p-sig. (exact)
TOT_PRE, PENCRISAL pre-test; RD_PRE, Deductive reasoning pre-test; RI_PRE, Inductive reasoning pre-test; RP_PRE, Practical reasoning pre-test; TD_PRE, Decision making pre-test; SP_PRE, Problem solving pre-test; TOT_POST, PENCRISAL post-test; RD_ POST, Deductive reasoning post-test; RI_ POST, Inductive reasoning post-test; RP_ POST, Practical reasoning post-test; TD_ POST, Decision making post-test; SP_ POST, Problem solving post-test; Min, minimum, Max, maximum, Asym, asymmetry; and Kurt, kurtosis.
Description of metacognition measurement (MAI).
Variables | Min. | Max. | Media | Asym. | Kurt. | K-S p-sig (exact) | ||
---|---|---|---|---|---|---|---|---|
TOT_MAI_PRE | 89 | 145 | 233 | 192.13 | 16.636 | −0.071 | 0.275 | 0.557 |
Decla_PRE | 89 | 22 | 37 | 30.58 | 3.391 | −0.594 | −0.152 | 0.055 |
Proce_PRE | 89 | 9 | 19 | 14.52 | 2.018 | −0.560 | 0.372 | 0.004 |
Condi_PRE | 89 | 8 | 23 | 18.04 | 3.003 | −0.775 | 0.853 | 0.013 |
CONO_PRE | 89 | 44 | 77 | 63.15 | 6.343 | −0.384 | 0.044 | 0.445 |
Plani_PRE | 89 | 10 | 31 | 24.35 | 4.073 | −0.827 | 0.988 | 0.008 |
Orga_PRE | 89 | 26 | 48 | 38.20 | 4.085 | −0.307 | 0.331 | 0.022 |
Moni_PRE | 89 | 15 | 35 | 25.24 | 3.760 | −0.436 | 0.190 | 0.005 |
Depu_PRE | 89 | 14 | 25 | 20.71 | 2.144 | −0.509 | 0.310 | 0.004 |
Eva_PRE | 89 | 12 | 28 | 20.49 | 3.310 | −0.178 | −0.044 | 0.176 |
REGU_PRE | 89 | 97 | 160 | 128.99 | 12.489 | −0.070 | 0.043 | 0.780 |
OT_MAI_POST | 89 | 138 | 250 | 197.65 | 17.276 | −0.179 | 0.969 | 0.495 |
Decla_POST | 89 | 23 | 39 | 31.21 | 3.492 | −0.407 | 0.305 | 0.020 |
Proce_POST | 89 | 8 | 20 | 15.24 | 2.116 | −0.723 | 0.882 | 0.001 |
Condi_POST | 89 | 0 | 24 | 18.85 | 2.874 | −0.743 | 0.490 | 0.029 |
CONO_ POST | 89 | 44 | 82 | 65.30 | 6.639 | −0.610 | 1.014 | 0.153 |
Plani_ POST | 89 | 12 | 33 | 25.51 | 3.659 | −0.539 | 0.994 | 0.107 |
Orga_ POST | 89 | 27 | 48 | 39.40 | 4.150 | −0.411 | 0.053 | 0.325 |
Moni_ POST | 89 | 17 | 35 | 26.44 | 3.296 | −0.277 | 0.421 | 0.143 |
Depu_ POST | 89 | 15 | 24 | 20.40 | 2.245 | −0.214 | −0.531 | 0.023 |
Eva_ POST | 89 | 12 | 29 | 20.60 | 3.680 | −0.083 | −0.098 | 0.121 |
REGU_PRE | 89 | 94 | 168 | 132.35 | 12.973 | −0.227 | 0.165 | 0.397 |
TOT_MAI_PRE, MAI pre-test; Decla_PRE, Declarative pre-test; Proce_PRE, Procedural pre-test; Condi_PRE, Conditional pre-test; CONO_PRE, Knowledge pre-test; Plani_PRE, Planning pre-test; Orga_PRE, Organization pre-test; Moni_PRE, Monitoring pre-test; Depu_PRE, Troubleshooting pre-test; Eva_PRE, Evaluation pre-test; REGU_PRE, Regulation pre-test; TOT_MAI_POST, MAI post-test; Decla_ POST, Declarative post-test; Proce_ POST, Procedural post-test; Condi_ POST, Conditional post-test; CONO_ POST, Knowledge post-test; Plani_ POST, Planning post-test; Orga_POST, Organization post-test; Moni_ POST, Monitoring post-test; Depu_ POST, Troubleshooting post-test; Eva_ POST, Evaluation post-test; and REGU_ POST, Regulation post-test;
As we see in the description of all study variables, the evidence is that the majority of them adequately fit the normal model, although some present significant deviations which can be explained by sample size.
Next, to verify whether there were significant differences in the metacognition variable based on measurements before and after the intervention, we contrasted medians for samples related with Student’s t -test (see Table 3 ).
Comparison of the METAKNOWLEDGE variable as a function of PRE-POST measurements.
Variables | Mean Difference (CI 95%) | value | gl. | p-sig. (bilateral) | ||||
---|---|---|---|---|---|---|---|---|
TOT_MAI | Pre. | 89 | 192.13 | 16.636 | −8.152_−2.882 | −4.161 | 88 | 0.000 |
Post. | 89 | 197.65 | 17.276 | |||||
Decla | Pre. | 89 | 30.58 | 3.391 | −1.235_−0.023 | −2.063 | 88 | 0.042 |
Post. | 89 | 31.21 | 3.492 | |||||
Proce | Pre. | 89 | 14.52 | 2.018 | −1.210_−0.228 | −2.911 | 88 | 0.005 |
Post. | 89 | 15.24 | 2.116 | |||||
Condi. | Pre. | 89 | 18.04 | 3.003 | −1.416_−0.202 | −2.65 | 88 | 0.010 |
Post. | 89 | 18.85 | 2.874 | |||||
CONO | Pre. | 89 | 63.15 | 6.343 | −3.289_−1.025 | −3.787 | 88 | 0.000 |
Post. | 89 | 65.3 | 6.639 | |||||
Plan | Pre. | 89 | 24.35 | 4.073 | −1.742_−0.573 | −3.934 | 88 | 0.000 |
Post. | 89 | 25.51 | 3.659 | |||||
Orga | Pre. | 89 | 38.2 | 4.085 | −2.054_−0.350 | −2.803 | 88 | 0.006 |
Post. | 89 | 39.4 | 4.15 | |||||
Moni | Pre. | 89 | 25.24 | 3.76 | −1.924_−0.480 | −3.308 | 88 | 0.001 |
Post. | 89 | 26.44 | 3.296 | |||||
TS | Pre. | 89 | 20.71 | 2.144 | −0.159_−0.766 | 1.303 | 88 | 0.196 |
Post. | 89 | 20.4 | 2.245 | |||||
Eval | Pre. | 89 | 20.49 | 3.31 | −0.815_−0.613 | −0.282 | 88 | 0.779 |
Post. | 89 | 20.6 | 3.68 | |||||
REGU | Pre. | 89 | 128.99 | 12.489 | −5.364_−1.356 | −3.331 | 88 | 0.001 |
Post. | 89 | 132.35 | 12.973 |
The results show that there are significant differences in the metaknowledge scale total and in most of its dimensions, where all the post medians for both the scale overall and for the three dimensions of the knowledge factor (declarative, procedural, and conditional) are higher than the pre-medians. However, in the cognition regulation dimension, there are only significant differences in the total and in the planning, organization, and monitoring dimensions. The medians are also greater in the post-test than the pre-test. However, the troubleshooting and evaluation dimensions do not differ significantly after intervention.
Finally, for critical thinking skills, the results show significant differences in the scale total and in the five factors regarding the measurement time, where performance medians rise after intervention (see Table 4 ).
Comparison of the CRITICAL THINKING variable as a function of PRE-POST measurements.
Variables | N | M | SD | Student’s -test | ||||
---|---|---|---|---|---|---|---|---|
Mean difference (CI 95%) | value | gl. | p-sig. (bilateral) | |||||
TOT | Pre. | 89 | 25.146 | 5.436 | −8.720_−6.246 | −12.023 | 88 | 0.000 |
Post. | 89 | 32.629 | 5.763 | |||||
RD | Pre. | 89 | 2.978 | 3.391 | −2.298_−1.364 | −7.794 | 88 | 0.000 |
Post. | 89 | 4.809 | 3.492 | |||||
RI | Pre. | 89 | 4.213 | 1.627 | −1.608_−0.706 | −5.097 | 88 | 0.000 |
Post. | 89 | 5.371 | 1.547 | |||||
RP | Pre. | 89 | 18.04 | 2.248 | −1.416_−0.202 | −10.027 | 88 | 0.000 |
Post. | 89 | 18.85 | 2.295 | |||||
TD | Pre. | 89 | 63.15 | 1.796 | −3.083_−2.063 | −6.54 | 88 | 0.000 |
Post. | 89 | 65.3 | 1.748 | |||||
SP | Pre. | 89 | 24.35 | 2.058 | −1.135_−0.213 | −2.906 | 88 | 0.005 |
Post. | 89 | 25.51 | 1.812 |
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.
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.
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.
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.
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.
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.
Metacognition.
Chick, N. (2013). Metacognition. Vanderbilt University Center for Teaching. Retrieved [todaysdate] from https://cft.vanderbilt.edu/guides-sub-pages/metacognition/. |
Thinking about One’s Thinking | Putting Metacognition into Practice
Initially studied for its development in young children (Baker & Brown, 1984; Flavell, 1985), researchers soon began to look at how experts display metacognitive thinking and how, then, these thought processes can be taught to novices to improve their learning (Hatano & Inagaki, 1986). In How People Learn , the National Academy of Sciences’ synthesis of decades of research on the science of learning, one of the three key findings of this work is the effectiveness of a “‘metacognitive’ approach to instruction” (Bransford, Brown, & Cocking, 2000, p. 18).
Metacognitive practices increase students’ abilities to transfer or adapt their learning to new contexts and tasks (Bransford, Brown, & Cocking, p. 12; Palincsar & Brown, 1984; Scardamalia et al., 1984; Schoenfeld, 1983, 1985, 1991). They do this by gaining a level of awareness above the subject matter : they also think about the tasks and contexts of different learning situations and themselves as learners in these different contexts. When Pintrich (2002) asserts that “Students who know about the different kinds of strategies for learning, thinking, and problem solving will be more likely to use them” (p. 222), notice the students must “know about” these strategies, not just practice them. As Zohar and David (2009) explain, there must be a “ conscious meta-strategic level of H[igher] O[rder] T[hinking]” (p. 179).
Metacognitive practices help students become aware of their strengths and weaknesses as learners, writers, readers, test-takers, group members, etc. A key element is recognizing the limit of one’s knowledge or ability and then figuring out how to expand that knowledge or extend the ability. Those who know their strengths and weaknesses in these areas will be more likely to “actively monitor their learning strategies and resources and assess their readiness for particular tasks and performances” (Bransford, Brown, & Cocking, p. 67).
The absence of metacognition connects to the research by Dunning, Johnson, Ehrlinger, and Kruger on “Why People Fail to Recognize Their Own Incompetence” (2003). They found that “people tend to be blissfully unaware of their incompetence,” lacking “insight about deficiencies in their intellectual and social skills.” They identified this pattern across domains—from test-taking, writing grammatically, thinking logically, to recognizing humor, to hunters’ knowledge about firearms and medical lab technicians’ knowledge of medical terminology and problem-solving skills (p. 83-84). In short, “if people lack the skills to produce correct answers, they are also cursed with an inability to know when their answers, or anyone else’s, are right or wrong” (p. 85). This research suggests that increased metacognitive abilities—to learn specific (and correct) skills, how to recognize them, and how to practice them—is needed in many contexts.
In “ Promoting Student Metacognition ,” Tanner (2012) offers a handful of specific activities for biology classes, but they can be adapted to any discipline. She first describes four assignments for explicit instruction (p. 116):
Next are recommendations for developing a “classroom culture grounded in metacognition” (p. 116-118):
To facilitate these activities, she also offers three useful tables:
Weimer’s “ Deep Learning vs. Surface Learning: Getting Students to Understand the Difference ” (2012) offers additional recommendations for developing students’ metacognitive awareness and improvement of their study skills:
“[I]t is terribly important that in explicit and concerted ways we make students aware of themselves as learners. We must regularly ask, not only ‘What are you learning?’ but ‘How are you learning?’ We must confront them with the effectiveness (more often ineffectiveness) of their approaches. We must offer alternatives and then challenge students to test the efficacy of those approaches. ” (emphasis added)
She points to a tool developed by Stanger-Hall (2012, p. 297) for her students to identify their study strategies, which she divided into “ cognitively passive ” (“I previewed the reading before class,” “I came to class,” “I read the assigned text,” “I highlighted the text,” et al) and “ cognitively active study behaviors ” (“I asked myself: ‘How does it work?’ and ‘Why does it work this way?’” “I wrote my own study questions,” “I fit all the facts into a bigger picture,” “I closed my notes and tested how much I remembered,” et al) . The specific focus of Stanger-Hall’s study is tangential to this discussion, 1 but imagine giving students lists like hers adapted to your course and then, after a major assignment, having students discuss which ones worked and which types of behaviors led to higher grades. Even further, follow Lovett’s advice (2013) by assigning “exam wrappers,” which include students reflecting on their previous exam-preparation strategies, assessing those strategies and then looking ahead to the next exam, and writing an action plan for a revised approach to studying. A common assignment in English composition courses is the self-assessment essay in which students apply course criteria to articulate their strengths and weaknesses within single papers or over the course of the semester. These activities can be adapted to assignments other than exams or essays, such as projects, speeches, discussions, and the like.
As these examples illustrate, for students to become more metacognitive, they must be taught the concept and its language explicitly (Pintrich, 2002; Tanner, 2012), though not in a content-delivery model (simply a reading or a lecture) and not in one lesson. Instead, the explicit instruction should be “designed according to a knowledge construction approach,” or students need to recognize, assess, and connect new skills to old ones, “and it needs to take place over an extended period of time” (Zohar & David, p. 187). This kind of explicit instruction will help students expand or replace existing learning strategies with new and more effective ones, give students a way to talk about learning and thinking, compare strategies with their classmates’ and make more informed choices, and render learning “less opaque to students, rather than being something that happens mysteriously or that some students ‘get’ and learn and others struggle and don’t learn” (Pintrich, 2002, p. 223).
What would such a handout look like for your discipline?
Students can even be metacognitively prepared (and then prepare themselves) for the overarching learning experiences expected in specific contexts . Salvatori and Donahue’s The Elements (and Pleasures) of Difficulty (2004) encourages students to embrace difficult texts (and tasks) as part of deep learning, rather than an obstacle. Their “difficulty paper” assignment helps students reflect on and articulate the nature of the difficulty and work through their responses to it (p. 9). Similarly, in courses with sensitive subject matter, a different kind of learning occurs, one that involves complex emotional responses. In “ Learning from Their Own Learning: How Metacognitive and Meta-affective Reflections Enhance Learning in Race-Related Courses ” (Chick, Karis, & Kernahan, 2009), students were informed about the common reactions to learning about racial inequality (Helms, 1995; Adams, Bell, & Griffin, 1997; see student handout, Chick, Karis, & Kernahan, p. 23-24) and then regularly wrote about their cognitive and affective responses to specific racialized situations. The students with the most developed metacognitive and meta-affective practices at the end of the semester were able to “clear the obstacles and move away from” oversimplified thinking about race and racism ”to places of greater questioning, acknowledging the complexities of identity, and redefining the world in racial terms” (p. 14).
Ultimately, metacognition requires students to “externalize mental events” (Bransford, Brown, & Cocking, p. 67), such as what it means to learn, awareness of one’s strengths and weaknesses with specific skills or in a given learning context, plan what’s required to accomplish a specific learning goal or activity, identifying and correcting errors, and preparing ahead for learning processes.
————————
1 Students who were tested with short answer in addition to multiple-choice questions on their exams reported more cognitively active behaviors than those tested with just multiple-choice questions, and these active behaviors led to improved performance on the final exam.
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Metacognitive strategies like self-reflection empower students for a lifetime..
Posted October 9, 2020 | Reviewed by Abigail Fagan
Metacognition is a high order thinking skill that is emerging from the shadows of academia to take its rightful place in classrooms around the world. As online classrooms extend into homes, this is an important time for parents and teachers to understand metacognition and how metacognitive strategies affect learning. These skills enable children to become better thinkers and decision-makers.
Metacognition: The Neglected Skill Set for Empowering Students is a new research-based book by educational consultants Dr. Robin Fogarty and Brian Pete that not only gets to the heart of why metacognition is important but gives teachers and parents insightful strategies for teaching metacognition to children from kindergarten through high school. This article summarizes several concepts from their book and shares three of their thirty strategies to strengthen metacognition.
Metacognition is the practice of being aware of one’s own thinking. Some scholars refer to it as “thinking about thinking.” Fogarty and Pete give a great everyday example of metacognition:
Think about the last time you reached the bottom of a page and thought to yourself, “I’m not sure what I just read.” Your brain just became aware of something you did not know, so instinctively you might reread the last sentence or rescan the paragraphs of the page. Maybe you will read the page again. In whatever ways you decide to capture the missing information, this momentary awareness of knowing what you know or do not know is called metacognition.
When we notice ourselves having an inner dialogue about our thinking and it prompts us to evaluate our learning or problem-solving processes, we are experiencing metacognition at work. This skill helps us think better, make sound decisions, and solve problems more effectively. In fact, research suggests that as a young person’s metacognitive abilities increase, they achieve at higher levels.
Fogarty and Pete outline three aspects of metacognition that are vital for children to learn: planning, monitoring, and evaluation. They convincingly argue that metacognition is best when it is infused in teaching strategies rather than taught directly. The key is to encourage students to explore and question their own metacognitive strategies in ways that become spontaneous and seemingly unconscious .
Metacognitive skills provide a basis for broader, psychological self-awareness , including how children gain a deeper understanding of themselves and the world around them.
Fogarty and Pete successfully demystify metacognition and provide simple ways teachers and parents can strengthen children’s abilities to use these higher-order thinking skills. Below is a summary of metacognitive strategies from the three areas of planning, monitoring, and evaluation.
As students learn to plan, they learn to anticipate the strengths and weaknesses of their ideas. Planning strategies used to strengthen metacognition help students scrutinize plans at a time when they can most easily be changed.
One of ten metacognitive strategies outlined in the book is called “Inking Your Thinking.” It is a simple writing log that requires students to reflect on a lesson they are about to begin. Sample starters may include: “I predict…” “A question I have is…” or “A picture I have of this is…”
Writing logs are also helpful in the middle or end of assignments. For example, “The homework problem that puzzles me is…” “The way I will solve this problem is to…” or “I’m choosing this strategy because…”
Monitoring strategies used to strengthen metacognition help students check their progress and review their thinking at various stages. Different from scrutinizing, this strategy is reflective in nature. It also allows for adjustments while the plan, activity, or assignment is in motion. Monitoring strategies encourage recovery of learning, as in the example cited above when we are reading a book and notice that we forgot what we just read. We can recover our memory by scanning or re-reading.
One of many metacognitive strategies shared by Fogarty and Pete, called the “Alarm Clock,” is used to recover or rethink an idea once the student realizes something is amiss. The idea is to develop internal signals that sound an alarm. This signal prompts the student to recover a thought, rework a math problem, or capture an idea in a chart or picture. Metacognitive reflection involves thinking about “What I did,” then reviewing the pluses and minuses of one’s action. Finally, it means asking, “What other thoughts do I have” moving forward?
Teachers can easily build monitoring strategies into student assignments. Parents can reinforce these strategies too. Remember, the idea is not to tell children what they did correctly or incorrectly. Rather, help children monitor and think about their own learning. These are formative skills that last a lifetime.
According to Fogarty and Pete, the evaluation strategies of metacognition “are much like the mirror in a powder compact. Both serve to magnify the image, allow for careful scrutiny, and provide an up-close and personal view. When one opens the compact and looks in the mirror, only a small portion of the face is reflected back, but that particular part is magnified so that every nuance, every flaw, and every bump is blatantly in view.” Having this enlarged view makes inspection much easier.
When students inspect parts of their work, they learn about the nuances of their thinking processes. They learn to refine their work. They grow in their ability to apply their learning to new situations. “Connecting Elephants” is one of many metacognitive strategies to help students self-evaluate and apply their learning.
In this exercise, the metaphor of three imaginary elephants is used. The elephants are walking together in a circle, connected by the trunk and tail of another elephant. The three elephants represent three vital questions: 1) What is the big idea? 2) How does this connect to other big ideas? 3) How can I use this big idea? Using the image of a “big idea” helps students magnify and synthesize their learning. It encourages them to think about big ways their learning can be applied to new situations.
Reflective thinking is at the heart of metacognition. In today’s world of constant chatter, technology and reflective thinking can be at odds. In fact, mobile devices can prevent young people from seeing what is right before their eyes.
John Dewey, a renowned psychologist and education reformer, claimed that experiences alone were not enough. What is critical is an ability to perceive and then weave meaning from the threads of our experiences.
The function of metacognition and self-reflection is to make meaning. The creation of meaning is at the heart of what it means to be human.
Everyone can help foster self-reflection in young people.
Marilyn Price-Mitchell, Ph.D., is an Institute for Social Innovation Fellow at Fielding Graduate University and author of Tomorrow’s Change Makers.
Sticking up for yourself is no easy task. But there are concrete skills you can use to hone your assertiveness and advocate for yourself.
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Metacognition comprises both the ability to be aware of one’s cognitive processes (metacognitive knowledge) and to regulate them (metacognitive control). Research in educational sciences has amassed a large body of evidence on the importance of metacognition in learning and academic achievement. More recently, metacognition has been studied from experimental and cognitive neuroscience perspectives. This research has started to identify brain regions that encode metacognitive processes. However, the educational and neuroscience disciplines have largely developed separately with little exchange and communication. In this article, we review the literature on metacognition in educational and cognitive neuroscience and identify entry points for synthesis. We argue that to improve our understanding of metacognition, future research needs to (i) investigate the degree to which different protocols relate to the similar or different metacognitive constructs and processes, (ii) implement experiments to identify neural substrates necessary for metacognition based on protocols used in educational sciences, (iii) study the effects of training metacognitive knowledge in the brain, and (iv) perform developmental research in the metacognitive brain and compare it with the existing developmental literature from educational sciences regarding the domain-generality of metacognition.
Introduction.
Metacognition is defined as “thinking about thinking” or the ability to monitor and control one’s cognitive processes 1 and plays an important role in learning and education 2 , 3 , 4 . For instance, high performers tend to present better metacognitive abilities (especially control) than low performers in diverse educational activities 5 , 6 , 7 , 8 , 9 . Recently, there has been a lot of progress in studying the neural mechanisms of metacognition 10 , 11 , yet it is unclear at this point how these results may inform educational sciences or interventions. Given the potential benefits of metacognition, it is important to get a better understanding of how metacognition works and of how training can be useful.
The interest in bridging cognitive neuroscience and educational practices has increased in the past two decades, spanning a large number of studies grouped under the umbrella term of educational neuroscience 12 , 13 , 14 . With it, researchers have brought forward issues that are viewed as critical for the discipline to improve education. Recurring issues that may impede the relevance of neural insights for educational practices concern external validity 15 , 16 , theoretical discrepancies 17 and differences in terms of the domains of (meta)cognition operationalised (specific or general) 15 . This is important because, in recent years, brain research is starting to orient itself towards training metacognitive abilities that would translate into real-life benefits. However, direct links between metacognition in the brain and metacognition in domains such as education have still to be made. As for educational sciences, a large body of literature on metacognitive training is available, yet we still need clear insights about what works and why. While studies suggest that training metacognitive abilities results in higher academic achievement 18 , other interventions show mixed results 19 , 20 . Moreover, little is known about the long-term effects of, or transfer effects, of these interventions. A better understanding of the cognitive processes involved in metacognition and how they are expressed in the brain may provide insights in these regards.
Within cognitive neuroscience, there has been a long tradition of studying executive functions (EF), which are closely related to metacognitive processes 21 . Similar to metacognition, EF shows a positive relationship with learning at school. For instance, performance in laboratory tasks involving error monitoring, inhibition and working memory (i.e. processes that monitor and regulate cognition) are associated with academic achievement in pre-school children 22 . More recently, researchers have studied metacognition in terms of introspective judgements about performance in a task 10 . Although the neural correlates of such behaviour are being revealed 10 , 11 , little is known about how behaviour during such tasks relates to academic achievement.
Educational and cognitive neuroscientists study metacognition in different contexts using different methods. Indeed, while the latter investigate metacognition via behavioural task, the former mainly rely on introspective questionnaires. The extent to which these different operationalisations of metacognition match and reflect the same processes is unclear. As a result, the external validity of methodologies used in cognitive neuroscience is also unclear 16 . We argue that neurocognitive research on metacognition has a lot of potential to provide insights in mechanisms relevant in educational contexts, and that theoretical and methodological exchange between the two disciplines can benefit neuroscientific research in terms of ecological validity.
For these reasons, we investigate the literature through the lenses of external validity, theoretical discrepancies, domain generality and metacognitive training. Research on metacognition in cognitive neuroscience and educational sciences are reviewed separately. First, we investigate how metacognition is operationalised with respect to the common framework introduced by Nelson and Narens 23 (see Fig. 1 ). We then discuss the existing body of evidence regarding metacognitive training. Finally, we compare findings in both fields, highlight gaps and shortcomings, and propose avenues for research relying on crossovers of the two disciplines.
Meta-knowledge is characterised as the upward flow from object-level to meta-level. Meta-control is characterised as the downward flow from meta-level to object-level. Metacognition is therefore conceptualised as the bottom-up monitoring and top-down control of object-level processes. Adapted from Nelson and Narens’ cognitive psychology model of metacognition 23 .
In cognitive neuroscience, metacognition is divided into two main components 5 , 24 , which originate from the seminal works of Flavell on metamemory 25 , 26 . First, metacognitive knowledge (henceforth, meta-knowledge) is defined as the knowledge individuals have of their own cognitive processes and their ability to monitor and reflect on them. Second, metacognitive control (henceforth, meta-control) consists of someone’s self-regulatory mechanisms, such as planning and adapting behaviour based on outcomes 5 , 27 . Following Nelson and Narens’ definition 23 , meta-knowledge is characterised as the flow and processing of information from the object level to the meta-level, and meta-control as the flow from the meta-level to the object level 28 , 29 , 30 (Fig. 1 ). The object-level encompasses cognitive functions such as recognition and discrimination of objects, decision-making, semantic encoding, and spatial representation. On the meta-level, information originating from the object level is processed and top-down regulation on object-level functions is imposed 28 , 29 , 30 .
Educational researchers have mainly investigated metacognition through the lens of Self-Regulated Learning theory (SRL) 3 , 4 , which shares common conceptual roots with the theoretical framework used in cognitive neuroscience but varies from it in several ways 31 . First, SRL is constrained to learning activities, usually within educational settings. Second, metacognition is merely one of three components, with “motivation to learn” and “behavioural processes”, that enable individuals to learn in a self-directed manner 3 . In SRL, metacognition is defined as setting goals, planning, organising, self-monitoring and self-evaluating “at various points during the acquisition” 3 . The distinction between meta-knowledge and meta-control is not formally laid down although reference is often made to a “self-oriented feedback loop” describing the relationship between reflecting and regulating processes that resembles Nelson and Narens’ model (Fig. 1 ) 3 , 23 . In order to facilitate the comparison of operational definitions, we will refer to meta-knowledge in educational sciences when protocols operationalise self-awareness and knowledge of strategies, and to meta-control when they operationalise the selection and use of learning strategies and planning. For an in-depth discussion on metacognition and SRL, we refer to Dinsmore et al. 31 .
Operational definitions.
In cognitive neuroscience, research in metacognition is split into two tracks 32 . One track mainly studies meta-knowledge by investigating the neural basis of introspective judgements about one’s own cognition (i.e., metacognitive judgements), and meta-control with experiments involving cognitive offloading. In these experiments, subjects can perform actions such as set reminders, making notes and delegating tasks 33 , 34 , or report their desire for them 35 . Some research has investigated how metacognitive judgements can influence subsequent cognitive behaviour (i.e., a downward stream from the meta-level to the object level), but only one study so far has explored how this relationship is mapped in the brain 35 . In the other track, researchers investigate EF, also referred to as cognitive control 30 , 36 , which is closely related to metacognition. Note however that EF are often not framed in metacognitive terms in the literature 37 (but see ref. 30 ). For the sake of concision, we limit our review to operational definitions that have been used in neuroscientific studies.
Cognitive neuroscientists have been using paradigms in which subjects make judgements on how confident they are with regards to their learning of some given material 10 . These judgements are commonly referred to as metacognitive judgements , which can be viewed as a form of meta-knowledge (for reviews see Schwartz 38 and Nelson 39 ). Historically, researchers mostly resorted to paradigms known as Feelings of Knowing (FOK) 40 and Judgements of Learning (JOL) 41 . FOK reflect the belief of a subject to knowing the answer to a question or a problem and being able to recognise it from a list of alternatives, despite being unable to explicitly recall it 40 . Here, metacognitive judgement is thus made after retrieval attempt. In contrast, JOL are prospective judgements during learning of one’s ability to successfully recall an item on subsequent testing 41 .
More recently, cognitive neuroscientists have used paradigms in which subjects make retrospective metacognitive judgements on their performance in a two-alternative Forced Choice task (2-AFC) 42 . In 2-AFCs, subjects are asked to choose which of two presented options has the highest criterion value. Different domains can be involved, such as perception (e.g., visual or auditory) and memory. For example, subjects may be instructed to visually discriminate which one of two boxes contains more dots 43 , identify higher contrast Gabor patches 44 , or recognise novel words from words that were previously learned 45 (Fig. 2 ). The subjects engage in metacognitive judgements by rating how confident they are relative to their decision in the task. Based on their responses, one can evaluate a subject’s metacognitive sensitivity (the ability to discriminate one’s own correct and incorrect judgements), metacognitive bias (the overall level of confidence during a task), and metacognitive efficiency (the level of metacognitive sensitivity when controlling for task performance 46 ; Fig. 3 ). Note that sensitivity and bias are independent aspects of metacognition, meaning that two subjects may display the same levels of metacognitive sensitivity, but one may be biased towards high confidence while the other is biased towards low confidence. Because metacognitive sensitivity is affected by the difficulty of the task (one subject tends to display greater metacognitive sensitivity in easy tasks than difficult ones and different subjects may find a task more or less easy), metacognitive efficiency is an important measure as it allows researchers to compare metacognitive abilities between subjects and between domains. The most commonly used methods to assess metacognitive sensitivity during retrospective judgements are the receiver operating curve (ROC) and meta- d ′. 46 Both derive from signal detection theory (SDT) 47 which allows Type 1 sensitivity, or d’ ′ (how a subject can discriminate between stimulus alternatives, i.e. object-level processes) to be differentiated from metacognitive sensitivity (a judgement on the correctness of this decision) 48 . Importantly, only comparing meta- d ′ to d ′ seems to give reliable assessments metacognitive efficiency 49 . A ratio of 1 between meta- d’ ′ and d’ ′, indicates that a subject was perfectly able to discriminate between their correct and incorrect judgements. A ratio of 0.8 suggests that 80% of the task-related sensory evidence was available for the metacognitive judgements. Table 1 provides an overview of the different types of tasks and protocols with regards to the type of metacognitive process they operationalise. These operationalisations of meta-knowledge are used in combination with brain imaging methods (functional and structural magnetic resonance imaging; fMRI; MRI) to identify brain regions associated with metacognitive activity and metacognitive abilities 10 , 50 . Alternatively, transcranial magnetic stimulation (TMS) can be used to temporarily deactivate chosen brain regions and test whether this affects metacognitive abilities in given tasks 51 , 52 .
a Visual perception task: subjects choose the box containing the most (randomly generated) dots. Subjects then rate their confidence in their decision. b Memory task: subjects learn a list of words. In the next screen, they have to identify which of two words shown was present on the list. The subjects then rate their confidence in their decision.
The red and blue curves represent the distribution of confidence ratings for incorrect and correct trials, respectively. A larger distance between the two curves denotes higher sensitivity. Displacement to the left and right denote biases towards low confidence (low metacognitive bias) and high confidence (high metacognitive bias), respectively (retrieved from Fig. 1 in Fleming and Lau 46 ). We repeat the disclaimer of the original authors that this figure is not a statistically accurate description of correct and incorrect responses, which are typically not normally distributed 46 , 47 .
A recent meta-analysis analysed 47 neuroimaging studies on metacognition and identified a domain-general network associated with high vs. low confidence ratings in both decision-making tasks (perception 2-AFC) and memory tasks (JOL, FOK) 11 . This network includes the medial and lateral prefrontal cortex (mPFC and lPFC, respectively), precuneus and insula. In contrast, the right anterior dorsolateral PFC (dlPFC) was specifically involved in decision-making tasks, and the bilateral parahippocampal cortex was specific to memory tasks. In addition, prospective judgements were associated with the posterior mPFC, left dlPFC and right insula, whereas retrospective judgements were associated with bilateral parahippocampal cortex and left inferior frontal gyrus. Finally, emerging evidence suggests a role of the right rostrolateral PFC (rlPFC) 53 , 54 , anterior PFC (aPFC) 44 , 45 , 55 , 56 , dorsal anterior cingulate cortex (dACC) 54 , 55 and precuneus 45 , 55 in metacognitive sensitivity (meta- d ′, ROC). In addition, several studies suggest that the aPFC relates to metacognition specifically in perception-related 2-AFC tasks, whereas the precuneus is engaged specifically in memory-related 2-AFC tasks 45 , 55 , 56 . This may suggest that metacognitive processes engage some regions in a domain-specific manner, while other regions are domain-general. For educational scientists, this could mean that some domains of metacognition may be more relevant for learning and, granted sufficient plasticity of the associated brain regions, that targeting them during interventions may show more substantial benefits. Note that rating one’s confidence and metacognitive sensitivity likely involve additional, peripheral cognitive processes instead of purely metacognitive ones. These regions are therefore associated with metacognition but not uniquely per se. Notably, a recent meta-analysis 50 suggests that domain-specific and domain-general signals may rather share common circuitry, but that their neural signature varies depending on the type of task or activity, showing that domain-generality in metacognition is complex and still needs to be better understood.
In terms of the role of metacognitive judgements on future behaviour, one study found that brain patterns associated with the desire for cognitive offloading (i.e., meta-control) partially overlap with those associated with meta-knowledge (metacognitive judgements of confidence), suggesting that meta-control is driven by either non-metacognitive, in addition to metacognitive, processes or by a combination of different domain-specific meta-knowledge processes 35 .
In EF, processes such as error detection/monitoring and effort monitoring can be related to meta-knowledge while error correction, inhibitory control, and resource allocation can be related to meta-control 36 . To activate these processes, participants are asked to perform tasks in laboratory settings such as Flanker tasks, Stroop tasks, Demand Selection tasks and Motion Discrimination tasks (Fig. 4 ). Neural correlates of EF are investigated by having subjects perform such tasks while their brain activity is recorded with fMRI or electroencephalography (EEG). Additionally, patients with brain lesions can be tested against healthy participants to evaluate the functional role of the impaired regions 57 .
a Flanker task: subjects indicate the direction to which the arrow in the middle points. b Stroop task: subjects are presented with the name of colour printed in a colour that either matches or mismatches the name. Subjects are asked to give the name of the written colour or the printed colour. c Motion Discrimination task: subjects have to determine in which direction the dots are going with variating levels of noise. d Example of a Demand Selection task: in both options subjects have to switch between two tasks. Task one, subjects determine whether the number shown is higher or lower than 5. Task two, subjects determine whether the number is odd or even. The two options (low and high demand) differ in their degree of task switching, meaning the effort required. Subjects are allowed to switch between the two options. Note, the type of task is solely indicated by the colour of the number and that the subjects are not explicitly told about the difference in effort between the two options (retrieved from Fig. 1c in Froböse et al. 58 ).
In a review article on the neural basis of EF (in which they are defined as meta-control), Shimamura argues that a network of regions composed of the aPFC, ACC, ventrolateral PFC (vlPFC) and dlPFC is involved in the regulations of cognition 30 . These regions are not only interconnected but are also intricately connected to cortical and subcortical regions outside of the PFC. The vlPFC was shown to play an important role in “selecting and maintaining information in working memory”, whereas the dlPFC is involved in “manipulating and updating information in working memory” 30 . The ACC has been proposed to monitor cognitive conflict (e.g. in a Stroop task or a Flanker task), and the dlPFC to regulate it 58 , 59 . In particular, activity in the ACC in conflict monitoring (meta-knowledge) seems to contribute to control of cognition (meta-control) in the dlPFC 60 , 61 and to “bias behavioural decision-making toward cognitively efficient tasks and strategies” (p. 356) 62 . In a recent fMRI study, subjects performed a motion discrimination task (Fig. 4c ) 63 . After deciding on the direction of the motion, they were presented additional motion (i.e. post-decisional evidence) and then were asked to rate their confidence in their initial choice. The post-decisional evidence was encoded in the activity of the posterior medial frontal cortex (pMFC; meta-knowledge), while lateral aPFC (meta-control) modulated the impact of this evidence on subsequent confidence rating 63 . Finally, results from a meta-analysis study on cognitive control identified functional connectivity between the pMFC, associated with monitoring and informing other regions about the need for regulation, and the lPFC that would effectively regulate cognition 64 .
While the processes engaged during tasks such as those used in EF research can be considered as metacognitive in the sense that they are higher-order functions that monitor and control lower cognitive processes, scientists have argued that they are not functionally equivalent to metacognitive judgements 10 , 11 , 65 , 66 . Indeed, engaging in metacognitive judgements requires subjects to reflect on past or future activities. As such, metacognitive judgements can be considered as offline metacognitive processes. In contrast, high-order processes involved in decision-making tasks such as used in EF research are arguably largely made on the fly, or online , at a rapid pace and subjects do not need to reflect on their actions to perform them. Hence, we propose to explicitly distinguish online and offline processes. Other researchers have shared a similar view and some have proposed models for metacognition that make similar distinctions 65 , 66 , 67 , 68 . The functional difference between online and offline metacognition is supported by some evidence. For instance, event-related brain potential (ERP) studies suggest that error negativities are associated with error detection in general, whereas an increased error positivity specifically encodes error that subjects could report upon 69 , 70 . Furthermore, brain-imaging studies suggest that the MFC and ACC are involved in online meta-knowledge, while the aPFC and lPFC seem to be activated when subjects engage in more offline meta-knowledge and meta-control, respectively 63 , 71 , 72 . An overview of the different tasks can be found in Table 1 and a list of different studies on metacognition can be found in Supplementary Table 1 (organised in terms of the type of processes investigated, the protocols and brain measures used, along with the brain regions identified). Figure 5 illustrates the different brain regions associated with meta-knowledge and meta-control, distinguishing between what we consider to be online and offline processes. This distinction is often not made explicitly but it will be specifically helpful when building bridges between cognitive neuroscience and educational sciences.
The regions are divided into online meta-knowledge and meta-control, and offline meta-knowledge and meta-control following the distinctions introduced earlier. Some regions have been reported to be related to both offline and online processes and are therefore given a striped pattern.
There are extensive accounts in the literature of efforts to improve EF components such as inhibitory control, attention shifting and working memory 22 . While working memory does not directly reflect metacognitive abilities, its training is often hypothesised to improve general cognitive abilities and academic achievement. However, most meta-analyses found that training methods lead only to weak, non-lasting effects on cognitive control 73 , 74 , 75 . One meta-analysis did find evidence of near-transfer following EF training in children (in particular working memory, inhibitory control and cognitive flexibility), but found no evidence of far-transfer 20 . According to this study, training on one component leads to improved abilities in that same component but not in other EF components. Regarding adults, however, one meta-analysis suggests that EF training in general and working memory training specifically may both lead to significant near- and far-transfer effects 76 . On a neural level, a meta-analysis showed that cognitive training resulted in decreased brain activity in brain regions associated with EF 77 . According to the authors, this indicates that “training interventions reduce demands on externally focused attention” (p. 193) 77 .
With regards to meta-knowledge, several studies have reported increased task-related metacognitive abilities after training. For example, researchers found that subjects who received feedback on their metacognitive judgements regarding a perceptual decision-making task displayed better metacognitive accuracy, not only in the trained task but also in an untrained memory task 78 . Related, Baird and colleagues 79 found that a two-week mindfulness meditation training lead to enhanced meta-knowledge in the memory domain, but not the perceptual domain. The authors link these results to evidence of increased grey matter density in the aPFC in meditation practitioners.
Research on metacognition in cognitive science has mainly been studied through the lens of metacognitive judgements and EF (specifically performance monitoring and cognitive control). Meta-knowledge is commonly activated in subjects by asking them to rate their confidence in having successfully performed a task. A distinction is made between metacognitive sensitivity, metacognitive bias and metacognitive efficacy. Monitoring and regulating processes in EF are mainly operationalised with behavioural tasks such as Flanker tasks, Stroop tasks, Motion Discrimination tasks and Demand Selection tasks. In addition, metacognitive judgements can be viewed as offline processes in that they require the subject to reflect on her cognition and develop meta-representations. In contrast, EF can be considered as mostly online metacognitive processes because monitoring and regulation mostly happen rapidly without the need for reflective thinking.
Although there is some evidence for domain specificity, other studies have suggested that there is a single network of regions involved in all meta-cognitive tasks, but differentially activated in different task contexts. Comparing research on meta-knowledge and meta-control also suggest that some regions play a crucial role in both knowledge and regulation (Fig. 5 ). We have also identified a specific set of regions that are involved in either offline or online meta-knowledge. The evidence in favour of metacognitive training, while mixed, is interesting. In particular, research on offline meta-knowledge training involving self-reflection and metacognitive accuracy has shown some promising results. The regions that show structural changes after training, were those that we earlier identified as being part of the metacognition network. EF training does seem to show far-transfer effects at least in adults, but the relevance for everyday life activity is still unclear.
One major limitation of current research in metacognition is ecological validity. It is unclear to what extent the operationalisations reviewed above reflect real-life metacognition. For instance, are people who can accurately judge their performance on a behavioural task also able to accurately assess how they performed during an exam? Are people with high levels of error regulation and inhibitory control able to learn more efficiently? Note that criticism on the ecological validity of neurocognitive operationalisations extends beyond metacognition research 16 . A solution for improving validity may be to compare operationalisations of metacognition in cognitive neuroscience with the ones in educational sciences, which have shown clear links with learning in formal education. This also applies to metacognitive training.
The most popular protocols used to measure metacognition in educational sciences are self-report questionnaires or interviews, learning journals and thinking-aloud protocols 31 , 80 . During interviews, subjects are asked to answer questions regarding hypothetical situations 81 . In learning journals, students write about their learning experience and their thoughts on learning 82 , 83 . In thinking-aloud protocols, subjects are asked to verbalise their thoughts while performing a problem-solving task 80 . Each of these instruments can be used to study meta-knowledge and meta-control. For instance, one of the most widely used questionnaires, the Metacognitive Awareness Inventory (MAI) 42 , operationalises “Flavellian” metacognition and has dedicated scales for meta-knowledge and meta-control (also popular are the MSLQ 84 and LASSI 85 which operate under SRL). The meta-knowledge scale of the MAI operationalises knowledge of strategies (e.g., “ I am aware of what strategies I use when I study ”) and self-awareness (e.g., “ I am a good judge of how well I understand something ”); the meta-control scale operationalises planning (e.g., “ I set a goal before I begin a task ”) and use of learning strategies (e.g., “ I summarize what I’ve learned after I finish ”). Learning journals, self-report questionnaires and interviews involve offline metacognition. Thinking aloud, though not engaging the same degree self-reflection, also involves offline metacognition in the sense that online processes are verbalised, which necessitate offline processing (see Table 1 for an overview and Supplementary Table 2 for more details).
More recently, methodologies borrowed from cognitive neuroscience have been introduced to study EF in educational settings 22 , 86 . In particular, researchers used classic cognitive control tasks such as the Stroop task (for a meta-analysis 86 ). Most of the studied components are related to meta-control and not meta-knowledge. For instance, the BRIEF 87 is a questionnaire completed by parents and teachers which assesses different subdomains of EF: (1) inhibition, shifting, and emotional control which can be viewed as online metacognitive control, and (2) planning, organisation of materials, and monitoring, which can be viewed as offline meta-control 87 .
Assessment of metacognition is usually compared against metrics of academic performance such as grades or scores on designated tasks. A recent meta-analysis reported a weak correlation of self-report questionnaires and interviews with academic performance whereas think-aloud protocols correlated highly 88 . Offline meta-knowledge processes operationalised by learning journals were found to be positively associated with academic achievement when related to reflection on learning activities but negatively associated when related to reflection on learning materials, indicating that the type of reflection is important 89 . EF have been associated with abilities in mathematics (mainly) and reading comprehension 86 . However, the literature points towards contrary directions as to what specific EF component is involved in academic achievement. This may be due to the different groups that were studied, to different operationalisations or to different theoretical underpinnings for EF 86 . For instance, online and offline metacognitive processes, which are not systematically distinguished in the literature, may play different roles in academic achievement. Moreover, the bulk of research focussed on young children with few studies on adolescents 86 and EF may play a role at varying extents at different stages of life.
A critical question in educational sciences is that of the nature of the relationship between metacognition and academic achievement to understand whether learning at school can be enhanced by training metacognitive abilities. Does higher metacognition lead to higher academic achievement? Do these features evolve in parallel? Developmental research provides valuable insights into the formation of metacognitive abilities that can inform training designs in terms of what aspect of metacognition should be supported and the age at which interventions may yield the best results. First, meta-knowledge seems to emerge around the age of 5, meta-control around 8, and both develop over the years 90 , with evidence for the development of meta-knowledge into adolescence 91 . Furthermore, current theories propose that meta-knowledge abilities are initially highly domain-dependent and gradually become more domain-independent as knowledge and experience are acquired and linked between domains 32 . Meta-control is believed to evolve in a similar fashion 90 , 92 .
Common methods used to train offline metacognition are direct instruction of metacognition, metacognitive prompts and learning journals. In addition, research has been done on the use of (self-directed) feedback as a means to induce self-reflection in students, mainly in computer-supported settings 93 . Interestingly, learning journals appear to be used for both assessing and fostering metacognition. Metacognitive instruction consists of teaching learners’ strategies to “activate” their metacognition. Metacognitive prompts most often consist of text pieces that are sent at specific times and that trigger reflection (offline meta-knowledge) on learning behaviour in the form of a question, hint or reminder.
Meta-analyses have investigated the effects of direct metacognitive instruction on students’ use of learning strategies and academic outcomes 18 , 94 , 95 . Their findings show that metacognitive instruction can have a positive effect on learning abilities and achievement within a population ranging from primary schoolers to university students. In particular, interventions lead to the highest effect sizes when they both (i) instructed a combination of metacognitive strategies with an emphasis on planning strategies (offline meta-control) and (ii) “provided students with knowledge about strategies” (offline meta-knowledge) and “illustrated the benefits of applying the trained strategies, or even stimulated metacognitive reasoning” (p.114) 18 . The longer the duration of the intervention, the more effective they were. The strongest effects on academic performance were observed in the context of mathematics, followed by reading and writing.
While metacognitive prompts and learning journals make up the larger part of the literature on metacognitive training 96 , meta-analyses that specifically investigate their effectiveness have yet to be performed. Nonetheless, evidence suggests that such interventions can be successful. Researchers found that metacognitive prompts fostered the use of metacognitive strategies (offline meta-control) and that the combination of cognitive and metacognitive prompts improved learning outcomes 97 . Another experiment showed that students who received metacognitive prompts performed more metacognitive activities inside the learning environment and displayed better transfer performance immediately after the intervention 98 . A similar study using self-directed prompts showed enhanced transfer performance that was still observable 3 weeks after the intervention 99 .
Several studies suggest that learning journals can positively enhance metacognition. Subjects who kept a learning journal displayed stronger high meta-control and meta-knowledge on learning tasks and tended to reach higher academic outcomes 100 , 101 , 102 . However, how the learning journal is used seems to be critical; good instructions are crucial 97 , 103 , and subjects who simply summarise their learning activity benefit less from the intervention than subjects who reflect about their knowledge, learning and learning goals 104 . An overview of studies using learning journals and metacognitive prompts to train metacognition can be found in Supplementary Table 3 .
In recent years, educational neuroscience researchers have tried to determine whether training and improvements in EF can lead to learning facilitation and higher academic achievement. Training may consist of having students continually perform behavioural tasks either in the lab, at home, or at school. Current evidence in favour of training EF is mixed, with only anecdotal evidence for positive effects 105 . A meta-analysis did not show evidence for a causal relationship between EF and academic achievement 19 , but suggested that the relationship is bidirectional, meaning that the two are “mutually supportive” 106 .
A recent review article has identified several gaps and shortcoming in the literature on metacognitive training 96 . Overall, research in metacognitive training has been mainly invested in developing learners’ meta-control rather than meta-knowledge. Furthermore, most of the interventions were done in the context of science learning. Critically, there appears to be a lack of studies that employed randomised control designs, such that the effects of metacognitive training intervention are often difficult to evaluate. In addition, research overwhelmingly investigated metacognitive prompts and learning journals in adults 96 , while interventions on EF mainly focused on young children 22 . Lastly, meta-analyses evaluating the effectiveness of metacognitive training have so far focused on metacognitive instruction on children. There is thus a clear disbalance between the meta-analyses performed and the scope of the literature available.
An important caveat of educational sciences research is that metacognition is not typically framed in terms of online and offline metacognition. Therefore, it can be unclear whether protocols operationalise online or offline processes and whether interventions tend to benefit more online or offline metacognition. There is also confusion in terms of what processes qualify as EF and definitions of it vary substantially 86 . For instance, Clements and colleagues mention work on SRL to illustrate research in EF in relation to academic achievement but the two spawn from different lines of research, one rooted in metacognition and socio-cognitive theory 31 and the other in the cognitive (neuro)science of decision-making. In addition, the MSLQ, as discussed above, assesses offline metacognition along with other components relevant to SRL, whereas EF can be mainly understood as online metacognition (see Table 1 ), which on the neural level may rely on different circuitry.
Investigating offline metacognition tends to be carried out in school settings whereas evaluating EF (e.g., Stroop task, and BRIEF) is performed in the lab. Common to all protocols for offline metacognition is that they consist of a form of self-report from the learner, either during the learning activity (thinking-aloud protocols) or after the learning activity (questionnaires, interviews and learning journals). Questionnaires are popular protocols due to how easy they are to administer but have been criticised to provide biased evaluations of metacognitive abilities. In contrast, learning journals evaluate the degree to which learners engage in reflective thinking and may therefore be less prone to bias. Lastly, it is unclear to what extent thinking-aloud protocols are sensitive to online metacognitive processes, such as on-the-fly error correction and effort regulation. The strength of the relationship between metacognitive abilities and academic achievement varies depending on how metacognition is operationalised. Self-report questionnaires and interviews are weakly related to achievement whereas thinking-aloud protocols and EF are strongly related to it.
Based on the well-documented relationship between metacognition and academic achievement, educational scientists hypothesised that fostering metacognition may improve learning and academic achievement, and thus performed metacognitive training interventions. The most prevalent training protocols are direct metacognitive instruction, learning journals, and metacognitive prompts, which aim to induce and foster offline metacognitive processes such as self-reflection, planning and selecting learning strategies. In addition, researchers have investigated whether training EF, either through tasks or embedded in the curriculum, results in higher academic proficiency and achievement. While a large body of evidence suggests that metacognitive instruction, learning journals and metacognitive prompts can successfully improve academic achievement, interventions designed around EF training show mixed results. Future research investigating EF training in different age categories may clarify this situation. These various degrees of success of interventions may indicate that offline metacognition is more easily trainable than online metacognition and plays a more important role in educational settings. Investigating the effects of different methods, offline and online, on the neural level, may provide researchers with insights into the trainability of different metacognitive processes.
In this article, we reviewed the literature on metacognition in educational sciences and cognitive neuroscience with the aim to investigate gaps in current research and propose ways to address them through the exchange of insights between the two disciplines and interdisciplinary approaches. The main aspects analysed were operational definitions of metacognition and metacognitive training, through the lens of metacognitive knowledge and metacognitive control. Our review also highlighted an additional construct in the form of the distinction between online metacognition (on the fly and largely automatic) and offline metacognition (slower, reflective and requiring meta-representations). In cognitive neuroscience, research has focused on metacognitive judgements (mainly offline) and EF (mainly online). Metacognition is operationalised with tasks carried out in the lab and are mapped onto brain functions. In contrast, research in educational sciences typically measures metacognition in the context of learning activities, mostly in schools and universities. More recently, EF has been studied in educational settings to investigate its role in academic achievement and whether training it may benefit learning. Evidence on the latter is however mixed. Regarding metacognitive training in general, evidence from both disciplines suggests that interventions fostering learners’ self-reflection and knowledge of their learning behaviour (i.e., offline meta-knowledge) may best benefit them and increase academic achievement.
We focused on four aspects of research that could benefit from an interdisciplinary approach between the two areas: (i) validity and reliability of research protocols, (ii) under-researched dimensions of metacognition, (iii) metacognitive training, and (iv) domain-specificity vs. domain generality of metacognitive abilities. To tackle these issue, we propose four avenues for integrated research: (i) investigate the degree to which different protocols relate to similar or different metacognitive constructs, (ii) implement designs and perform experiments to identify neural substrates necessary for offline meta-control by for example borrowing protocols used in educational sciences, (iii) study the effects of (offline) meta-knowledge training on the brain, and (iv) perform developmental research in the metacognitive brain and compare it with the existing developmental literature in educational sciences regarding the domain-generality of metacognitive processes and metacognitive abilities.
First, neurocognitive research on metacognitive judgements has developed robust operationalisations of offline meta-knowledge. However, these operationalisations often consist of specific tasks (e.g., 2-AFC) carried out in the lab. These tasks are often very narrow and do not resemble the challenges and complexities of behaviours associated with learning in schools and universities. Thus, one may question to what extent they reflect real-life metacognition, and to what extent protocols developed in educational sciences and cognitive neuroscience actually operationalise the same components of metacognition. We propose that comparing different protocols from both disciplines that are, a priori, operationalising the same types of metacognitive processes can help evaluate the ecological validity of protocols used in cognitive neuroscience, and allow for more holistic assessments of metacognition, provided that it is clear which protocol assesses which construct. Degrees of correlation between different protocols, within and between disciplines, may allow researchers to assess to what extent they reflect the same metacognitive constructs and also identify what protocols are most appropriate to study a specific construct. For example, a relation between meta- d ′ metacognitive sensitivity in a 2-AFC task and the meta-knowledge subscale of the MAI, would provide external validity to the former. Moreover, educational scientists would be provided with bias-free tools to assess metacognition. These tools may enable researchers to further investigate to what extent metacognitive bias, sensitivity and efficiency each play a role in education settings. In contrast, a low correlation may highlight a difference in domain between the two measures of metacognition. For instance, metacognitive judgements in brain research are made in isolated behaviour, and meta-d’ can thus be viewed to reflect “local” metacognitive sensitivity. It is also unclear to what extent processes involved in these decision-making tasks cover those taking place in a learning environment. When answering self-reported questionnaires, however, subjects make metacognitive judgements on a large set of (learning) activities, and the measures may thus resemble more “global” or domain-general metacognitive sensitivity. In addition, learners in educational settings tend to receive feedback — immediate or delayed — on their learning activities and performance, which is generally not the case for cognitive neuroscience protocols. Therefore, investigating metacognitive judgements in the presence of performance or social feedback may allow researchers to better understand the metacognitive processes at play in educational settings. Devising a global measure of metacognition in the lab by aggregating subjects’ metacognitive abilities in different domains or investigating to what extent local metacognition may affect global metacognition could improve ecological validity significantly. By investigating the neural correlates of educational measures of metacognition, researchers may be able to better understand to what extent the constructs studied in the two disciplines are related. It is indeed possible that, though weakly correlated, the meta-knowledge scale of the MAI and meta-d’ share a common neural basis.
Second, our review highlights gaps in the literature of both disciplines regarding the research of certain types of metacognitive processes. There is a lack of research in offline meta-control (or strategic regulation of cognition) in neuroscience, whereas this construct is widely studied in educational sciences. More specifically, while there exists research on EF related to planning (e.g. 107 ), common experimental designs make it hard to disentangle online from offline metacognitive processes. A few studies have implemented subject reports (e.g., awareness of error or desire for reminders) to pin-point the neural substrates specifically involved in offline meta-control and the current evidence points at a role of the lPFC. More research implementing similar designs may clarify this construct. Alternatively, researchers may exploit educational sciences protocols, such as self-report questionnaires, learning journals, metacognitive prompts and feedback to investigate offline meta-control processes in the brain and their relation to academic proficiency and achievement.
Third, there is only one study known to us on the training of meta-knowledge in the lab 78 . In contrast, meta-knowledge training in educational sciences have been widely studied, in particular with metacognitive prompts and learning journals, although a systematic review would be needed to identify the benefits for learning. Relative to cognitive neuroscience, studies suggest that offline meta-knowledge trained in and outside the lab (i.e., metacognitive judgements and meditation, respectively) transfer to meta-knowledge in other lab tasks. The case of meditation is particularly interesting since meditation has been demonstrated to beneficiate varied aspects of everyday life 108 . Given its importance for efficient regulation of cognition, training (offline) meta-knowledge may present the largest benefits to academic achievement. Hence, it is important to investigate development in the brain relative to meta-knowledge training. Evidence on metacognitive training in educational sciences tends to suggest that offline metacognition is more “plastic” and may therefore benefit learning more than online metacognition. Furthermore, it is important to have a good understanding of the developmental trajectory of metacognitive abilities — not only on a behavioural level but also on a neural level — to identify critical periods for successful training. Doing so would also allow researchers to investigate the potential differences in terms of plasticity that we mention above. Currently, the developmental trajectory of metacognition is under-studied in cognitive neuroscience with only one study that found an overlap between the neural correlates of metacognition in adults and children 109 . On a side note, future research could explore the potential role of genetic factors in metacognitive abilities to better understand to what extent and under what constraints they can be trained.
Fourth, domain-specific and domain-general aspects of metacognitive processes should be further investigated. Educational scientists have studied the development of metacognition in learners and have concluded that metacognitive abilities are domain-specific at the beginning (meaning that their quality depends on the type of learning activity, like mathematics vs. writing) and progressively evolve towards domain-general abilities as knowledge and expertise increase. Similarly, neurocognitive evidence points towards a common network for (offline) metacognitive knowledge which engages the different regions at varying degrees depending on the domain of the activity (i.e., perception, memory, etc.). Investigating this network from a developmental perspective and comparing findings with the existing behavioural literature may improve our understanding of the metacognitive brain and link the two bodies of evidence. It may also enable researchers to identify stages of life more suitable for certain types of metacognitive intervention.
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We would like to thank the University of Amsterdam for supporting this research through the Interdisciplinary Doctorate Agreement grant. W.v.d.B. is further supported by the Jacobs Foundation, European Research Council (grant no. ERC-2018-StG-803338), the European Union Horizon 2020 research and innovation programme (grant no. DiGYMATEX-870578), and the Netherlands Organization for Scientific Research (grant no. NWO-VIDI 016.Vidi.185.068).
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Simply put, "metacognition" is thinking about our thinking. This ability is essential to critical thinking because of its role in evaluating the success of current approaches and the extent to which they can be improved. In research literature, this process is called "self-regulated learning."
"Critical thinkers are willing to question the justifiability of their own ideas, brave enough to risk being wrong, and wise enough to realize that much can be learned from errors and failed solutions" (p. xiv). Nelson (2005)
Research studies indicate a positive relationship between a student’s metacognition, grit, mindset, and academic success. These traits can all be taught, and through experience, enhanced. Further, these traits all assist students with being successful lifelong learners.
Dr. Peter Arthur, Senior Instructor from University of British Columbia - Okanagan
Describes anything one knows about thinking, especially one's own.
Declarative knowledge - Knowledge about one's self as a learner and what can influence one's performance.
Procedural knowledge - Skills, heuristics, and strategies. Knowledge about how to do things.
Conditional knowledge - Knowledge about when and in what conditions certain knowledge is useful.
The process of managing one's own learning; includes planning, monitoring, and evaluating.
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Critical thinking is one of the imperatives of education, and research shows that openly practicing basic thought routines can make huge differences for learners. Some of these thinking skills are so commonplace, so ingrained in our daily mental processes, that we may not even realize we’re already doing them. Building thinking skills can bring great dividends and lay the foundation for life-long learning.
So how can teachers lay the foundation for critical and higher-order thinking? What are the building blocks of critical thinking?
The term metacognition was first introduced by developmental psychologist Dr. John Flavell in 1976, who recognized that metacognition consists of both self-monitoring and self-regulation of thought processes. In an educational context, metacognition refers to students’ self-understanding and knowledge about themselves as learners. In short, how they think about thinking. Take, for example, practices like using an internal monologue to solve a math problem or using a mnemonic device to help recall specific information. As part of the suite of executive function skills, metacognitive abilities like self-control and self-assessment are strong indicators of learning success and complex thought . Let’s take a deeper look at metacognition and see how it can be applied in the classroom.
Teaching cognitive processes through direct instruction allows students to foster and develop these learning strategies so that they can call on them efficiently and effectively as they learn. Metacognition not only allows students to take more ownership in their learning process by prompting them to evaluate what they are learning and confront challenges, but it also can help students develop more self-awareness as they learn, where they can better understand their own strengths and weaknesses and develop learning strategies for easier problem-solving. Metacognition lets students own their learning by prompting them to evaluate what they are learning and confront challenges. As students develop self-awareness, they form learning strategies, problem-solving techniques, and study habits.
One aspect of metacognition, distinguishing between what we know and what we don’t, can be built by taking quick, informal assessments like mini quizzes. Through timely, effective immediate feedback, students can differentiate their strengths and weaknesses.
The self-monitoring aspect of metacognition, knowing what you know, allows students to take more ownership over their learning journey through self-assessment. Strong learners know when they need more study, how long the study will take, and how much they can trust their recall in the future. They take corrective action like taking notes or looking up words they don’t know.
The self-regulation aspect of metacognition is often an internal list of instructions we give ourselves to work through tasks. At an advanced level, this inner monologue is a curated list of “if-then” conditions. With self-control, students can think through the consequences and implications of their actions by visualizing outcomes and setting up strategies to maneuver around potential obstacles.
It’s important to be transparent about the value of engaging with your own thinking and evaluating ideas when discussing metacognition with students so that they can better understand how such practices will benefit them. When educators verbalize their thinking process, the example leads students along a problem-solving journey. Remember that it’s important to demonstrate mistakes too. Show how to own your mistakes, then go back and fix them.
Ready to get started? Take a look at a few techniques you can try out in the classroom to help your students develop metacognition and other critical-thinking skills:
When shared in groups, metacognitive techniques are empowering. Creating a community of inquiry is fun too! Imagine that your classroom shares an interactive neural “brain” hanging from the ceiling. It’s a great introduction to brain science and active thinking! Diane Dahl, a teacher in McKinney, Texas, created a mind-map class activity for her elementary students using pipe cleaners, a hole punch, and notecards. Students built a physical model of their shared, connective knowledge like a mind map. Every time that students made a connection, like linking the Mississippi River to the Nile River when studying different units, they created a physical representation of the information.
Think-Pair-Share Another great way to practice cognitive skills is with “ think-pair-share ” activities. After a short period to write, study, or read a short passage, students explain their thought processes to a partner. Eventually, the pair presents its thoughts to the entire class for questions, discussion, and ways to build more ideas. Through discussing ideas openly, teachers can evaluate students’ thought processes and ask open-ended questions for future reflection. It's also a great chance to consider both sides. Taking different perspectives into consideration is important for critical thinking (and everyday life).
Think-Aloud A “ think-aloud ” activity is an easy metacognitive technique to demonstrate ways monitoring thinking. Use think-aloud activities to show the process of problem-solving or reading for summarization. By checking math, making notes, or rereading parts of a text, learners can become self-corrective and self-directed. Incidentally, think-aloud activities are also useful assessments of creative skills that improve comprehension. You can also try these metacognition steps with reading assignments:
As students develop metacognitive control, they begin to plan and check their work. In later stages of development, they will be able to evaluate their progress and reflect on prior knowledge with skill. Motivated students will think through the consequences and implications of their actions by visualizing outcomes and setting up strategies to maneuver around potential obstacles. Metacognition is the ability to monitor thinking and strategy through self-examination. Ask students to practice active learning and measure themselves with these questions:
Remember that building mental fortitude is a long process. It’s important to have patience. Cognitive skills don’t fully develop until the teenage years or after. It’s different for everyone, but it can be scaled quickly in learning communities like schools and classrooms.
Looking for more ways to incorporate critical thinking into your lessons? Learn more about how you can elevate your teaching and empower your students to become inquisitive, reflective thinkers.
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This chapter critically reviews the literature of metacognition and metacognitive teaching to shed light on the need for the effective implementation of a competency-based curriculum. Studies conducted on memory, problem solving, reading and comprehension, and the findings on the developing metacognitive ability will be critically discussed. The effects of metacognitive teaching on improving students’ academic achievements, enhancing their abilities in problem solving and mathematical skills, fostering reading and comprehension skills, advancing critical thinking skills, self-regulated learning, independent learning, and promoting self-management communication and collaboration skills will be articulated. An empirical model for analysing the effects of student metacognition on their academic learning outcomes is discussed.
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Cheng, E.C.K., Chan, J.K.M. (2021). Metacognition and Metacognitive Learning. In: Developing Metacognitive Teaching Strategies Through Lesson Study. Springer, Singapore. https://doi.org/10.1007/978-981-16-5569-2_2
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Critical Thinking , Metacognition , Mindfulness
I was recently asked “Is mindfulness the same as metacognition?”
It is a reasonable question. The concepts are closely related. However I think they should be teased apart. They are more like cousins than identical twins.
Mindfulness in the everyday sense is something like “having your mind on the job” which I would translate as doing something attentively and carefully.
This is not exactly what Ellen Langer meant by it. Langer is the academic who brought the concept of mindfulness to prominence in social science, and more widely, with publications like Mindfulness and The Power of Mindful Learning . In the former book she says
the key qualities of a mindful state of being [are]: (1) creation of new categories; (2) opennness to new information; and (3) awareness of more than one perspective. (p.62)
Metacognition is basically just thinking about one’s own thinking, though the term generally also has the connotation that the thinking one is doing about one’s thinking is aimed at or being used to improve that thinking.
So with these definitions on the table, it seems fairly clear that metacognition is not the same as mindfulness in either of its senses. Metacognition is concerned what you’re thinking about . Mindfulness is concerned with how you think as you go about what you’re doing.
You can be engaged in your work mindfully, in the ordinary sense, without going up a level, so to speak, and attending to your thought processes themselves – that is, without any metacognition. And I think the same is true for mindfulness in Langer’s sense. I can create new categories, be open to new information, and be aware of more than one perspective, without “stepping back” and thinking about whether and how I am actually doing these things.
In fact I’d go further and say that “expert” mindfulness – the mindful behavior of someone who had truly mastered mindfulness – would not be metacognitive. The truly mindful person would not need to reflect on her thinking, and indeed doing so would actually interfere with mindful activity.
Generally it is beginners who need to think about what they are doing. The learner driver needs to pay lots of attention to even the most mundane aspects of driving, such as where the gearshift is. The experienced driver pays very little attention to driving, and can carry on a lively conversation instead.
The same is true for thinking. “Beginner” thinkers – that is, thinkers who have only just begun to try to rise above ordinary (in)competence – will need to pay lots of attention to their thinking, with the intent to understand how they are thinking and to modify that thinking in line with certain guidelines. As they master those alternative patterns of thought, the need for metacognitive reflection as a steering mechanism diminishes.
When you first attempt to cultivate Langerian mindfulness, you would need to pay attention to how you are going about your tasks, and in particular how you are thinking as you go about them; and you would have to be thinking about how that thinking could be modified in a “mindful” direction. Thus, metacognition would be an essential activity. But as you mastered mindfulness, you could just be mindfully engaged without needing to think about it (the thinking). This is good because whatever mental energy you might have put into reflecting on your thinking can instead be devoted to the primary task, deepening your mindful engagement.
Coming from the other direction, metacognition can be “un-mindful”. I can think about my thinking without (1) creating new categories, etc.. In fact a beginner’s metacognition is likely to be quite “mindless” in this technical sense. But just as you will, say, exercise better if you do so mindfully, so you will cognize and indeed metacognize better if you do so mindfully.
Thus mindfulness and metacognition differ in this respect: novice mindfulness is metacognitive; expert metacognition is mindful.
All this reminds me of an issue in the definition of critical thinking. If you look in the academic literature, there are lots of different definitions of “critical thinking.” My feeling is that nobody has every really improved on Francis’ Bacon’s account back in 1605:
For myself, I found that I was fitted for nothing so well as for the study of Truth; as having a mind nimble and versatile enough to catch the resemblances of things … and at the same time steady enough to fix and distinguish their subtler differences; as being gifted by nature with desire to seek, patience to doubt, fondness to meditate, slowness to assert, readiness to consider, carefulness to dispose and set in order; and as being a man that neither affects what is new nor admires what is old, and that hates every kind of imposture.
However for most people this definition is too wordy, too complicated, and just too… old . Surely these days we can pin down the essence of critical thinking more precisely and succinctly? If you really want the concept in a nutshell, then my version is
The art of being right
which may not capture every nuance, but is, I sincerely maintain, “better than any other definition that short.”
Anyway, one of the better known figures in the field, Richard Paul, has defined critical thinking as
The art of thinking about your thinking, while you’re thinking, so as to make your thinking more clear, precise, accurate, relevant, consistent, and fair…
This seems to me almost exactly wrong. Sure, critical thinking is thinking that is clear, precise, etc.. But there should be no requirement that you have to think about your thinking. Just think clearly, precisely, etc. about your topic – your health, the financial crisis, or whatever. The beginner critical thinker will have to reflect on her thinking, in order to improve that thinking. But the expert critical thinker just will be clear, precise etc. in thinking about the matter at hand. Requiring the sharp thinker to think about her thinking would be like requiring the expert tennis player to think about her stroke while playing. It would immediately degrade her game.
So Richard Paul, my advice is – chop off the first phrase and you’d have a good definition. You might then add that if you’re a novice, then in order to make thinking more clear, etc., you may have to do some reflecting on your thinking. But your goal will be to get beyond that stage as quickly as you reasonably can.
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Tim, I appreciate your post. Although I am a fan of Richard Paul, I do agree with your point about defining critical thinking. It makes sense to me.
thanks, Nicole
I like your definition of CT — “the art of being right”. Short, sweet, and captures the essence.
As far as Paul goes (and others too), on introspection as a means to avoid cognitive biases: I’ve yet to meet a critical thinker so advanced in his/her abilities that they are completely free of such biases. I still think introspection is necessary even among the “expert” thinkers.
Hi Guys, I like the expression ‘the art of being right”, as I feel it the “best’ abstraction/generalization of all critical thinking conceptualizations. However, I am not sure if it is qualified to be a ‘definition’ of CT. To me, or perhaps from a mathematician / engineer perspective, IT is more like a ‘characterization’ CT. The majority of existent CT definitions per se I feel are actually ‘descriptions’ or ‘characterizations’ of CT. Like Tim, I also thinking Francis’ Bacon’s account is the best ‘description’ of a critical thinker. I am a fan of Tim, John McPeck, and Tim Van Gelder, though.
I find your post quite enlightening in my attempt to differentiate between metacognition and critical thinking.
I am learning to practice mindfulness at the moment. When I realise my thoughts are wandering I bring them back to the present moment. Is this act of realising my thoughts have wandered, and bringing them back both an example of mindfulness and metacognition?
Thanks for this post. As a graduate teacher interested in incorporating mindfulness into my own teaching as well as my students learning, it is great to juxtapose these two very useful concepts to learning.
Thanks again!
Relation between metacognitive strategies, motivation to think, and critical thinking skills.
Critical thinking is a complex reasoning skill, and even though it is hard to reach a consensus on its definition, there is agreement on it being an eminently cognitive skill. It is strongly related with reflective and metacognitive skills, as well as attitudinal or motivational aspects, although no model has yet been able to integrate these three elements. We present herein the preliminary results of a study seeking to establish these relations, in a sample of Chilean university students. 435 students from three universities participated, of which 88 were men, 333 were women, and 14 did not indicate their gender. Their ages ranges between 18 and 51 years old ( M = 21, SD = 3.09). Three instruments were applied, one to measure metacognitive strategies, one to measure motivation to critical thinking, and a third to measure critical thinking skills. The relation was analyzed via structural equations. The results show a positive, strong, and significant relation between metacognition and motivation to think. However, only a weak significant relation was observed between motivation to think and critical thinking, and no direct relation was found between metacognition and critical thinking. We hypothesize a significant but moderate relation between the variables, where metacognition influences motivation to think, which in turn influences critical thinking skills. Factors are discussed which could negatively affect the studied relations, as well as the importance of generating integrated models between the three variables, as they would show a theoretical and empirical link.
Critical thinking is a relevant topic for the 21st century, highlighted by Unesco as one of the skills to develop among students to properly face the challenges of this century ( Scott, 2015 ). Despite its importance for human development, its implementation in educational curricula has been difficult to carry out, both at the level of school systems and in higher education systems ( Ossa et al., 2018 ; Silva Pacheco, 2019 ).
This difficulty of incorporating critical thinking into the educational process may be related with the complexity of the task. On one side, there is discussion as to whether the process can be taught as a skill, or whether it is more of a facet of thinking which can only be stimulated in a concrete way ( Saiz, 2017 ). Building on this factor, the complexity of the matter is also expressed in the attempts at defining the process, since there are various definitions of critical thinking. These definitions present different natures, ranging from only cognitive reasoning processes; cognitive and metacognitive processes; cognitive, metacognitive and attitudinal processes; and finally, cognitive, metacognitive, attitudinal, and social agency processes ( Montero, 2010 ; Rivas and Saiz, 2012 ; Ossa and Díaz, 2017 ; Saiz, 2017 ).
As society and socio-cultural challenges have become more complex, it is necessary to adopt more complex perspectives on human processes. Critical thinking perspectives which help integrate diverse processes could be more pertinent for the effective development of this skill among people ( Paul and Elder, 2003 ).
Critical thinking has been linked to different skills, both cognitive and non-cognitive, for example, problem solving, scientific reasoning, motivation, metacognition, and now ultimately creativity ( Saiz and Rivas, 2008 ; Tamayo-Alzate et al., 2019 ; Halpern and Dunn, 2021 ; Muñoz and Ruiz, 2022 ; Santana et al., 2022 ). Of these skills, problem solving has been incorporated as a constituent element of critical thinking in some models; Likewise, motivation and metacognition are closely related factors and it has been proposed that they are satellite skills for critical thinking processes ( Valenzuela and Nieto, 2008 ; Rivas and Saiz, 2011 ; García, 2022 ), although no empirical information has been shown to clearly demonstrate this. The objective of this paper is precisely to show the relationship between motivation to think and metacognition with critical thinking, in order to contribute to what is proposed.
Even when critical thinking is a broadly used concept in the academic and educational world, with a wide range of studies in the last decade, it continues to be a difficult phenomenon to conceptualize and to create little consensus ( Ossa et al., 2016 ; Saiz, 2017 ; Díaz et al., 2019 ).
It is conceptualized as a cognitive mechanism which filters information about the ideological intentions accompanying said information, via continual questioning of knowledge production practices, and the recognition of its different perspectives ( Yang and Chung, 2009 ; Montero, 2010 ).
It is a type of thinking oriented toward data and action, in a context of solving problems and interacting with other people ( Daniel and Auriac, 2012 ; López, 2012 ). Critical thinking is self-directed, self-disciplined, self-regulated and self-corrected. It involves undergoing rigorous standards of excellence and a conscious dominion of its use. It also implies effective communication and the development of problem solving skills ( Saiz and Rivas, 2008 , 2012 , 2016 ).
Critical thinking is characterized by generating higher-level cognitive processing in people, centered on the skills of reflecting, comprehension, evaluation and creation. It therefore requires high intellectual development. However, it is also a skill which can be developed, since there are no important differences between people with average and high intellectual levels with regards to developing critical thinking ( Sierra et al., 2010 ).
Since critical thinking is a high-level cognitive process, and the ability to generate an elaborated thought, a close relation has been proposed with elements which are not considered merely cognitive, including metacognition ( Rivas et al., 2022 ). Metacognition is a reflective process which helps deepen thought, regulate, and generate consciousness about thought ( Tamayo-Alzate et al., 2019 ; Drigas and Mitsea, 2020 ). It has been worked on as both a reflective process of self-knowledge, and as a skill which helps develop other cognitive processes including memory, learning, or even intelligence, since different levels of application can be established in its use ( Drigas and Mitsea, 2021 ).
There is evidence that metacognitive strategies can influence critical thinking and its components. For one, it improves the use of metacognitive strategies due to intervention in critical thinking. It also improves the use of critical thinking with metacognitive strategies in interventions done with psychology students at universities ( Ossa et al., 2016 ; Rivas et al., 2022 ). Significant and positive relations have also been found between critical thinking and metacognitive consciousness among medical students, although not for regulation and knowledge tasks ( de la Portilla Maya et al., 2022 ).
In this way, we can observe a relative influence on the way that people think about thinking, since metacognition supports decision making and final evaluation about strategies to resolve problems ( Rivas et al., 2022 ).
Some authors also indicate the presence of another non-cognitive component in critical thinking, which is disposition or motivation ( Facione et al., 2000 ; Saiz and Rivas, 2008 ; Marin and Halpern, 2011 ; Valenzuela et al., 2014 ; Halpern and Dunn, 2023 ). This component is fundamental to achieve this skill, since even when the indicated cognitive functions are available, if people either lack the desire to apply critical thinking or deem it inconvenient to do so, critical thinking will not be adequately manifested ( Valenzuela and Nieto, 2008 ; Valenzuela et al., 2014 ).
This non-cognitive element is based on human attitudes or motivations which complement the use of critical thinking, allowing it to be better developed, since they drive personal improvement ( Boonsathirakul and Kerdsomboon, 2021 ). The factors presented as facets of a disposition toward critical thinking include seeking truth, open-mindedness, being analytical, systematicity, curiosity, self-confidence and maturity (Facione, in Boonsathirakul and Kerdsomboon, 2021 ).
However, considering these non-cognitive elements as dispositions of a being also involves assuming certain personality traits or dimensions of values which cannot always be adequately measured. They should thus be considered more as motivational aspects, since they could be better defined and with a greater possibility of modification, given that they are more related with behavioral and perceptual elements ( Valenzuela et al., 2014 , 2023 ). From this perspective, we understand that non-cognitive components are based on the expectations and value given to the task. In this way, we establish a direct and causal relation between motivation and critical thinking, where the former explains critical thinking development by between 8 and 17%, according to the instrument used to measure it ( Valenzuela et al., 2023 ).
In this way, promoting motivational aspects is a relevant factor for developing cognitive and metacognitive processes, since complex processes are exhausting and require a high and constant investment of cognitive and emotional factors ( Valenzuela and Nieto, 2008 ; Valenzuela and Saiz, 2010 ; Gaviria, 2019 ; Nieto-Márquez et al., 2021 ).
Finally, a relative relation has been noted between motivational processes and metacognitive strategies. Correa et al. (2019) performed an evaluation among Chilean high school students about the use of metacognitive strategies and motivation to critical thinking in bias recognition. They found a positive, significant, and medium-intensity correlation ( r = 0.50, p < 0.001) between both variables, which indicates that cognitive and non-cognitive factors have a relevant link for human thought.
With the aforementioned background, we can hypothesize the existence of a significant and positive relation between critical thinking, metacognitive strategies, and motivation to think critically; that motivation to think directly affects critical thinking; and those metacognitive strategies are related with both variables.
In this article it will be showed preliminary results from this relation, presenting a relational model based on structural equations which would allow for establishing direct and mediated relations between said variables.
A correlational study was done via structural equations.
435 students from pedagogy majors at three Chilean universities participated in the study. Of these, 88 were male (20.2%), 333 were female (76.6%), 7 were students of unidentified gender (1.6%), and 7 did not respond (1.6%). Students’ ages fell between 18 and 51 years ( M = 21, SD = 3.09). The careers to which the students belong are in the area of pedagogy, in specialties of mathematics (22%), history (8%), science (15%), special education (15%), and early childhood education (40%).
For this study, a battery with three instruments was applied:
1. Metacognitive strategy questionnaire from O’Neil and Abedi, adapted into Spanish by Martínez (2007) . This measure metacognitive strategies applied to different academic tasks. There are 20 items organized into three dimensions: self-knowledge (referring to metacognitive consciousness), self-regulation (referring to metacognitive control), and evaluation (referring to global task evaluation). Results are recorded with a Likert-type scale of 5 choices (0 to 4 points). This instrument has been applied to Chilean university students and shown adequate reliability indicators. The global Cronbach’s α was 0.87, and for the dimensions it was between 0.62 and 0.65 ( Correa et al., 2019 ).
2. Critical thinking motivation questionnaire from Valenzuela, measuring the intention of applying thinking to knowledge tasks, based on personal expectations and the value of the task. It contains 19 items organized into 5 dimensions: Expectation ( α = 0.774), Importance ( α = 0.770), Cost ( α = 0.775), Utility ( α = 0.790) and Interest ( α = 0.724). Its results are recorded based on a Likert-type scale with 5 alternatives (0–4 points). It has been applied to Chilean university students with strong reliability indicators. The global Cronbach’s α was 0.92, and the values for its dimensions ranged from 0.69 to 0.83 ( Valenzuela and Nieto, 2008 ; Correa et al., 2019 ).
3. Critical thinking task test from Miranda, adapted by Palma Luengo et al. (2021) . This measured the capacity to apply cognitive critical thinking processes to socio-scientific topics. It contains 15 items organized into three dimensions: inquiry (referring to identifying useful information), analysis (referring to the decision to use pertinent and reliable data), and arguing (referring to providing arguments with useful and reliable data). Its results are recorded with a sequence of scores ranging from 0 to 3 points, based on a performance rubric. It has been applied to a sample of Chilean university students with moderately adequate reliability indicators. The overall Cronbach’s α was 0.67, with moderately low values in its dimensions ranging from 0.47 to 0.60 ( Palma Luengo et al., 2021 ).
Three metacognition questions were incorporated into this instrument to reflect on the tasks being done, one for each dimension (e.g., How are you so confident about knowing how to do the activity? ). Two questions about motivation to thinking were also included, in the middle and at the end of the test, seeking to analyze whether there was a disposition to answer a question in a more voluntary form (e.g., Do you want to finish the test here or do you want to continue to delve deeper into the topic? ). The overall Cronbach’s α was 0.78 (five dimensions), and the values were moderately adequate within these dimensions (0.54 for metacognition and 0.73 for motivation).
We made contact with the directors of the pedagogy majors at three different universities, coordinating the process and determining the courses to consider. After this, a talk was carried out in each course, inviting students to participate in the study. Written informed consent was incorporated into the survey, indicating the study objectives and describing the anonymous and voluntary nature of participation. Open consultations were made about participation in applying the surveys, applying the battery of instruments only to those who wished to participate.
After answering the instruments, the data was emptied into a digital database and analyzed with SPSS v.27 and RStudio software. For data analysis, we used inferential and multivariate statistics. For all inference effects, a 5% significance threshold has been considered. In the structural models, we applied formats from Partial Least Squares (SEM-PLS).
We present an application of structural equations based on partial least squares (PLS), designed to model behavioral situations and social sciences. According to Wold (1980) it is fairly flexible, since it is useful for small sample sizes and also does not require distributional assumptions for the variables, along with being useful for predictive analysis as well as theoretical confirmation. With the PLS format, there are three methodological considerations which are relevant for application: (i) choosing variable with items that effectively belong, (ii) valuing items’ reliability and validity, and (iii) properly interpreting the coefficients.
As indicated in this type of modeling, there are two sections. The first is the measurement model, where each dimension is formatively related with its items: i.e., the item contributes to the variable with a certain coefficient called weight ( w ). This factorial weight represents the weighting of the dimension regarding the latent variable which it intends to measure, so that we can expect it to have sufficient magnitude to be statistically significant.
To begin, for the Metacognition variable, the scores for Self-Knowledge ( w = 0.67, p < 0.001, 95% IC: 0.41; 0.97) and Evaluation ( w = 0.34, p < 0.01, 95% IC: 0.12; 0.56) are relevant for generating the latent indicator. For the Motivation variable, the scores for Expectations ( w = 0.21, p < 0.05, 95% IC: 0.25; 0.62), Importance ( w = 0.43, p < 0.001, 95% IC: 0.14; 0.60), and Usefulness ( w = 0.39, p < 0.001, 95% IC: 0.19; 0.25) are representative when generating this indicator. For Critical Thinking, only the Metacognition indicator ( w = 0.71, p < 0.05, 95% IC: 0.56; 0.86) turned out to be appropriate.
The second section of this type of models is called the structural model. It shows the causality relations between the latent variables. Schematically, we consider that a variable X is the cause of another variable Y, and an arrow will go from X to Y. For this study, the relational schematic between variables is given by the following hypothesis set:
H1 : There is a positive effect of the Metacognition Strategy (ME) on Critical Thinking Motivation (MO). H2 : There is a positive effect of Metacognition Strategy (ME) on Critical Thinking (PC). H3 : There is a positive effect of Critical Thinking Motivation (MO) on Critical Thinking (PC).
Figure 1 shows the hypotheses combined with their respective variables, indicating the measurement and structural models.
Figure 1 . Schematic of hypothesis and effects expected. Structural equation model. Source: authors.
The empirical results from the model appear in Table 1 with their significance level.
Table 1 . Structural equation model results.
Finally, in the structural model ( Figure 2 ), we can see the fulfillment of hypothesis H1 ( B = 0.56, p < 0.001, 95% IC: 0.49; 0.63) where a greater perception of Metacognition leads to a greater level of Critical Thinking Motivation. There is also fulfillment for hypothesis H3 ( B = 0.21, p < 0.01, 95% IC: 0.06; 0.34) indicating that greater levels of Critical Thinking Motivation lead to a greater level of Critical Thinking.
Figure 2 . Results schematic. Structural equations model. Source: authors.
Our preliminary study results show ties between the three variables, as indicated both in theory ( Facione et al., 2000 ; Valenzuela and Nieto, 2008 ; Tamayo-Alzate et al., 2019 ) and in other studies ( Correa et al., 2019 ; Rivas et al., 2022 ; Valenzuela et al., 2023 ). However, we found some disparate data with regards to the latter points.
For the structural models, hypotheses H1 and H3 have been fulfilled, reporting statistically significant evidence that greater perceived Metacognition explains a greater level of Critical Thinking Motivation, a greater level of Critical Thinking Motivation implies a higher level of Critical Thinking.
One important aspect here is that a significant relation was found between motivation and critical thinking skills, which is supported by Valenzuela et al. (2023) . While the value of the relation is moderate, it can be related, as presented in the aforementioned study, and may be due to the type of instrument used to measure critical thinking. One notable aspect is that the motivation question incorporated into the critical thinking task instrument had little weight within this instrument. However, this could be explained because the questions sought to consider effort for the task. Reviewing the components of the critical thinking motivation survey, the dimensions with the strongest ties were those oriented towards expectations, usefulness and importance, not effort or energy costs.
It is possible that the relationship between metacognition and motivation to think is established because, from the theoretical model used ( Valenzuela and Nieto, 2008 ; Valenzuela et al., 2014 ), the expectation of the task, and its assessment of usefulness (aspects motivation), require an evaluation process (metacognitive aspect); However, this idea must be deepened and reviewed in more detail.
Considering metacognition, no direct relation was observed between the instrument used in this study to measure the metacognitive strategies of self-knowledge, self-regulation and evaluation on one hand, and critical thinking on the other. This situation goes against other studies’ findings ( de la Portilla Maya et al., 2022 ; Rivas et al., 2022 ), and may be explained by the type of instrument used, which may not be sensitive to the critical thinking tasks measured by the test from Palma Luengo et al. (2021) .
The relation discovered about metacognition supporting critical thinking motivation, in order to thus achieve better critical thinking, is one of the key relevant findings in this study. It implies that reflecting on oneself and tasks can generate greater expectations and evaluation for the task, which can drive better performance. These results still need more breadth and depth from further research.
This study is only a preliminary report of results, to account for the relationship between the aforementioned variables and propose that critical thinking benefits from metacognitive and motivational work. Its limitations are the fact that its objective was only empirical, in order to account for the relationship raised in studies ( Valenzuela and Nieto, 2008 ), so the theoretical depth was less. On the other hand, there was a limited number of participating students, and only from some university majors. Likewise, it is considered that the critical thinking test that was used presents adequate reliability values overall, but with less powerful values in some of its dimensions (specifically, inquiry and motivation). It is considered necessary to replicate the study with another instrument and a larger sample to more fully support the results found.
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
The studies involving humans were approved by Pedro Labraña Research Unit of Bio-Bio University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
CO: Conceptualization, Methodology, Project administration, Writing – original draft. SR: Investigation, Supervision, Writing – review & editing. CS: Conceptualization, Methodology, Writing – review & editing.
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This article was developed in part with funding from FONDECYT Project 11220056, from the Chilean National Research and Development Agency (ANID).
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.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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Keywords: critical thinking, structural models, cognition, motivation, pedagogy
Citation: Ossa CJ, Rivas SF and Saiz C (2023) Relation between metacognitive strategies, motivation to think, and critical thinking skills. Front. Psychol . 14:1272958. doi: 10.3389/fpsyg.2023.1272958
Received: 04 August 2023; Accepted: 13 November 2023; Published: 04 December 2023.
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Copyright © 2023 Ossa, Rivas and Saiz. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Carlos J. Ossa, [email protected]
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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Metacognition and critical-thinking are closely related concepts and the overlap between the two ideas presents opportunities for teachers.
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.
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The difference in the two models is a function on how metacognition is treated whether with two or eight indicators. Metacognition with two indicators as it affects critical thinking describes collective skills of metacognition for knowledge and regulation of cognition in order to attain critical thinking while metacognition with eight ...
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significant relationships were found between age, grade, and gender and the latent categories of emotional regulation among high school students. Moreover, significant differences were observed among these four latent categories of emotional regulation in habits, understanding, reflection, and critical thinking behaviors.