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Fostering Metacognition to Support Student Learning and Performance

  • Julie Dangremond Stanton
  • Amanda J. Sebesta
  • John Dunlosky

*Address correspondence to: Julie Dangremond Stanton ( E-mail Address: [email protected] ).

Department of Cellular Biology, University of Georgia, Athens, GA 30602

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Department of Biology, Saint Louis University, St. Louis, MO 63103

Department of Psychological Sciences, Kent State University, Kent, OH 44240

Metacognition is awareness and control of thinking for learning. Strong metacognitive skills have the power to impact student learning and performance. While metacognition can develop over time with practice, many students struggle to meaningfully engage in metacognitive processes. In an evidence-based teaching guide associated with this paper ( ), we outline the reasons metacognition is critical for learning and summarize relevant research on this topic. We focus on three main areas in which faculty can foster students’ metacognition: supporting student learning strategies (i.e., study skills), encouraging monitoring and control of learning, and promoting social metacognition during group work. We distill insights from key papers into general recommendations for instruction, as well as a special list of four recommendations that instructors can implement in any course. We encourage both instructors and researchers to target metacognition to help students improve their learning and performance.


Supporting the development of metacognition is a powerful way to promote student success in college. Students with strong metacognitive skills are positioned to learn more and perform better than peers who are still developing their metacognition (e.g., Wang et al. , 1990 ). Students with well-developed metacognition can identify concepts they do not understand and select appropriate strategies for learning those concepts. They know how to implement strategies they have selected and carry out their overall study plans. They can evaluate their strategies and adjust their plans based on outcomes. Metacognition allows students to be more expert-like in their thinking and more effective and efficient in their learning. While collaborating in small groups, students can also stimulate metacognition in one another, leading to improved outcomes. Ever since metacognition was first described ( Flavell, 1979 ), enthusiasm for its potential impact on student learning has remained high. In fact, as of today, the most highly cited paper in CBE—Life Sciences Education is an essay on “Promoting Student Metacognition” ( Tanner, 2012 ).

Despite this enthusiasm, instructors face several challenges when attempting to harness metacognition to improve their students’ learning and performance. First, metacognition is a term that has been used so broadly that its meaning may not be clear ( Veenman et al. , 2006 ). We define metacognition as awareness and control of thinking for learning ( Cross and Paris, 1988 ). Metacognition includes metacognitive knowledge , which is your awareness of your own thinking and approaches for learning. Metacognition also includes metacognitive regulation , which is how you control your thinking for learning ( Figure 1 ). Second, metacognition includes multiple processes and skills that are named and emphasized differently in the literature from various disciplines. Yet upon examination, the metacognitive processes and skills from different fields are closely related, and they often overlap (see Supplemental Figure 1). Third, metacognition consists of a person’s thoughts, which may be challenging for that person to describe. The tacit nature of metacognitive processes makes it difficult for instructors to observe metacognition in their students, and it also makes metacognition difficult for researchers to measure. As a result, classroom intervention studies of metacognition—those that are necessary for making the most confident recommendations for promoting student metacognition—have lagged behind foundational and laboratory research on metacognitive processes and skills.

FIGURE 1. Metacognition framework commonly used in biology education research (modified from Schraw and Moshman, 1995 ). This theoretical framework divides metacognition into two components: metacognitive knowledge and metacognitive regulation. Metacognitive knowledge includes what you know about your own thinking and what you know about strategies for learning. Declarative knowledge involves knowing about yourself as a learner, the demands of the task, and what learning strategies exist. Procedural knowledge involves knowing how to use learning strategies. Conditional knowledge involves knowing when and why to use particular learning strategies. Metacognitive regulation involves the actions you take in order to learn. Planning involves deciding what strategies to use for a future learning task and when you will use them. Monitoring involves assessing your understanding of concepts and the effectiveness of your strategies while learning. Evaluating involves appraising your prior plan and adjusting it for future learning.

How do undergraduate students develop metacognitive skills?

To what extent do active learning and generative work 1 promote metacognition?

To what extent do increases in metacognition correspond to increases in achievement in science courses?

FIGURE 2. (A) Landing page for the Student Metacognition guide. The landing page provides a map with sections an instructor can click on to learn more about how to support students’ metacognition. (B) Example paper summary showing instructor recommendations. At the end of each summary in our guide, we used italicized text to point out what instructors should know based on the paper’s results.

The organization of this essay reflects the organization of our evidence-based teaching guide. In the guide, we first define terms and provide important background from papers that highlight the underpinnings and benefits of metacognition ( ). We then explore metacognition research by summarizing both classic and recent papers in the field and providing links for readers who want to examine the original studies. We consider three main areas related to metacognition: 1) student strategies for learning, 2) monitoring and control of learning, and 3) social metacognition during group work.


What strategies do students use for learning.

First our teaching guide examines metacognition in the context of independent study (
-learning-strategies ). When students transition to college, they have increased responsibility for directing their learning, which includes making important decisions about how and when to study. Students rely on their metacognition to make those decisions, and they also use metacognitive processes and skills while studying on their own. Empirical work has confirmed what instructors observe about their own students’ studying—many students rely on passive strategies for learning. Students focus on reviewing material as it is written or presented, as opposed to connecting concepts and synthesizing information to make meaning. Some students use approaches that engage their metacognition, but they often do so without a full understanding of the benefits of these approaches ( Karpicke et al. , 2009 ). Students also tend to study based on exam dates and deadlines, rather than planning out when to study ( Hartwig and Dunlosky, 2012 ). As a result, they tend to cram, which is also known in the literature as massing their study. Students continue to cram because this approach is often effective for boosting short-term performance, although it does not promote long-term retention of information.

Which Strategies Should Students Use for Learning?

Here, we make recommendations about what students should do to learn, as opposed to what they typically do. In our teaching guide, we highlight three of the most effective strategies for learning: 1) self-testing, 2) spacing, and 3) interleaving (
#whatstudentsshould ). These strategies are not yet part of many students’ metacognitive knowledge, but they should know about them and be encouraged to use them while metacognitively regulating their learning. Students self-test when they use flash cards and answer practice questions in an attempt to recall information. Self-testing provides students with opportunities to monitor their understanding of material and identify gaps in their understanding. Self-testing also allows students to activate relevant knowledge and encode prompted information so it can be more easily accessed from their memory in the future ( Dunlosky et al. , 2013 ).

Students space their studying when they spread their learning of the same material over multiple sessions. This approach requires students to intentionally plan their learning instead of focusing only on what is “due” next. Spacing can be combined with retrieval practice , which involves recalling information from memory. For example, self-testing is a form of retrieval practice. Retrieval practice with spacing encourages students to actively recall the same content across several study sessions, which is essential for consolidating information from prior study periods ( Dunlosky et al. , 2013 ). Importantly, when students spread their learning over multiple sessions, they are less susceptible to superficial familiarity with concepts, which can mislead them into thinking they have learned concepts based on recognition alone ( Kornell and Bjork, 2008 ).

Students interleave when they alternate studying of information from one category with studying of information from another category. For example, when students learn categories of amino acid side groups, they should alternate studying nonpolar amino acids with polar amino acids. This allows students to discriminate across categories, which is often critical for correctly solving problems ( Rohrer et al. , 2020 ). Interleaving between categories also supports student learning because it usually results in spacing of study.

How are students enacting specific learning strategies, and do different students enact them in different ways?

To what extent do self-testing, spacing, and interleaving support achievement in the context of undergraduate science courses?

What can instructors do to increase students’ use of effective learning strategies?

What Factors Affect the Strategies Students Should Use to Learn?

Next, we examined the factors that affect what students should do to learn. Although we recommend three well-established strategies for learning, other appropriate strategies can vary based on the learning context. For example, the nature of the material, the type of assessment, the learning objectives, and the instructional methods can render some strategies more effective than others ( Scouller, 1998 ; Sebesta and Bray Speth, 2017 ). Strategies for learning can be characterized as deep if they involve extending and connecting ideas or applying knowledge and skills in new ways ( Baeten et al. , 2010 ). Strategies can be characterized as surface if they involve recalling and reproducing content. While surface strategies are often viewed negatively, there are times when these approaches can be effective for learning ( Hattie and Donoghue, 2016 ). For example, when students have not yet gained background knowledge in an area, they can use surface strategies to acquire the necessary background knowledge. They can then incorporate deep strategies to extend, connect, and apply this knowledge. Importantly, surface and deep strategies can be used simultaneously for effective learning. The use of surface and deep strategies ultimately depends on what students are expected to know and be able to do, and these expectations are set by instructors. Openly discussing these expectations with students can enable them to more readily select effective strategies for learning.

What Challenges Do Students Face in Using Their Metacognition to Enact Effective Strategies?

How can students address challenges they will face when using effective—but effortful—strategies for learning?

What approaches can instructors take to help students overcome these challenges?


Metacognition can be investigated in the context of any learning task, but in the sciences, metacognitive processes and skills are most often investigated in the context of high-stakes exams. Because exams are a form of assessment common to nearly every science course, in the next part of our teaching guide, we summarized some of the vast research focused on monitoring and control before, during, and after an exam ( ). In the following section, we demonstrate the kinds of monitoring and control decisions learners make by using an example of introductory biology students studying for an exam on cell division. The students’ instructor has explained that the exam will focus on the stages of mitosis and cytokinesis, and the exam will include both multiple-choice and short-answer questions.

How Should Students Use Metacognition while Preparing for and Taking an Exam?

As students prepare for an exam, they can use metacognition to inform their learning. Students can consider how they will be tested, set goals for their learning, and make a plan to meet their goals. It is expected that students who set specific goals while planning for an exam will be more effective in their studying than students who do not make specific goals. For example, a student who sets a specific goal to identify areas of confusion each week by answering end-of-chapter questions each weekend is expected to do better than a student who sets a more general goal of staying up-to-date on the material. Although some studies include goal setting and planning as one of many metacognitive strategies introduced to students, the influence of task-specific goal setting on academic achievement has not been well studied on its own in the context of science courses.

As students study, it is critical that they monitor both their use of learning strategies and their understanding of concepts. Yet many students struggle to accurately monitor their own understanding ( de Carvalho Filho, 2009 ). In the example we are considering, students may believe they have already learned mitosis because they recognize the terms “prophase,” “metaphase,” “anaphase,” and “telophase” from high school biology. When students read about mitosis in the textbook, processes involving the mitotic spindle may seem familiar because of their exposure to these concepts in class. As a result, students may inaccurately predict that they will perform well on exam questions focused on the mitotic spindle, and their overconfidence may cause them to stop studying the mitotic spindle and related processes ( Thiede et al. , 2003 ). Students often rate their confidence in their learning based on their ability to recognize, rather than recall, concepts.

Instead of focusing on familiarity, students should rate their confidence based on how well they can retrieve relevant information to correctly answer questions. Opportunities for practicing retrieval, such as self-testing, can improve monitoring accuracy. Instructors can help students monitor their understanding more accurately by encouraging students to complete practice exams and giving students feedback on their answers, perhaps in the form of a key or a class discussion ( Rawson and Dunlosky, 2007 ). Returning to the example, if students find they can easily recall the information needed to correctly answer questions about cytokinesis, they may wisely decide to spend their study time on other concepts. In contrast, if students struggle to remember information needed to answer questions about the mitotic spindle, and they answer these questions incorrectly, then they can use this feedback to direct their efforts toward mastering the structure and function of the mitotic spindle.

While taking a high-stakes exam, students can again monitor their performance on a single question, a set of questions, or an entire exam. Their monitoring informs whether they change an answer, with students tending to change answers they judge as incorrect. Accordingly, the accuracy of their monitoring will influence whether their changes result in increased performance ( Koriat and Goldsmith, 1996 ). In some studies, changing answers on an exam has been shown to increase student performance, in contrast to the common belief that a student’s first answer is usually right ( Stylianou-Georgiou and Papanastasiou, 2017 ). Changing answers on an exam can be beneficial if students return to questions they had low confidence in answering and make a judgment on their answers based on the ability to retrieve the information from memory, rather than a sense of familiarity with the concepts. Two important open questions are:

What techniques can students use to improve the accuracy of their monitoring, while preparing for an exam and while taking an exam?

How often do students monitor their understanding when studying on their own?

How Should Students Use Metacognition after Taking an Exam?

How do students develop metacognitive regulation skills such as evaluation?

To what extent does the ability to evaluate affect student learning and performance?

When students evaluate the outcome of their studying and believe their preparation was lacking, to what degree do they adopt more effective strategies for the next exam?


Next, our teaching guide covers a relatively new area of inquiry in the field of metacognition called social metacognition , which is also known as socially shared metacognition (
-group-work ). Science students are expected to learn not only on their own, but also in the context of small groups. Understanding social metacognition is important because it can support effective student learning during collaborations both inside and outside the classroom. While individual metacognition involves awareness and control of one’s own thinking, social metacognition involves awareness and control of others’ thinking. For example, social metacognition happens when students share ideas with peers, invite peers to evaluate their ideas, and evaluate ideas shared by peers ( Goos et al. , 2002 ). Students also use social metacognition when they assess, modify, and enact one another’s strategies for solving problems ( Van De Bogart et al. , 2017 ). While enacting problem-solving strategies, students can evaluate their peers’ hypotheses, predictions, explanations, and interpretations. Importantly, metacognition and social metacognition are expected to positively affect one another ( Chiu and Kuo, 2009 ).

How do social metacognition and individual metacognition affect one another?

How can science instructors help students to effectively use social metacognition during group work?


We encourage instructors to support students’ success by helping them develop their metacognition. Our teaching guide ends with an Instructor Checklist of actions instructors can take to include opportunities for metacognitive practice in their courses ( ). We also provide a list of the most promising approaches instructors can take, called Four Strategies to Implement in Any Course (
-Strategies-to-Foster-Student-Metacognition.pdf ). We not only encourage instructors to consider using these strategies, but given that more evidence for their efficacy is needed from classroom investigations, we also encourage instructors to evaluate and report how well these strategies are improving their students’ achievement. By exploring and supporting students’ metacognitive development, we can help them learn more and perform better in our courses, which will enable them to develop into lifelong learners.

1 Generative work “involves students working individually or collaboratively to generate ideas and products that go beyond what has been presented to them” ( Andrews et al. , 2019 , p2). Generative work is often stimulated by active-learning approaches.


We are grateful to Cynthia Brame, Kristy Wilson, and Adele Wolfson for their insightful feedback on this paper and the guide. This material is based upon work supported in part by the National Science Foundation under grant number 1942318 (to J.D.S.). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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metacognitive strategies research paper

© 2021 J. D. Stanton et al. CBE—Life Sciences Education © 2021 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (


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

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.


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 :

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.

Figure 2 . 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


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).


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: .

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 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.

Table 1 . Description of critical thinking measurement (PENCRISAL).

Table 2 . Description of metacognition measurement (MAI).

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 ).

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 ).

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

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

Ethics Statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. 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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Keywords: critical thinking, instruction, evaluation, metacognition, problem-solving

Citation: Rivas SF, Saiz C and Ossa C (2022) Metacognitive Strategies and Development of Critical Thinking in Higher Education. Front. Psychol . 13:913219. doi: 10.3389/fpsyg.2022.913219

Received: 05 April 2022; Accepted: 19 May 2022; Published: 15 June 2022.

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*Correspondence: Silvia F. Rivas, [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.

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Towards a Theory of Thinking pp 203–214 Cite as

The Development of Metacognitive Competences

  • Wolfgang Schneider 4  
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Part of the book series: On Thinking ((ONTHINKING))

This paper describes historical and current trends in research on the development of metacognitive competencies. Stimulated by classic theoretical analyses of the concept of metacognition initiated by Ann Brown, John Flavell and their colleagues, contemporary extensions of the concept emphasize the important roles of both procedural and declarative metacognition for successful information processes. Major research findings on the development of these two components of metacognition are reviewed, and links between children’s early “theory of mind” and subsequent verbalizable metamemory are described. Next, new evidence on children’s metacognitive development in childhood and adolescence is summarized, indicating major shifts in children’s declarative metacognitive knowledge, in particular, their strategy knowledge, between the end of kindergarten and the end of elementary school. Although similarly fast developments could not be demonstrated for procedural metacognitive knowledge, several empirical studies suggest developmental changes in the relationship between monitoring and self-regulatory abilities, with older (but not younger) children being able to regulate their ­achievement-related behavior based on the outcome of their monitoring attempts. Finally, the paper reviews classic and contemporary applications of ­metacognitive theory to various educational settings, generally illustrating the importance of metacognition for various aspects of academic performance.

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Schneider, W. (2010). The Development of Metacognitive Competences. In: Glatzeder, B., Goel, V., Müller, A. (eds) Towards a Theory of Thinking. On Thinking. Springer, Berlin, Heidelberg.

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A questionnaire-based validation of metacognitive strategies in writing and their predictive effects on the writing performance of English as foreign language student writers

Ruru Zhang,

Yanling Xiao,

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.


This study—drawing upon data from a questionnaire—examined 503 Chinese university students’ metacognitive strategies in writing (MSW). The focus was on Chinese student writers who are learning English as a foreign language (EFL).

The examination was conducted through a survey on MSW and a writing test administered at the end of the semester. We employed exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) for data analysis. Multiple regression analysis was also adopted for understanding the predictive effects of strategies on writing performance.

The findings provided validity to MSW, including person, task, strategies, planning, monitoring, and evaluating. The different components of MSW were reported to significantly affect the participants’ writing performance. The findings highlight that EFL student writers were aware of metacognitive writing strategies. The MSW survey could be used to assess EFL students’ metacognitive writing strategies and develop curricula in writing strategy training.

Writing instruction can direct learners’ ability to acquire metacognitive writing strategies, particularly those of planning, monitoring, and evaluating, to build their awareness as agents in EFL writing. Relevant pedagogical implications are discussed.

Metacognitive strategies are essential to the process of learning to write when learning English as a foreign language (EFL; Nguyen and Gu, 2013 ; Teng, 2016 , 2019 ; Teng and Yue,2022 ). However, in the Chinese EFL context, for which English writing instruction typically emphasizes grammatical correctness rather than idea development, learners may find it difficult to build an awareness of using metacognitive writing strategies ( Ruan, 2014 ). Through a mixed-methods study, Amani (2014) found that explicit metacognitive strategy instruction had a positive impact on the writing competence of L2 writing students. However, in terms of EFL writing, university EFL students may find it challenging because of their lack of awareness of metacognitive writing strategies ( Teng, 2019 ). In addition, EFL learners in the Chinese context receive limited English language input, making it more challenging to learn to write. Student writers are expected to have repertoires of strategies when learning to write ( Raimes, 1987 ). In particular, they need to build an advanced level of “self-initiated thoughts, feelings, and actions” for them to “attain various literary goals” ( Zimmerman and Risemberg, 1997 , p.76). Hence, metacognitive writing strategies are essential to possible improvements in EFL writing.

Nevertheless, even though students are taught how to plan, monitor, and evaluate their own writing, students may know little about themselves as writers ( Leung and Hicks, 2014 ). They may also not recognize their own writing strengths or weaknesses, tending to overemphasize the latter and overlook any progress they have made or can make in their writing ( Teng, 2016 ). Wenden (1998) argued that metacognitive knowledge is a prerequisite for self-regulation, and metacognitive knowledge is essential to learner autonomy because it “informs planning decisions taken at the outset of learning and the monitoring processes that regulate the completion of a learning task and decisions to remediate; it also provides the criteria for evaluation made once a learning task is completed” (p. 528). Teng and Zhang (2021) argued that there is a dynamic and longitudinal relationship between metacognitive knowledge and reading and writing in a foreign language context. However, teachers may not recognize the importance of metacognitive knowledge in Chinese EFL writing contexts, wherein teaching academic writing is product oriented ( Teng and Zhang, 2016 ). The student writers were passive and found it difficult to keep positive beliefs in writing ( Bruning and Horn, 2000 ). This may be related to learners’ lack of awareness of self-regulation in writing. They may exert more effort learning vocabulary knowledge and grammar for writing, rather than being an agent for writing ( Graham and Harris, 2000 ). Student writers need self-awareness, motivation, and positive behavioral skills for writing ( Zimmerman, 2002 , p.65–66). Metacognitive writing strategies are thus essential to EFL students’ writing performance.

Self-regulation principles, measurements, and practices have a solid ground for enriching second and foreign language learning and teaching ( Teng and Zhang, 2022 ). Through a socio-cognitive approach to writing, Nishino and Atkinson (2015) argued that writing is primarily a cognitive activity and that cognition plays a vital role in writing and its development. To help students become competent English writers and autonomous learners, instructors need to support their development of metacognitive strategies. However, scarce attention was paid to writing strategies from the perspective of metacognition, particularly for low-achieving students in the EFL context. The present study examined Chinese university EFL students’ metacognitive strategies in EFL writing. We aim for the following purposes: (a) to assess the reliability of a new scale, which we named it as metacognitive strategies in writing (MSW) and (b) to explore how different components of MSW predict EFL students’ writing performance. The findings are insightful in helping researchers and classroom practitioners to diagnose the needs of metacognitive strategies in writing and develop guidelines for instructing writing courses for university EFL students. The findings shed lights on how to teach EFL writing and deliver more effective program for writing teacher preparation.

Literature review

Language learning strategies.

Oxford (1990) classified a list of language learning strategies based on cognitive learning theory. These strategies include memory, cognitive, compensatory, affective, social, and metacognitive strategies. Past studies have documented differences in strategy use between more and less successful learners. For example, successful learners use these strategies in larger numbers and at higher frequencies ( Magogwe and Oliver, 2007 ). Most importantly, cognitive and metacognitive strategies are associated with a higher level of language proficiency ( Peacock and Ho, 2003 ). However, contradictory findings were also reported, showing that less successful learners used more strategies than more successful learners did because the former automatized their language learning process ( Oxford and Cohen, 1992 ). Another point worth noting is that unsuccessful learners may adopt a large number of strategies frequently, but it does not necessarily mean that they are able to identify appropriate strategy use. In fact, it was reported that successful learners were able to identify appropriate strategies depending on the task requirements, but unsuccessful learners failed to choose the most appropriate and efficient strategies during the task ( Chamot and El-Dinary, 1999 ).

Although ample research has been reported relating to learners’ proficiency level and strategy use, learner variables, such as cultural background and national origin, could have a strong influence on learners’ strategy use ( Oxford and Nyikos, 1989 ). Therefore, their findings might not be generalizable to learners with completely different cultural backgrounds. In light of this, Lai (2009) conducted a questionnaire survey that investigated the relationships between the language learning strategies used by 418 EFL learners in Taiwan based on learners’ language proficiency and their use of strategies. While the more proficient learners used metacognitive strategies and cognitive strategies most frequently and memory strategies least frequently, the less proficient learners preferred social and memory strategies to cognitive and metacognitive strategies. This finding partially echoes Wu (2008) , who reported that higher-proficiency EFL students in Taiwan used learning strategies more often than lower-proficiency EFL students did, especially the cognitive, metacognitive and social strategies.

Although research documented in the literature examines general language learning strategy use, it is possible that these summarized findings could serve as a reference for the specific examination of metacognitive strategy use during English writing.

Understanding metacognition

Metacognition is multidimensional and domain-general. When we talk about metacognition, we may need to mention the theory of mind ( Flavell, 1979 ). Such theory is the foundation of understanding metacognition. Generally, metacognition is related to self-regulatory capacity because metacognition provides individuals with domain knowledge and regulatory skills that are essential to become an agentive learner in relevant domains ( Schraw, 2001 , p. 7). Metacognition refers to how learners build an awareness of their own thinking processes and executive processes ( Flavell, 1979 ). Metacognition is essential to helping learners regulate their cognitive processes, and finally, becoming an independent thinker and learner. Zhang and Zhang (2019) applied metacognition in second and foreign language learning, and posited that EFL learners need to plan, monitor, and evaluate their cognitive processes for better language learning performance.

Metacognition includes metacognitive knowledge and metacognitive regulation. Flavell (1985) suggested that person, task, and strategy knowledge are three key elements of metacognitive knowledge. Wenden (1998) explained the three elements. For example, person knowledge is the knowledge for the learners to control their cognitive processes. Task knowledge is the knowledge that can be helpful for the learners to understand the purpose, nature, and demands of different task conditions. Strategy knowledge is the knowledge of different important strategies that are helpful for realizing the pre-determined goals. Metacognitive regulation entails three skills: planning, monitoring, and evaluating ( Schraw, 1998 ). Planning refers to the ability to appropriately select the strategies and adequately allocate the resources for completing tasks. Monitoring refers to learners’ capacity to observe their task performance. Evaluating means learners’ capacity to reflect on their learning outcome and the use of different strategies for self-regulation.

Teng et al. (2022) summarized the procedures of understanding metacognition. First, monitoring function and control of cognition are two important functions of metacognition. In order to realize the functions, individuals need to process three major stages, i.e., acquisition, retention, and retrieval. Second, learners need metacognitive knowledge and metacognitive experiences to process the monitoring function. In contrast, they need metacognitive strategies or metacognitive skills to fulfill the needs of control of cognition. Third, metacognitive knowledge, metacognitive experiences, and metacognitive skills are interconnected with each other. Metacognitive knowledge includes person, task, and strategies. Metacognitive experiences include feelings and judgments. Metacognitive skills are important for their metacognitive regulation, which needs learners to plan, monitor, and evaluate their learning process. Finally, reflection is the outcome of the interconnected process of planning, monitoring, and evaluating ( Figure 1 ).

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The multifaceted elements of metacognition ( Teng et al., 2022 , p. 171).

Metacognitive strategies in EFL writing

Macaro (2010) maintains that strategic behavior plays a vital role in second language learning success and proposes that strategic behavior should be essential to linguistic knowledge resources. Dornyei (2010) emphasizes that students need a repertoire of appropriate task-related plans, scripts, and self-regulatory strategies that are activated by their ideal L2 selves; that is, learners’ aptitude, motivation, goals, and self-regulatory strategies all interact and affect one another in the SLA process. Writing strategies include rhetorical strategies, metacognitive strategies, cognitive strategies, and social/affective strategies ( Wenden, 1991 ; Riazi, 1997 ). Writers explore rhetorical strategies to organize and present their ideas based on the writing conventions of the target language. Metacognitive strategies are used to monitor the writing process consciously and evaluate the effectiveness of writing actions. Cognitive strategies are used to implement actual writing actions. Social/affective strategies are employed to interact with others and to regulate emotions, motivation, and attitudes in writing.

Wenden (1991) classifies writing strategies based on metacognitive and cognitive frameworks. She distinguishes general executive metacognitive strategies of planning, self-monitoring, and self-evaluating from more specific cognitive strategies, such as clarification, retrieval, resourcing, avoidance, and verification. Each of these metacognitive strategies is discussed below.

Planning for writing involves thinking and self-questioning strategies such as identifying one’s purpose, activating background knowledge, and organizing ideas. Planning is not limited to a specific stage of writing but rather appears recursively throughout the writing process. Flower and Hayes (1981) identified three different types of planning strategies based on the focus of the goal: (1) generating ideas; (2) setting procedural goals; and (3) organizing. Generating ideas includes retrieving information from long-term memory, revising old ideas to incorporate new information, drawing inferences, making connections, and looking for examples, contradictions, and objections. Setting procedural goals includes content goals (e.g., plans for content, text structure and audience, and criteria for evaluation) and process goals (how to proceed, generated by the writer, done at any time during the composing process, followed or preceded by generating ideas, revising strategies, etc.). The third strategy (organizing) includes selecting the most useful materials produced during the generating process and organizing them in the writing plan. Organizing strategies include grouping and sequencing ideas, deciding on the presentation of the text, planning the introduction and conclusions, and structuring the text based on a particular genre. Furthermore, in using these strategies, it is essential to consider the audience, topic, and rhetorical knowledge. Planning in EFL writing determines how writers write in subsequent stages. It engages them in metacognitive activities that allow them to consider the purpose and goals for writing, identify their audience, decide upon voice, and generate a framework for their essays.

Monitoring involves conscious control and regulation of the writing process. Hayes and Flower (1980) include self-monitoring in their model of the cognitive processes of writing, noting that the ability to self-monitor the composing process is an important part of writing strategies. Charles (1990) claims that self-monitoring makes it easier for L2 students to avoid uncertainty about any part of their text, to find direct answers to their queries and to encourage them “to look critically and analytically at their writing and to place themselves in the position of readers” (p. 289). The more important functions of self-monitoring are controlling, directing, and sequencing the composing processes and one’s progress in the task. Monitoring allows the writer to decide whether something needs to be retrieved, whether new ideas need to be further generated, or whether a given subprocess has ended. Monitoring allows L2 writers to evaluate the effectiveness of writing strategies and how and when to check the outcomes of problem-solving processes and strategically regulate the processes according to cognitive goals ( Mayer, 1999 ).

Self-evaluating—experiencing the quality of one’s writing in relation to one’s goals—is crucial for developing an individual’s perception of writing. In self-evaluation, students can recognize weaknesses, identify needs, and make changes ( Zimmerman, 2002 ). In cognitive research, evaluation has been characterized as a strategy for considering the outcome of the undertaken task, an essential metacognitive strategy that successful learners need to execute and control.

Empirical studies on the use of metacognitive writing strategies

Various studies have been conducted on EFL students’ use of metacognitive writing strategies. Employing think-aloud protocols and immediate retrospective interviews, Chien (2012) investigated the differences in writing strategies and English writing achievements of 20 low-achieving and 20 high-achieving student writers in Taiwan. Chien found that high-achieving student writers were more aware of and focused more on, formulating their position statements when planning, generating, revising, and editing their essays and focused more on correcting grammatical and spelling errors. Teng and Zhang (2016) validated questionnaire-based self-regulated strategies in EFL writing and highlighted planning, monitoring, and evaluating in EFL writing. Teng and Huang (2019) also suggested that learners’ self-regulated strategies in writing, as well as their English proficiency and language learning experiences, and significantly influenced their EFL writing. In a recent publication ( Teng et al., 2022 ), two experimental studies were reported. Study 1 adopted a factorial design using exploratory and confirmatory factor analysis to validate a self-regulatory writing strategy questionnaire. Study 2 assessed the predictive effects of the different components of the scale on students’ writing performance. The results supported the construct validity for the six strategy factors, i.e., writing planning, goal-oriented monitoring, goal-oriented evaluation, emotional control, memorization, and metacognitive judgment. The factors also predicted writing performance. Zhang and Qin (2018) also validated the newly developed scale on metacognitive strategies in a multimedia writing context. The results provided evidence for the validation of planning, monitoring, and evaluating strategies. In an early empirical study on the importance of planning in EFL writing, Graham et al. (1995) examined differences between expert and less-skilled L2 writers. They found that expert L2 writers spent considerable time planning and appeared to have higher-level plans and self-conscious control of their planning. In contrast, less-skilled EFL writers were less likely to use knowledge of textual structure in planning, to use heuristic strategies in searching their memory for content, or to establish goals to direct the writing process and were more likely to engage in “knowledge telling” (i.e., writing everything they knew about a topic and stopping when they felt that they had written down everything they knew). Less-skilled writers did not write with goals or plans in mind; rather, they tended to generate ideas through free writing and usually did not organize those ideas. As shown in a longitudinal study ( Teng and Zhang, 2021 ), learners’ L2 writing development was dependent on their initial level of metacognitive knowledge. This is evidence for the strong correlation between metacognitive knowledge and writing.

Nguyen and Gu (2013) explored the impact of strategy-based instruction on promoting learner autonomy (operationally defined as learner self-initiation and learner self-regulation) of students at a Vietnamese university; 37 students were in an experimental group, and 54 students were in two control groups. After an 8-week metacognition training intervention, students in the experimental group were found to have improved their planning, monitoring, and evaluating of a writing task more than those in the two control groups. The findings suggest that strategy-based instruction on task-specific metacognitive self-regulation improves learner autonomy and writing performance. Teng (2020) also incorporated training of metacognitive strategies for EFL learners. There were two groups of learners, i.e., those with group feedback guidance and those with self-explanation guidance. The results supported the positive effects of group metacognitive support on EFL students’ writing. EFL students need to build a certain level of metacognitive awareness to manage themselves as writers.

Bai et al. (2014) conducted a questionnaire survey to explore the relationship between 1,618 Singapore primary school pupils’ reported use of strategies in learning to write and the correlation with their English language proficiency. They found that participants used a wide range of writing strategies at medium frequency. They also reported a significant correlation between the participants’ English language proficiency and the use of writing strategies such as planning, text-generating, revising, monitoring and evaluating, and resourcing. Similar results were also found in Bai and Guo (2021) , wherein high achievers reported higher levels of motivation (i.e., growth mindset, self-efficacy, and interest) and self-regulated learning strategy use than the average achievers, and average achievers reported more strategy use than the low achievers, Ma and Teng (2021) collected qualitative data from two undergraduate university students learning English as L2 in Hong Kong to explore their use of writing strategies. They reported that both students realized the importance of self-evaluation and revision. It seems that the students perceived affordances in the kind of writing that enabled them to play an active role in seeking, interpreting, and using teacher feedback to perform the evaluation and modification of their own work. However, variations in engagement in the process of learning to write and their metacognitive knowledge development were also detected. For example, students’ varying degrees of engagement may result in various degrees of developing metacognitive awareness. Teng et al. (2022) validated a new instrument, i.e., the Metacognitive Academic Writing Strategies Questionnaire (MAWSQ). Analyses were conducted through a series of Confirmatory factor analyses (CFA). Results supported two hypothesized models, i.e., an eight-factor correlated model and a one-factor second-order model. Model comparisons supported the role of metacognition as a higher-order construct. Metacognition also explains the eight metacognitive strategies, including declarative knowledge, procedural knowledge, conditional knowledge, planning, monitoring, evaluating, information management, and debugging strategies. Those strategies also significantly influenced EFL writing performance.

Overall, the studies on metacognition development reviewed in this section highlight the importance of the high-level cognitive processes involved in composing, the development of the autonomous and self-regulated use of effective writing strategies, and the formation of positive attitudes about writing. Metacognitively oriented learners are aware of both their own learner characteristics and the writing task and are able to select, employ, monitor, and evaluate their use of metacognitive strategies.

The present study

Metacognition functions as an important predictor in EFL writing performance. We aim for two purposes in the present study. First, we attempted to validate a questionnaire on metacognitive strategies in writing. Second, we assessed the predictive effects of different metacognitive strategies in the outcome EFL writing. The present study sheds light on learners’ awareness and use of metacognitive writing strategies. The present study includes two questions:

  • What is the evidence to support the validity and reliability of metacognitive strategies in writing?
  • What is the evidence for the predictive effects of metacognitive strategies on EFL writing proficiency?

Materials and methods


The present study included 503 participants. They were undergraduate students at a university in China. They were first-year students with Chinese as their first language and English as a foreign language. They had received at least 6 years of formal English instruction. Writing is a subject to be taught in college English and a compulsory course for all the participants. We selected the participants because they were all enrolled in a university English course. The first author was teaching the participants, and the sample of participants was a convenient sample. Among the 503 students, 351 were men and 152 were women. An unequal gender balance may be because most of the students were from science and engineering majors. Originally, there were 700 students who responded to the questionnaire. We finally selected data from 503 students for data analysis. Some participants’ data were excluded because of missing values or because some were unable to take the writing test. They attended the study voluntarily by signing the consent form.

Questionnaire development

The questionnaire, which was named Metacognitive Strategies in Writing (MSW), was developed through item generation, reference consultation, initial piloting, psychometric evaluation, and exploratory factor analysis (EFA) in a pilot study. We first invited 10 students to reflect on their writing practices and strategies. The students were mainly interviewed about the strategies they adopted for writing. We generated approximately 50 items based on analyzing the transcriptions of learners’ interviews. In the next stage, we consulted relevant literature on metacognition, self-regulation, and language learning strategies ( Schraw and Dennison, 1994 ; Oxford, 2013 ; Teng et al., 2022 ). We selected the items that fit with metacognition theories. In the third stage, we invited the 10 students to check the items. In the fourth stage, which was psychometric evaluation, we invited two researchers in L2 writing to assess the items. Based on the comments, we finally removed 10 items. In the final stage, we ran an EFA with a sample of 360 students with similar backgrounds. We deleted 10 items with unsatisfactory factor loading values. The final questionnaire includes 30 items, which are in the Appendix .

This questionnaire was a novel one as it was based on metacognition theory, through which the focus was on understanding metacognitive knowledge and regulation in learning to write. We adopted a seven-point Likert scale (i.e., from 1, Strongly disagree to 7, Strongly agree). MSW focuses on metacognitive knowledge and metacognitive regulation. Metacognitive knowledge includes three factors, i.e., person, task, and strategies. Metacognitive regulation includes three factors: planning, monitoring, and evaluating. Cronbach’s alpha, which ranged from 0.81 to 0.90 for the six factors, ensured the internal consistency of responses to the items. The questionnaires were administered to the participants in Chinese. The author translated into Chinese while a research assistant was invited to check the translated items through back translation.

Writing test

A writing test from IELTS (writing task 2) was adopted to measure learners’ writing proficiency. Students were required to write at least 250 words within 1 h. Students were asked to respond to the topic provided by giving and justifying an opinion, discussing the topic, summarizing details, outlining problems, identifying possible solutions and supporting what they wrote with reasons, arguments and relevant examples. The topic proposed the possible influence of social media sites on personal relationships.

The marking scheme was consistent with the writing rubrics in IELTS. However, we adjusted it to fit with our school assessment needs. Each learner was awarded with six marks for task response, coherence and cohesion, lexical resource, and grammatical range and accuracy. The maximum possible score was 24 points. A total of 40 English teachers were paid to rate the writing. The teachers did not know the participants’ identities. They also joined a training session on the marking scheme. Disagreements on marking were subject to further discussion. The Cronbach’s alpha for the test was.85, indicating acceptable reliability.

We invited 20 EFL teachers to help us distribute a QR code to the students through WeChat group. The students spent an average of 6 min completing the questionnaire. The writing test was administered as an exercise for all students during class. They needed to complete it within 1 h. The format for the writing test was a paper-and-pencil format. All participants received the same format for the questionnaire and the writing test.

Data analysis

The final dataset was run through a series of confirmatory factor analyses (CFAs). STATA was used for data analysis. CFA is used to test a theoretical model by confirming factors, correlations, covariance patterns, and residual or error values within a data matrix ( Byrne, 2016 ). We used the maximum likelihood (ML) estimation method. The model fit was evaluated through the following statistics: a chi-square statistic, the degrees of freedom (df), p value, the ratio of chi-square χ 2 divided by the df, the root mean square error of approximation (RMSEA), the standardized root mean square residual (SRMR), the comparative fit index (CFI), and the Tucker–Lewis Index (TLI; DiStefano and Hess, 2005 ). The following criteria are a relatively good fit between the hypothesized model and the observed data: the value of RMSEA should be close to 0.06, the value of SRMR should be close to 0.08, and the values for CFI and TLI should be close to 0.95 ( Hu and Bentler, 1999 ). Finally, multiple regression analysis was adopted to evaluate the predictive effects of MSW on students’ writing proficiency.

Descriptive statistics

The kurtosis and skewness values for the metacognitive strategies in writing, as well as the mean and standard deviation, are shown in Table 1 . The means of the six factors ranged from 3.346 to 4.079, with the two factors, monitoring and evaluating, greater than 4. There were no noticeable variations based on the standard deviation values.

Means, standard deviations. and normality test.

Exploratory factor analysis in the pilot study

Exploratory factor analysis was conducted on a sample of 360 learners from similar background in the pilot study. We examined the adequacy of the sample. The Kaiser-Meyer-Olkin value was 0.914, which appropriate for EFA ( Tabachnick and Fidell, 2001 ). Bartlett’s test of sphericity was significant, p < 0 .001; thus, the matrix was adequate for factor analysis. We adopted principal component analysis as a factor extraction method. We finally extracted six factors that explained 57.411% of the variance ( Table 2 ). The scree plot showed a considerable drop after the sixth factor, for which we excluded other possible factors. Based on key theories in metacognition, we named the six factors as following: person, task, strategies, planning, monitoring, and evaluating.

Extraction results for the six factors.

The six factors’ eigenvalues exceeded 1. The next step was to examine the factor loadings. We deleted 10 items with factor loadings lower than 0.4. The final version included 30 items across six factors ( Table 3 ). Items’ factor loadings ranged from 0.534 to 0.772, while communality ranged from 0.531 to 0.754. The items hence fit their respective factors well.

Results on factor loadings and the communality.

Construct validity of metacognitive strategies in writing through CFA

The data fitness metrics for metacognitive strategies in writing are displayed in Table 4 . Table 4 shows that the RMSEA was 0.073, less than 0.08, indicating a good fit; CFI, TLI, CNFI, IFI, and GFI all exceeded 0.9, which was ideal for adaptability. Although the χ 2 /df was 7.916, larger than 3, the scale on metacognitive strategies in writing still showed reliability when taken as a whole.

Model fit indices for metacognitive writing strategies.

According to Figure 2 and Table 5 , the factor loadings for Person, Task, Strategy, Planning and Evaluating were all greater than 0.5, while Monitoring was 0.41. Additionally, the average variance extracted (AVE) for each variable was 0.47, and the model’s convergent validity was good, as evidenced by the composite reliability (CR) being 0.84, indicating that the model had satisfactory convergent validity.

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A first-order model of metacognitive strategies in writing. Prs, Person; Tsk, Task; Str, Strategy; Pln, Planning; Mnt, Monitoring; and Evl, Evaluating.

Convergent validity of the model.

Predictive effect of metacognitive strategies in writing on EFL writing

Figure 3 presents the correlations between metacognitive strategies in writing and L2 learners’ writing proficiency in English. The findings indicated that each of the six metacognitive strategies was significantly correlated with learners’ English writing performance. Writing performance (WP) was correlated with Person ( r  = 0.264), Task ( r  = 0.500), Planning ( r  = 0.584), and Monitoring ( r  = 0.408). Strategy ( r  = 0.470) and Evaluating ( r  = 0.470) were significantly correlated with WP.

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Spearman correlation for metacognitive writing strategies and L2 learners’ proficiency in English. Persontotal, Person; Tasktotal, Task; Strategytotal, Strategy; Planningtotal, Planning; Monitoringtotal, Monitoring; and Evaluatingtotal, Evaluating.

Moreover, we adopted a structural equation model to investigate the degree to which metacognitive strategies in writing predicted learners’ L2 writing proficiency. Table 6 presents the model fitness indices. For our model, seven indices (i.e., χ 2 /df, RMSEA, CFI, TLI, NFI, WIFI, and GFI) indicated acceptable model fit ( Table 6 ). Figure 4 shows a structural equation model of the relationship between metacognitive strategies in writing and writing proficiency. The six variables on the left side of the model represent the six factors of metacognitive strategies in writing. The only rectangular variable on the right side of the model was EFL learners’ writing proficiency. The findings demonstrated that metacognitive strategies in writing had a predictive power of 0.65 for L2 learners’ writing proficiency, indicating that it could account for 65% of the variances in writing performance.

Model fit indices for metacognitive writing strategies on writing performance.

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The structural equation model of metacognitive strategies in writing proficiency.

Regression analysis was employed in the study to show the extent to which each factor impacts writing performance. The results presented in Table 7 demonstrate that all factors significantly predicted writing competence ( p  < 0.001), with the exception of Strategy ( p  = 0.344). Planning had the greatest effect on writing abilities, and Task had the least effect. Notably, monitoring and evaluating also had a great effect on EFL learners’ writing proficiency. According to the findings, there was no multicollinearity among the strategies, as indicated by the variance inflation factor (VIF), which was less than 3. In addition, the residuals adhered to a normal distribution, as shown in Figure 5 . This offered a trustworthy foundation for the regression analysis results.

Linear regression results.

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Normal P–P plot of regression standardized residual.

Discussion and conclusion

Overall, the present study aims to answer two research questions. The first research question entails the validation of a newly developed scale, which we named Metacognitive Strategies in Writing (MSW). The scale was developed based on metacognition theory. The findings supported the factorial structure of the scale. The second research question aims to answer the predictive effects of different factors of MSW in writing performance. Overall, the findings provided evidence for the factorial structure of MSW. The findings also suggested the predictive effects of different factors on writing performance.

Validation of MSW

First, MSW is with satisfactory psychometric properties. The six factors were reliable in terms of conceptual and empirical evidence. The six factors were distinct but correlated with each other. Consistent with previous studies ( Teng et al., 2022 ), metacognition is an important construct that can explain the significant correlations of different lower-order metacognitive dimensions in writing. In line with Schraw and Moshman (1995) , metacognition is a domain that can explain self-regulatory capacity. The present study thus provides insights into metacognition theory, which can entail person, task, strategies, planning, monitoring, and evaluating ( Schraw and Dennison, 1994 ). These strategies are interconnected and reflect the metacognitive process in writing. To build metacognitive awareness, learners need to be engaged in self-reflection and controlling of cognition ( Paris and Winograd, 1990 ). In terms of writing, student writers need to assess their knowledge states and executive abilities to orchestrate different dimensions of metacognitive awareness. Overall, the sum of the six strategies in writing indicates EFL student writers’ overall level of metacognitive awareness in writing.

The six factors were interpreted through metacognitive knowledge and regulation. The two paradigms were also conceptualized in early studies ( Flavell, 1979 ; Schraw, 1998 ; Wenden, 1998 ). In the present study, the two paradigms can represent key elements of metacognition. Person, task, and strategies represent learners’ beliefs and knowledge about themselves. Planning, monitoring, and evaluating reflect the process of cultivating one’ self-regulatory capacity for learning to write ( Teng and Zhang, 2016 ; Teng et al., 2022 ). The findings showed a positive and significant relationship between metacognitive knowledge and regulation ( Pugalee, 2001 ; Teng, 2016 ). We may need to reconsider the strong connection between metacognitive knowledge and regulation. The positive correlation may reflect the need of both knowledge and regulation in learning to write. For example, EFL students may need cognitive, metacognitive, and regulatory skills and strategies for writing ( Teng, 2020 ). The importance of metacognitive knowledge and regulation may reflect the argument by Wolters (1999) that learners’ engagement, effort, and achievement are influenced by their metacognitive knowledge and regulation. Hence, metacognition is essential to the development of self-regulated capacity ( Efklides, 2008 ), build identity as a student writer ( Zimmerman and Risemberg, 1997 , p.76), and develop self-awareness in processing their second and foreign language learning ( Zhang and Zhang, 2019 ).

Overall, the MSW data suggest that the student writers adopted metacognitive knowledge, i.e., person, task, and strategies, to understand their strengths and weakness in writing, demands in writing, and solutions for solving problems in writing. The data also suggest that the planning strategy should be used. In the planning stage, the student writers directed their attention to fulfilling the goal of the task, planning thoroughly, evaluating the relevance and effectiveness of ideas, and eliminating inappropriate examples. Data regarding the second subscale (monitoring) reflected that students tended to use some metacognitive monitoring strategies. During the monitoring stage, the student writers focused on the overall essay development, concentrating on expanding and developing their initial ideas, evaluating their essay for clear development and focus/unity, and ignoring interruptions posed by language constraints, such as grammar and vocabulary. For the third subscale (self-evaluating), student writers tended to use certain metacognitive strategies. Student writers prioritized their attention to evaluating the unity and effectiveness of their writing before editing local errors, such as grammar, vocabulary, mechanics, and sentence variation.

Predictive effects of metacognitive strategies in writing

The findings suggest the predictive effects of metacognitive strategies in writing. The results confirmed that the metacognitive strategies significantly predicted learners’ writing performance, which was consistent with previous studies ( Teng and Huang, 2019 ; Teng et al., 2022 ). One reason is that student writers’ meager metacognitive knowledge base could result in unsatisfactory cognitive monitoring of production and progress toward the writing task goal, which, in turn, may also affect their writing performance ( Teng et al., 2022 ). For example, lower-level writers tended to be bound to the local areas of writing, focusing on language correctness, while higher-level writers tended to focus on developing ideas and revising at the discourse level, saving editing until later ( Teng and Huang, 2019 ). As supported in previous studies ( Chien, 2012 ; Bai et al., 2014 ), higher level student writers were more aware of metacognitive strategies and used them more frequently in writing.

The argument revealed, at least for this particular sample and the chosen test, a strong and significant link between the writing abilities of EFL students and the factors of person, task, strategy, planning, monitoring, and evaluation. The EFL learners’ writing performance variations were accounted for by the six metacognitive components. The findings complement cognitive writing model of Flower and Hayes (1981) , which recognizes the abilities in process writing such as planning, monitoring, and reviewing. Writing necessitates the adaptive use of emotional strategies, performance strategies, and cognitive strategies ( Teng et al., 2022 ). The effectiveness of the strategies highlights the personal, behavioral, and environmental impacts on the regulatory capacity in learning to write ( Zimmerman and Risemberg, 1997 ).

In our study, person and task significantly predicted writing performance with a large effect size. According to earlier research ( Brown, 1987 ; Schraw, 2001 ), learners who have declarative, procedural, and conditional knowledge are more likely to become strategic learners. These results provide evidence for the idea that to master writing, EFL learners need to be able to distinguish among the various strategies, employ the appropriate strategies, and apply these strategies in their writing. The results also support earlier research that metacognitive knowledge is crucial for encouraging active involvement in applying their understanding of the writing process, recognizing the kinds of strategies useful in the growth of writing, and improving students’ writing outputs ( Ruan, 2014 ).

In terms of metacognitive regulation, planning, monitoring, and evaluating are also important for writing performance. The effect size was quite large in the current study, for which we can detect similar results in previous studies ( Teng, 2019 ; Teng et al., 2022 ). The writing abilities of students who were more self-controlled in their writing were higher in terms of goal setting, time management, and planning for writing resources ( Teng and Zhang, 2016 ). We argue that Chinese EFL students need an awareness of planning ahead and monitoring and evaluating their planning tactics to produce successful written essays. The success of EFL academic writing depends heavily on this method. Academic writing development may be seen as a complex process for student writers because it depends on how strategically they seek information and modify their planning techniques. Students who have prepared well for academic writing are typically those who have a high level of metacognitive awareness of their writing-related objectives ( Zhang and Qin, 2018 ). When composing their essays, lower-level writers often experienced difficulty in transferring ideas to paper during the planning, monitoring, and self-evaluating stages. The constraints in the lower-level writers’ knowledge system, including their limited linguistic competence (grammar and vocabulary), their confusion about their role as writers, their lack of knowledge strategies for overcoming writing difficulties, and their lack of knowledge of how and when to apply those strategies, impeded their composition of a meaningful essay. Consequently, many students tended to simultaneously engage in a few different stages of writing—planning, composing, revising, and editing—without any extra attention resources to monitor the overall unity and coherence of the essay, thus making the essay messy and confusing.

Limitations and implications

Despite the positive findings, we still need to acknowledge some limitations of this study. First, the strategies described in the questionnaire were still scarce, although we showed excellent content validity. Due to the limited amount of time the learners could invest in data collection, we did not assess metacognitive experiences, another crucial component of metacognition. Interview data with students were not conducted to yield adequate methods connected to metacognitive experiences. Second, a self-report questionnaire served as the foundation for this study. Because they are dependent on the use of self-reported information, surveys may not fully reflect learners’ actual metacognitive awareness and activities. The quantitative data in future studies should be triangulated with interview data. Third, the writing test should include additional activity categories that can gauge various writing abilities. We only used one writing performance indicator. The performance of student writers may also be impacted by individual characteristics, including their language learning experiences and English proficiency level ( Teng and Huang, 2019 ). Future studies might look at learners’ individual differences and their use of different metacognitive strategies.

However, there are also some implications based on the findings. Our findings suggest directions for pedagogy as well as future research. Considerations include issues of focus on form, development of metacognitive awareness to support metacognitive knowledge and strategies, and appreciation of the many aspects of metacognitive awareness that good L2 writing entails.

Data collected from the surveys suggest a strong connection between EFL student writers’ metacognitive knowledge and the regulation strategies they employ. Helping students become more aware of themselves as writers and the metacognitive resources upon which they can draw during the writing process may help them develop their writing competence. Language teachers and instructors should clearly instruct the importance of metacognitive strategies for EFL student writers. Related to this, metacognitive training should help students develop such awareness in learning to write. However, an important step in developing productive pedagogy for metacognitive training is assessing learners’ needs and understandings of their metacognitive strategies. The MSW might potentially contribute to EFL writing assessment in China. The MSW monitoring subscale identified the important first step in writing—planning—as a potential problem. So far as these Chinese EFL non-English major student writers were concerned, regardless of their level of English class or their majors, it seems that many of them may need to faster a metacognitive awareness. As a result, it might be helpful to provide these students with additional lessons on metacognitive strategies to address their concerns and the problems evident in their English writing. While dealing with grammatical errors is essential to writing instruction, the students should focus not only on identifying the errors and fixing them but also on finding out why they make those mistakes and how to avoid making them again. In other words, instead of correcting the errors, they should also develop their awareness of metacognitive strategies to improve their overall language competence. The instructors may also explicitly teach and demonstrate effective strategies to enhance vocabulary acquisition, such as making learners aware of lexical morphology (including word roots and suffixes), synonyms, antonyms, word categories, and similar spellings.

Clearly, it should not be assumed that learners who do not score high on norm-referenced assessments of their L2 writing need to focus exclusively on their metacognitive strategies, even though that is where they may think they need to work. Rather, these learners need to consider not only metacognitive strategies but also discourse organization and considerations of audience, voice, and genre ( Hyland, 2007 ). It is only through an approach raising their awareness of the various aspects that contribute to good writing and through work on writing and revision strategies that they will progress optimally. Additionally, to implement these recommendations for pedagogy, teachers themselves must have substantial knowledge, professional development, and practice regarding approaches to support L2 writing. In the Chinese context, knowledge must be processed and understood in light of the metacognition and experiences of students, colleagues, and the community.

Data availability statement

Ethics statement.

The studies involving human participants were reviewed and approved by Hainan University. The patients/participants provided their written informed consent to participate in this study.

Author contributions

CQ: Coordinated the study, drafted, and revised the manuscript. RZ: Data collection, drafted literature review. YX: Participated in the design of the study, revised the manuscript and performed the statistical analysis and data interpretation. All authors proofread and approved the final manuscript.

This article is supported by the Project from the Education Department of Hainan Province, Project number: Hnky2020ZD-9.

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.

Supplementary material

The Supplementary material for this article can be found online at:

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  • Published: 06 March 2024

Artificial intelligence and illusions of understanding in scientific research

  • Lisa Messeri   ORCID: 1   na1 &
  • M. J. Crockett   ORCID: 2 , 3   na1  

Nature volume  627 ,  pages 49–58 ( 2024 ) Cite this article

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  • Human behaviour
  • Interdisciplinary studies
  • Research management
  • Social anthropology

Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists’ visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community’s ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI.

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We thank D. S. Bassett, W. J. Brady, S. Helmreich, S. Kapoor, T. Lombrozo, A. Narayanan, M. Salganik and A. J. te Velthuis for comments. We also thank C. Buckner and P. Winter for their feedback and suggestions.

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Department of Anthropology, Yale University, New Haven, CT, USA

Lisa Messeri

Department of Psychology, Princeton University, Princeton, NJ, USA

M. J. Crockett

University Center for Human Values, Princeton University, Princeton, NJ, USA

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Messeri, L., Crockett, M.J. Artificial intelligence and illusions of understanding in scientific research. Nature 627 , 49–58 (2024).

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Received : 31 July 2023

Accepted : 31 January 2024

Published : 06 March 2024

Issue Date : 07 March 2024


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metacognitive strategies research paper


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