, 1990). If an argument (or its propositions) is not credible, relevant, logical, and unbiased, you should consider excluding it or discussing its weaknesses as an objection.

Evaluating the credibility of claims and arguments involves progressing beyond merely identifying the source of propositions in an argument, to actually examining the "trustworthiness" of those identified sources (e.g. personal experiences, common beliefs/opinions, expert/authority opinion and scientific evidence). This is particularly important because some sources are more credible than others. Evaluation also implies deep consideration of the relevance of claims within an argument, which is accomplished by assessing the contextual relevance of claims and premises—that is, the pertinence or applicability of one proposition to another.

With respect to balance, bias (and potential omissions), it's important to consider the "slant" of an argument—if it seems imbalanced in favour of one line of thinking, then it’s quite possible that the argument has omitted key, opposing points that should also be considered. Imbalance may also imply some level of bias in the argument—another factor that should also be assessed.

However, just because an argument is balanced does not mean that it isn’t biased. It may very well be the case that the "opposing views" presented have been "cherry-picked" because they are easily disputed (akin to building a strawman ); thus, making supporting reasons appear stronger than they may actually be—and this is just one example of how a balanced argument may, in fact, be biased. The take-home message regarding balance, bias, and potential omissions should be that, in any argument, you should construct an understanding of the author or speaker’s motivations and consider how these might influence the structure and contents of the argument.

Finally, evaluating the logical strength of an argument is accomplished through monitoring both the logical relationships amongst propositions and the claims they infer. Assessment of logical strength can actually be aided through subsequent inference, as a means of double-checking the logical strength. For example, this can be checked by asking whether or not a particular proposition can actually be inferred based on the propositions that precede it. A useful means of developing this sub-skill is through practicing syllogistic reasoning .

3. Inference

Similar to other educational concepts like synthesis (e.g., see Bloom et al., 1956; Dwyer, 2011; 2017), the final core CT skill, inference , involves the “gathering” of credible, relevant and logical evidence based on the previous analysis and evaluation, for the purpose of drawing a reasonable conclusion (Dwyer, 2017; Facione, 1990). Drawing a conclusion always implies some act of synthesis (i.e. the ability to put parts of information together to form a new whole; see Dwyer, 2011). However, inference is a unique form of synthesis in that it involves the formulation of a set of conclusions derived from a series of arguments or a body of evidence. This inference may imply accepting a conclusion pointed to by an author in light of the evidence they present, or "conjecturing an alternative," equally logical, conclusion or argument based on the available evidence (Facione, 1990). The ability to infer a conclusion in this manner can be completed through formal logic strategies, informal logic strategies (or both) in order to derive intermediate conclusions, as well as central claims.

Another important aspect of inference involves the querying of available evidence, for example, by recognising the need for additional information, gathering it and judging the plausibility of utilising such information for the purpose of drawing a conclusion. Notably, in the context of querying evidence and conjecturing alternative conclusions, inference overlaps with evaluation to a certain degree in that both skills are used to judge the relevance and acceptability of a claim or argument. Furthermore, after inferring a conclusion, the resulting argument should be re-evaluated to ensure that it is reasonable to draw the conclusion that was derived.

Overall, the application of critical thinking skills is a process—one must analyse, evaluate and then infer; and this process can be repeated to ensure that a reasonable conclusion has been drawn. In an effort to simplify the description of this process, for the past few years, I’ve used the analogy of picking apples for baking . We begin by picking apples from a tree. Consider the tree as an analogy, in its own right, for an argument, which is often hierarchically structured like a tree-diagram. By picking apples, I mean identifying propositions and the role they play (i.e. analysis). Once we pick an apple, we evaluate it—we make sure it isn’t rotten (i.e. lacks credibility, is biased) and is suitable for baking (i.e. relevant and logically strong). Finally, we infer— we gather the apples in a basket and bring them home and group them together based on some rationale for construction— maybe four for a pie, three for a crumble and another four for a tart. By the end of the process, we have baked some apple-based goods, or developed a conclusion, solution or decision through critical thinking.

Of course, there is more to critical thinking than the application of skills—a critical thinker must also have the disposition to think critically and engage reflective judgment. However, without the appropriate skills—analysis, evaluation, and inference, it is not likely that CT will be applied. For example, though one might be willing to use CT skills and engage reflective judgment, they may not know how to do so. Conversely, though one might be aware of which CT skills to use in a given context and may have the capacity to perform well when using these skills, they may not be disposed to use them (Valenzuela, Nieto & Saiz, 2011). Though the core CT skills of analysis, evaluation, and inference are not the only important aspects of CT, they are essential for its application.

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Christopher Dwyer, Ph.D., is a lecturer at the Technological University of the Shannon in Athlone, Ireland.

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Metacognition & Critical Thinking: Differences and Similarities

Two terms we usually confuse with each other are Critical Thinking and Metacognition. Even though they both describe the skill of being aware of thinking processes and their possible outcomes, metacognition is a bit more complicated. To fully understand both terms properly, we need to examine their similarities and differences.

Critical Thinking

Critical thinking mostly means putting into practice theoretical frameworks and concepts. This skill is quite necessary because of its ability to allow us to face real-life issues better. When we learn how to critically think and process information, we immediately become smarter consumers, civilians, and people. We are also able to comprehend hidden meanings and to criticize better and more constructively.

Also, when our critical thinking skills are used we can engage in conflict with well-reasoned arguments (in favor; against a party involved).

Overall, critical thinking works as a way of perceiving reality, while it provides us with more ways of dealing with daily life issues.

Critical thinking skills though, need to be practiced, learned, and constantly improved. As critical thinking is not a set of skills that we acquire when we are born, we need to learn it, and how to practice it. This is often achieved through school-college and real-life events that challenge us and require some special treatment that urges us to think differently, and from another perspective.

Also, as our life changes, we need to be “equipped with” the appropriate critical thinking skills that will help us face any kind of situation. This usually occurs by having many challenging experiences, that offer us many new characteristics.

Taking into consideration the fact that a lot of us learn how to “critically think” at school, the educational field should be able to promote such skills and make sure that critical thinking is taught during every class/lesson.

Unfortunately, this is not what occurs. Conventional teaching and learning methods are still used, and they do not support critical thinking at all. In fact, they promote “rigid” learning and most of the time, too theoretical that can’t be applied in real-life situations. That results in students not being able to remember what they have learned, let alone apply all that information in various situations.

When defining this term, we need to take into account four traits.

• Foundation skills:  This category of skills consists of basic skills that also fall under the umbrella of critical thinking skills. For example, proper speculations about an issue, constructing an argument with facts, and doubting the credibility of a piece of information, are all independent skills that, in this case, constitute a foundation base for critical thinking.
• Knowledge base : This includes the context that allows us to apply our skills. For example, academic knowledge is a good part of this category as real-life events that require critical thinking from the participants, are.
• Willingness to question : This category describes the personal attitude of a person towards critical thinking, and towards practices that demand critical thinking. For example, some people avoid events that require critical thinking because they perceive it as difficult or too challenging.
• Self-reflection : This category means Metacognition, which we are going to examine below.

Metacognition

Metacognition, or the “Self-reflection” dimension of Critical Thinking, is a more theoretical and personal concept. It describes our perspective and reflection on our thinking processes. That is why it is also called “Self-reflection”. Because, with Metacognition we can evaluate the efficacy of our thinking procedures before, during, and after an issue.

Basically, Metacognition is the level after critical thinking. It allows us to examine our critical thinking abilities a bit more clearly, and to reflect on their usefulness. It is a slower procedure, and it requires good observation skills, regarding our critical thinking attitude.

In addition, we can control Metacognition, since it is our reflection. Metacognition is a rather new concept and somewhat difficult to examine since it heavily relies on personal thoughts and reflections that are not caused by external factors ( educational field).

Differences

When it comes to the differences these concepts have, there are 4 points to consider:

• When using critical thinking, we usually know our goal and expected result ( resolving an issue, reaching a conclusion). However, when we use Metacognition, we are aware of the efficiency of our thinking processes and methods, rather than the outcome of our critical thinking.
• In general, Critical thinking is more supported, in comparison with Metacognition. For example, educational institutions recognize Creative thinking as a useful tool, when Metacognition is not discussed ( very rarely).
• Overall, Critical Thinking is more directly applicable to real-life situations than Metacognition. Thus, critical thinking is much easier and less complicated.
• Metacognition is more personal than critical thinking. Critical thinking- as mentioned above- is also highly affected by interaction with other people and with external factors. Metacognition is purely personal, as it is a reflection.

Similarities: Metacognition and critical-thinking

Regarding the similarities between these two concepts, three points need to be considered:

• Critical thinking and metacognition need a lot of practice. They both include skills that work better when practiced frequently. However, critical thinking comes first, as Metacognition is a reflection of it (hence, it makes sense for Metacognition to come second).
• Both Critical thinking and metacognition are very much needed. They might have different goals, but they complete each other as concepts.
• Also, they both “require awareness of the relevant procedures”. That means that during both procedures, we learn more when we are conscious of the skills and tools we are using.

On An Ending Note

To sum up, the concept of Critical Thinking and the concept of Metacognition share some elements, but they are also very different from each other. Even though they both are necessary, Critical thinking (as the first level) needs to be more promoted and supported, when Metacognition (the second level) needs to be a concept that people raise awareness of, especially in the educational and academic domain.

Two forms of ‘thinking about thinking’: metacognition and critical thinking

https://www.encyclopedia.com/education/applied-and-social-sciences-magazines/critical-thinking-metacognition-and-problem-based-learningC

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Metacognitive writing strategies, critical thinking skills, and academic writing performance: A structural equation modeling approach

• Published: 24 November 2022
• Volume 18 , pages 237–260, ( 2023 )

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• Mark Feng Teng   ORCID: orcid.org/0000-0002-5134-8504 1 &
• Mei Yue   ORCID: orcid.org/0000-0003-0688-1040 2  

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The present study adopted the structural equation modeling approach to examine Chinese university students’ metacognition, critical thinking skills, and academic writing. In particular, this research explored whether awareness in metacognition can foster critical thinking and, thus, lead to enhancement in academic writing. The measure for exploring metacognitive writing strategies covered metacognitive knowledge and regulation in academic writing. The measure for understanding learners’ critical thinking encompassed the following five skills: inference, recognition of assumptions, deduction, interpretations, and evaluation of arguments. The academic writing assessment was based on an internal test. The participants consisted of 644 third-year students from a Chinese university. Three models tested: (1) the role of metacognition in academic writing; (2) the role of metacognition in critical thinking; and (3) correlations between metacognition, critical thinking skills, and academic writing. The results indicated significant relationships between the three variables, and the implications based on these findings were discussed.

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Acknowledgements

The study was supported by the Project from the Education Department of Hainan Province (Project number: Hnky2020ZD-9). We appreciate Professor Chuang Wang’ help in proofreading this article.

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Teng, M.F., Yue, M. Metacognitive writing strategies, critical thinking skills, and academic writing performance: A structural equation modeling approach. Metacognition Learning 18 , 237–260 (2023). https://doi.org/10.1007/s11409-022-09328-5

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Received : 27 March 2022

Accepted : 07 November 2022

Published : 24 November 2022

Issue Date : April 2023

DOI : https://doi.org/10.1007/s11409-022-09328-5

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