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Metacognition: What Does Science Tell Us About Its Role in Learning?

Wednesday, May 5, 2021

In recent decades, metacognitive strategy instruction has captured the attention of researchers as a pivotal evidence-informed learning strategy. Some 20 years ago, Bransford first reviewed the scientific literature and concluded that a metacognitive approach to instruction can help people better learn. Today, large-scale studies of metacognitive strategies no longer focus on their immediate effects—these appear clear—but rather on their long-term effects, which are just beginning to emerge as substantial and sustainable. Metacognitive strategy instruction works, but like most learning strategies, we must clearly understand how it works in order to understand where, when, and with whom it works best.

Metacognition, the target of metacognitive strategy instruction, refers to the processes people use to plan, monitor, and assess how well they understand and can do something. Often described as “thinking about thinking,” metacognition has been divided into two broad activities: regulating cognition and understanding cognition. The first occurs when we plan, monitor, and evaluate our learning; the second happens when we understand the key processes involved in our learning. Instructional strategies focus on either or both, and in doing so, take numerous forms.

Regulating Cognition

Much of the research on metacognitive strategies for regulating cognition comes from the K–12 literature. Between 1988 and 2016, 11 meta-analyses showed that strategies aiming to teach students how to plan, monitor, and evaluate their learning produce medium to large effects on learning. Later studies shown how self-regulation—the aim of these strategies—correlates positively with motivation for learning.

When educators teach cognitive regulation, they often focus on self-directed skills such as goal setting, learning strategy use, performance monitoring, and self-evaluation. Although these skills are discrete and can be taught and learned independently, they are embedded into a larger curriculum focused on other skills and knowledge (for example, mathematics, biology, or composition). An advantage of cognitive-regulation skills is that they can be scaffolded using technologies and job aids to reduce their immediate contribution to cognitive load. This allows learners to devote working memory to content of primary concern while using cognitive regulation strategies.

Consider, for instance, retrieval practice. In my own research, we studied how adults can use retrieval practice for metacognitive calibration. Retrieval practice is the act of repeatedly recalling information to strengthen memory retention. This simple act of self-testing allows learners to monitor and evaluate their level of learning by noting the frequency of correct responses during attempts to retrieve knowledge. Retrieve a high percentage of definitions and applications for key concepts? You probably know them. But if you struggle, then you probably don’t.

Teaching learners how to gauge their performance using this strategy doesn’t overwhelm cognitive load because it allows them to track their performance using outside technologies (via paper, spreadsheets, and so on). Further, it helps learners calibrate where they are in their learning relative to where they would like to be. Traditional study strategies that don’t allow for metacognitive calibration (such as rereading) leave learners unsure of their learning and, surprisingly, often overconfident.

Understanding Cognition

Rather than focus on cognitive regulation, adult learning researchers often focus on helping learners understand their cognition. Although these tasks are related, focusing solely on understanding yields different outcomes for different learners. When educators focus on helping learners understand their cognition, they often use reflective or critical reflective strategies, which tend to target self-discovery rather than performance. The focus on the former may contribute to the continuing difficulty researchers have in illustrating the effects of reflective strategies on novice learning in skills-based settings.

It’s unclear how asking novice learners to identify their assumptions related to what, how, and why they are learning (critical reflection) or asking them to integrate content with personal abilities, attitudes, traits, and experiences (reflection) aids in the recall, retention, application, or transfer of knowledge—what we commonly call learning.

The act of using reflective dialogue or journaling to better understand how one learns something requires people to integrate what they are learning into developed schema or relational networks. This means learners have first developed cognitive representations of what it is they are learning. The obvious problem here occurs with novice learners who haven’t developed these representations.

Research has illustrated that experts, unlike novices, construct complex cognitive representations to mediate their overt performance. This reduces the intrinsic load in what they are learning and allows them to use cognitive resources to better examine and understand the cognitive processes that have led to their understandings and applications of skills and knowledge.

This is an important factor to ensure you are setting learners up for success: Rudimentary understandings of content are prerequisite to reflective processes related to that content. This is consistent with what research has shown us about mental previewing and mental practice of skills and knowledge. Neuroimaging has revealed different brain activity regularities between novices and experts when engaging in mental practice, leading some to conclude, “If you can’t do it, you won’t think it.”

Research has shown that increasingly incorporating reflective learning strategies as learners transition from novice to expert is an effective approach for improving skill adaptation and transfer (see Roessger, 2016 for a review of the literature). Before that transition, however, there is little empirical support for their effect on learning outcomes.

Some Suggestions

Although research illustrates that metacognitive strategy instruction is effective, practitioners must still understand where and when to use it when working with adults in the workplace. My suggestions follow:

First, when working with novice learners, practitioners may introduce self-directed cognitive regulation skills that are easily scaffolded and supported by technologies. This includes setting goals and planning, monitoring and controlling learning, and evaluating the effectiveness of chosen strategies.

Second, as learners progress to experts in a given domain, practitioners may introduce self-directed cognitive understanding strategies such as reflective and critically reflective activities. These ask learners to identify the assumptions underlying what they are learning, as well as the personal experiences, processes, and knowledge that have given rise to how they understand that learning. The aim of this is to create more diverse applications of skills and knowledge and greater learning transfer.


Further Reading

Abrami, P. C., Bernard, R. M., Borokhovski, E., Wade, A., Surkes, M. A., Tamim, R., & Zhang,
D. (2008). Instructional interventions affecting critical thinking skills and dispositions: A stage 1 meta-analysis. Review of Educational Research, 78(4), 1102-1134.

Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). How people learn: Brain, mind, experience, and school. National Academy Press.

Chick, N. (2013). Metacognition. Vanderbilt University Center for Teaching.

Chiu, C.W.T. (1998, April 13-17). Synthesizing Metacognitive Interventions: What Training Characteristics Can Improve Reading Performance? [Paper presentation] Annual Meeting of the American Educational Research Association (AERA), San Diego, CA.

Cooper, G., Tindall-Ford, S. Chandler, P. & Sweller, J. (2001). Learning by imagining. Journal of Experimental Psychology: Applied, 7, 68–82.

Cross, D. R., & Paris, S. G. (1988). Developmental and instructional analyses of children’s metacognition and reading comprehension. Journal of Educational Psychology, 80(2),

de Boer, H., Donker, A. S., & van der Werf, G. (2014). Effects of the attributes of educational interventions on students’ academic performance: A meta-analysis. Review of Educational Research, 85(4), 509-545.

de Boer, H., Donker, A. S., Kostons, D., & van der Werf, G. (2018). Long-term effects of metacognitive strategy instruction on student academic performance: A meta-analysis.
Educational Research Review, 24, 98-115.

Dignath, C., Buettner, G. & Langfeldt, H. (2008). How can primary school students learn self-regulated learning strategies most effectively? A meta-analysis on self-regulation training programmes. Educational Research Review, 3(2), 101-129.

Donker, A. S., De Boer, H., Kostons, D., Dignath van Ewijk, C. C., & Van der Werf, M. P. C. (2014). Effectiveness of learning strategy instruction on academic performance: A meta-analysis. Educational Research Review, 11, 1-26.

Efklides, A., Schwartz, B. L., & Brown, V. (2018). Motivation and affect in self-regulated learning: Does metacognition play a role? In D. H. Schunk, & J. A. Greene (Eds.). Handbook of self-regulation of learning and performance (pp. 64–82). (2nd ed.). Routledge.

Ericsson, K. A. (2002). Attaining excellence through deliberate practice: Insights from the study of expert performance. In J. Ferrari (Ed.), The Pursuit of Excellence through Education (21–56). Lawrence Erlbaum.

Fauzan, N. (2003). The effects of metacognitive strategies on reading comprehension: a quantitative synthesis and the empirical investigation. [Doctoral dissertation, University of Durham].

Ginns, P., Chandler, P. & Sweller, J. (2003). When Imagining Information is Effective. Contemporary Educational Psychology, 28, 229–251.


Haller, E.P., Child, D.A. & Walberg, H.J. (1988). Can comprehension be taught? A quantitative synthesis of metacognitive studies, Educational Researcher, 17(9), 5-8.

Higgins, S., Hall, E., Baumfield, V., & Moseley, D. (2005). A meta-analysis of the impact of the implementation of thinking skills approaches on pupils. Thinking Skills Review Group.

Karpicke, J. D., & Grimaldi, P. J. (2012). Retrieval-Based Learning: A Perspective for Enhancing Meaningful Learning. Educational Psychology Review, 24(3), 401–418.

Klauer, K.J. & Phye, G.D. (2008). Inductive Reasoning: A Training Approach. Review of Educational Research, 78(1), 85-123.

Lee, W., Lee, M.-J., & Bong, M. (2014). Testing interest and self-efficacy as predictors of academic self-regulation and achievement. Contemporary Educational Psychology, 39, 86–99.

Losinski, M., Cuenca-Carlino, Y., Zablocki, M., & Teagarden, J. (2014). Examining the efficacy of self-regulated strategy development for students with emotional or behavioral disorders: A meta-analysis. Behavioral Disorders, 40(1), 52-67.

Miele, D. B., & Scholer, A. A. (2018). The role of metamotivational monitoring in motivation regulation. Educational Psychologist, 53, 1–21.

Northoff, G., & Bermpohl, F. (2004). Cortical midline structures and the self. Trends in Cognitive Science, 8(3), 102-107.

Olsson, C. J., Jonsson, B., Larsson, A., & Nyberg, L. (2008). Motor representations and practice affect brain systems underlying imagery: An fMRI study of internal imagery in novices and active high jumpers. The Open Neuroimaging Journal, 2, 5–13.

Olsson, C. J., & Nyberg, L. (2010). Motor imagery: If you can’t do it, you won’t think it. Scandinavian Journal of Medicine and Science in Sports, 20(5), 711–715

Parker, D., & Roessger, K. M. (2020). Self-directed learning and retrieval practice: Building a bridge through functional contextualism. International Journal of Lifelong Education, 39(2), 205-218.

Roessger, K. M. (2014). The effect of reflective activities on instrumental learning in adult work-related education: A critical review of the empirical research. Educational Research Review, 13, 17-34.

Roessger, K. M. (2016). Skills-based learning for reproducible expertise: Looking elsewhere for guiance. Journal of Vocational Education and Training, 68(1), 118-132.

Schunk, D. H., & DiBenedetto, M. K. (2021). Cognitive regulation. In L. Zhang (Ed.), Oxford encyclopedia of educational psychology. Advanced online publication.

Schunk, D. H., & Usher, E. L. (2019). Social cognitive theory and motivation. In R. M Ryan (Ed.), The Oxford handbook of human motivation (pp. 11–26). (2nd ed.).
Oxford University Press.

Zheng, L. (2016). The effectiveness of self-regulated learning scaffolds on academic performance in computer-based learning environments: a meta-analysis. Asia Pacific Education Review, 17(2), 187-202.

About the Author

Dr. Kevin Roessger is an associate professor of adult and lifelong learning. He received his B.S. in psychology, M.S. in administrative leadership, and Ph.D. in adult and continuing education from the University of Wisconsin-Milwaukee.

Dr. Roessger currently serves as co-editor of adult education's flagship research journal Adult Education Quarterly, and reviewer for the journals Adult Learning and Journal of Continuing Higher Education. He has published numerous studies in the field’s most respected outlets. Currently, his research focuses on applications of learning science to adult learning.

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