This article is very different from the articles that I usually write. Normally, I don’t put references in my articles because I use them as seed ideas for my students to build from in my classes. However, this article has a different purpose.
I am getting ready to publish an app for measuring and developing metacognitive awareness, the foundation of metacognition. As a result, I need to address the question, “Why is metacognition such a big thing?”. In this post, I will present research that tells us why metacognition is such a big thing.
The following factors are presented here along with the abstracts to the published research papers that underlie these findings.
- Creativity – becoming aware enough of what you know and what others don’t know to stop imitating
- Critical Thinking
- Critical thinking and analysis is all about evaluating the new against what you already know about a subject.
- Deep-level Reasoning
- Deep reasoning involves real thinking about what you already know.
- Logical Thinking
- Logical thinking requires you to know how you think
- Cognitive Flexibility
- Cognitive flexibility requires you to know what cognitive options you have available.
- Mindfulness is an awareness of the moment that requires you to be aware of your own thoughts and thought processes
- Academic Success
- Academic success depends on thinking – if IQ is the engine, metacognition is the driver.
- Rational Thought
- Making better rational decisions requires you to think about what you already know
- Intellectual ability contributes for about half as much as metacognition to learning something.
- Increases in metacognition significantly reduce relapse in people who suffer from depression.
Three aspects of creative thinking and production were examined: (a) metacognitive processing, (b) the knowledge base, and (c) personality variables. It was concluded that all three are essential elements, that they operate interactively, and that the results of creative thinking and problem solving are best assessed through evaluation of the products.
Feldhusen, J. F. (1995). Creativity: A knowledgebase, metacognitive skills, and personality factors. The Journal of Creative Behavior, 29(4), 255-268. doi/10.1002/j.2162-6057.1995.tb01399
This article presents a model that conceptualizes creative thinking as a metacognitive process. The author briefly discusses the concepts of metacognition and self-regulation and then links these concepts together in a model, that has heuristic value for those interested in the dynamics of self-regulated thought. Creativity techniques are reframed as metacognitive strategies. A new definition of creativity as a metacognitive process is discussed. The model has implications for education, research, and creativity training.
Pesut, D. J. (1990). Creative thinking as a self-regulator metacognitive process – A model for education, training, and further research. The Journal of Creative Behaviour, 24(2), 105 – 110. doi/10.1002/j.2162-6057.1990.tb00532
The study investigated the influence of metacognition on critical thinking skills. It is hypothesized in the study that critical thinking occurs when individuals use their underlying metacognitive skills and strategies that increase the probability of a desirable outcome. The Metacognitive Assessment Inventory (MAI) by Schraw and Dennison (Contemporary Educational Psychology 19:460–475,1994), which measures regulation of cognition and knowledge of cognition, and the Watson-Glaser Critical Thinking Appraisal (WGCTA) with the factors inference, recognition of assumptions, deduction, interpretations, and evaluation of arguments were administered to 240 college students from different universities in the National Capital Region in the Philippines. The Structural Equations Modeling (SEM) was used to determine the effect of metacognition on critical thinking as latent variables. Two models were tested: (1) In the first model, metacognition is composed of two factors while (2) in the second model, metacognition has eight factors as they affect critical thinking. The results indicated that in both models, metacognition has a significant path to critical thinking, p < .05. The analysis also showed that for both metacognition and critical thinking, all underlying factors are significant. The second model had a better goodness of fit as compared with the first as shown by the RMSEA value and other fit indices.
Magno, C., (2010). The role of metacognitive skills in developing critical thinking. Metacognitive Learning, 5, 137 – 156. doi/10.1007/s11409-010-9054-4
The need to cultivate students’ use of metacognitive strategies in critical thinking has been emphasized in the related literature. The present study aimed at examining the role of metacognitive strategies in critical thinking. Ten university students with comparable cognitive ability, thinking disposition and academic achievement but with different levels of critical thinking performance participated in the study (five in the high-performing group and five in the low-performing group). They were tested on six thinking tasks using think-aloud procedures. Results showed that good critical thinkers engaged in more metacognitive activities, especially high-level planning and high-level evaluating strategies. The importance of metacognitive knowledge as a supporting factor for effective metacognitive regulation was also revealed. The contribution of metacognitive strategies to critical thinking and implications for instructional practice are discussed.
Ku, K. Y. L., Ho, I. T., (2010). Metacognitive strategies that enhance critical thinking. Metacognitive Learning, 5, 251 – 267 http://link.springer.com/article/10.1007%2Fs11409-010-9060-6
It is generally accepted that providing explanations during a task can facilitate problem-solving performance in both adults and children. This paper aims to answer two important questions. First, can current theories of explanation be generalised to children’s explanations of self-generated answers? Second, what is the impact of such self-explanation on the development of children’s analogical reasoning skills? One-hundred-and-ten six- and seven-year-old children took part in seven sessions of matrix completion trials in one of ﬁve conditions: (l) explanation plus feedback; (2) explanation only; (3) feedback only; (4) practice; and (5) control. Analysis revealed that, contrary to existing theory, explanation of self- generated answers is not the most effective way to encourage the development of analogical reasoning. Rather, feedback on response accuracy is necessary for the attainment of heighten levels of performance. Results also indicate that children shift from using surface-level perceptual cues as a basis for their responses to a more sophisticated strategy involving an understanding of deeper-level relational structures. It is argued that these results support a metacognitive processing account of the development of analogical reasoning skills rather than an account emphasizing changes in mental representations.
Cheshire, A., Ball, L. J., & Lewis, C. N. (2005). Self-explanation, feedback and the development of analogical reasoning skills: Microgenetic evidence for a metacognitive processing account. In Proceedings of the Twenty-Second Annual Conference of the Cognitive Science Society, ed. BG Bara, L. Barsalou & M. Bucciarelli (pp. 435-41). http://csjarchive.cogsci.rpi.edu/Proceedings/2005/docs/p435.pdf
The author investigated whether high levels of metacognitive knowledge about problem-solving could compensate for low overall aptitude. To test this hypothesis, a 2 (high–low aptitude) × 2 (high–low metacognitive ability) design was used to analyze children’s problem-solving performance. Processing differences between ability groups were determined through an analysis of “think aloud” protocols. Protocols were analyzed at two levels: (a) grouping of subroutines that function as heuristic processes and (b) grouping of subroutines that function as strategies. Regardless of aptitude, higher metacognitive children performed better than the lower metacognitive children. Higher metacognitive ability groups were more likely to rely on hypothetico-deductive (if–then propositions) and evaluation (check the adequacy of a hypothesis) strategies than was the lower metacognitive group. The results are discussed in terms of the independence of metacognition and general academic aptitude. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Swanson, H. L. (1990). Influence of metacognitive knowledge and aptitude on problem solving. Journal of educational psychology, 82(2), 306. doi/10.1037/0022-06188.8.131.526
This essay describes a general approach to building perturbation-tolerant autonomous systems, based on the conviction that artificial agents should be able to notice when something is amiss, assess the anomaly, and guide a solution into place. This basic strategy of self-guided learning is termed the metacognitive loop; it involves system monitoring, reasoning about, and, when necessary, altering its own decision-making components. This paper (a) argues that equipping agents with a metacognitive loop can help to overcome the brittleness problem, (b) details the metacognitive loop and its relation to on going work on time-sensitive common sense reasoning, (c) describes specific, implemented systems whose perturbation tolerance was improved by adding a metacognitive loop, and (d) outlines both short-term and long-term research agendas.
Anderson, M. L., & Perlis, D. R. (2005). Logic, self-awareness and self-improvement: The metacognitive loop and the problem of brittleness. Journal of Logic and Computation, 15(1), 21-40. doi/10.1093/logcom/exh034
Cognitive complexity is defined as the number of independent dimensions-worth of concepts the individual brings to bear in describing a particular domain of phenomena; it is assessed with a measure of information-yield based on an object-sorting task. Cognitive flexibility is defined as the readiness with which the person’s concept system changes selectively response to appropriate environmental stimuli; it is assessed by inviting the subject to expand Me groups he has created on the original sorting task. In general, the greater a subject’s cognitive complexity, (a) the greater is the likelihood Mat he will expand the groups, and (b) the greater is his tendency to gain information (i.e., dimensional complexity) by the expansion. The measure of dimensional complexity was found to be fairly stable over two different lists of objects; moreover, it was found to correlate with independent measures of knowledge about the object-domain.
Scott, W. A. (1962). Cognitive complexity and cognitive flexibility. Sociometry, 405-414 doi/10.2307/2785779
Computers have become a topic of concern, debate, argument, dogmatism, and inquiry among a variety of people who are interested in the fate and effectiveness of the educational system. This book presents working hypotheses of ways in which computers may fit into and/or transform classroom education. Through the exploration of learning and cognitive theory as it infuses technological developments, this volume promises to illuminate a number of important issues, including experiential learning and non-traditional computer-based instruction.
Nix, D., & Spiro, R. (Eds.). (1990). Cognition, education and multimedia: Exploring ideas in high technology. Routledge. http://www.guilfordpress.co.uk/books/details/9780805800364/
People who recall or forecast many pleasant moments should perceive themselves as happier in the past or future than people who generate few such moments; the same principle should apply to generating unpleasant moments and perceiving unhappiness. Five studies suggest that this is not always true. Rather, people’s metacognitive experience of ease of thought retrieval (“fluency”) can affect perceived well-being over time beyond actual thought content. The easier it is to recall positive past experiences, the happier people think they were at the time; likewise, the easier it is to recall negative past experiences, the unhappier people think they were. But this is not the case for predicting the future. Although people who easily generate positive forecasts predict more future happiness, people who easily generate negative forecasts do not infer future unhappiness. Given pervasive tendencies to underestimate the likelihood of experiencing negative events, people apparently discount hard-to-believe metacognitive feelings (e.g., easily imagined unpleasant futures). Paradoxically, people’s well-being may be maximized when they contemplate some bad moments or just a few good moments.
O’Brien, E. (2013). Easy to Retrieve but Hard to Believe Metacognitive Discounting of the Unpleasantly Possible. Psychological science, 24(6), 844-851. doi/10.1177/0956797612461359
Although the current literature supports the effectiveness of metacognition as a learning strategy, little is known about the effects of metacognition on academic achievement and happiness. This study analyzed the effectiveness of training metacognition on the academic achievement and happiness of Esfahan University conditional students. Conditional students are the students whose averages are lower than 12 (12 out of 20). After three times of becoming conditional they are expelled from university. The sample consisted of 60 once-conditional female students. They were randomly selected and allocated to an experimental group and a control group. The independent variable was the metacognitive training sessions performed in the experimental group. The Oxford Happiness Questionnaire scores and the students’ second semester average scores in 2003-2004 were dependent variables. The study predicted that training in metacognition should have positive effects on the academic achievement and that it would increase students’ happiness. The results suggested that metacognitive training had increased the academic achievement average of the experimental group. Similarly, metacognitive training had increased the happiness scores average of the experimental group. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Rezvan, S., Ahmadi, S. A., & Abedi, M. R. (2006). The effects of metacognitive training on the academic achievement and happiness of Esfahan University conditional students. Counselling Psychology Quarterly, 19(4), 415-428. doi/10.1080/09515070601106471
High relapse and recurrence rate of depression put financial pressure on already stretched resources for health care. Therefore, the demand for the development of prophylactic treatments in order to keep patients well, once recovered, has increased during the last decade. The development of a new therapy manual for group interventions, ‘Mindfulness-based Cognitive Therapy for Depression’ (MBCT), appears to be one of the first major developments in this direction. MBCT represents an adaptation of the mindfulness meditation approach into a group programme for relapse prevention of depression. This paper links the mindfulness approach into current metacognitive concepts and models of emotional disorders by firstly exploring the limitations of Beck’s schema theory and then describing a more recent multilevel model (interacting cognitive sub-systems, or ICS) which represents the theoretical foundation for the mindfulness approach. A description of the MBCT manual is followed by looking at the methodological limitations of the mindfulness construct and its implications for future therapeutic interventions. Despite its methodological problems, MBCT appears to be an advanced and cost-efficient approach to prophylactic interventions to prevent recovered depressed patients from relapse. Further developments and methodological testing is still required to give the approach an even more robust foundation.
Scherer-Dickson, N. (2004). Current developments of metacognitive concepts and their clinical implications: mindfulness-based cognitive therapy for depression. Counselling Psychology Quarterly, 17(2), 223-234. doi/10.1080/09515070410001728253
The previous research on locus of control or metacognition suggested that they are closely related to academic performance and can be taught to students to improve their academic and non-academic success. These variables were often examined separately in order to explain and predict performance and rarely in a university setting. The integration of these variables into a common framework could provide a deeper understanding of university students’ learning process. This study examined the relationship between locus of control, metacognition, and academic success in a university setting and is based on a hypothesis that the relationship between locus of control and academic success is fully mediated by metacognition. The present study also examined whether metacognition and internal or external locus of control are predictors of academic success. Correlations and regression analyses were used to examine mediation effects of metacognition in the relationship between locus of control and academic performance. The participants were 282 undergraduates of Tomas Bata University in Zlín. The results showed that internal locus of control influences directly both academic success and metacognition; however, external locus of control does not influence performance directly or through metacognition. The direct relationship between internal locus of control and academic success was not significant with metacognition in the equation. The findings from this research may support training programs instructing students on how to adopt effective metacognitive skills and strategies and learn how to perform well if they have a better control of their behavior.
Hrbáčková, K., Hladík, J., and Vávrová, S., (2012). The relationship between locus of control, metacognition, and academic success. Procedia – Social and Behavioural Sciences. 69, 1805 – 1811. doi/10.1016/j.sbspro.2012.12.130
Authored by the foremost researchers in cognitive psychology, the handbook Memory is an outstanding reference tool for all cognitive psychologists and interested professionals. Memory provides an excellent synopsis of the research and literature in this field, including comprehensive chapters on basic theory. The text discusses storage and access of information in both short-term and long-term memory; how we control, monitor, and enhance memory; individual differences in mnemonic ability; and the processes of retrieval and retention, including eye-witness testimony, and training and instruction.
Metcalfe, J., (1996). Metacognitive processes. In E. L. Bjork, & R. A. Bjork (Eds.), Memory (pp. 381 – 407). San Diego, CA: Academic Press. http://www.amazon.com/Memory-Handbook-Perception-Cognition-Edition/dp/0121025713
Inference is elementary and ubiquitous: Cognition always goes beyond the data. Thinking – including problem solving, decision making, judgment, planning, and argumentation – is here deﬁned as the deliberate application and coordination of one’s inferences to serve one’s purposes. Reasoning, in turn, is epistemologically self-constrained thinking in which the application and coordination of inferences is guided by a metacognitive commitment to what are deemed to be justiﬁable inferential norms. The construction of rationality, in this view, involves increasing consciousness and control of logical and other inferences. This metacognitive conception of rationality begins with logic rather than ending with it, and allows for developmental progress without positing a state of maturity.
Moshman, D. (2004). : From inference to reasoning: The construction of rationality. Thinking & Reasoning, 10(2), 221-239. doi/10.1080/13546780442000024
We argue in favour of the general proposition that the nature of reasoning is best understood within a context of its origins and development. A major dimension of what develops in the years from childhood to adulthood, we propose, is increasing meta-level monitoring and management of cognition. Two domains are examined in presenting support for these claims – multivariable causal reasoning and argumentive reasoning.
Kuhn, D., Katz, J. B., & Dean, Jr, D. (2004). : Developing reason. Thinking & Reasoning, 10(2), 197-219. doi/10.1080/13546780442000015
This study investigated the nature of the relation between intelligence and metacognitive skillfulness as predictors of novice learning from text studying. Additionally, effects of text difficulty and time constraint were examined. The intelligence of 46 social-sciences students was assessed before studying two texts on different topics. Half of the participants studied the difficult text under time pressure, while the other half did so for the easy text. Metacognition was scored from thinking-aloud protocols. Results show that metacognition, although correlated to intelligence, also uniquely contributed to comprehension of both texts. Time constraint on studying a difficult text impaired text comprehension.
Veenman, M. V. J., & Beishuizen, J. J. (2004). Intellectual and metacognitive skills of novices while studying texts under conditions of text difficulty and time constraint. Learning and Instruction. 14, 619-638. doi/10.1016/j.learninstruc.2004.09.004
This is the ﬁrst issue of Metacognition and Learning, a new international journal dedicated to the study of metacognition and all its aspects within a broad context of learning processes. Flavell coined the term metacognition in the seventies of the last century (Flavell, 1979) and, since then, a huge amount of research has emanated from his initial efforts. Do we need metacognition as a concept in learning theory? Already in 1978, Brown posed the question whether metacognition was an epiphenomenon. Apparently, she was convinced otherwise as she has been working fruitfully for many years in the area of metacognition. Moreover, a review study by Wang, Haertel, and Walberg (1990) revealed metacognition to be a most powerful predictor of learning. Metacognition matters, but there are many unresolved issues that need further investigation. This introduction will present ten such issues, which are by no means exhaustive. They merely indicate what themes might be relevant to the journal.
Veenman, M. V. J., & Spaans, M. A. (2005). Relation between intellectual and metacognitive skills: Age and task differences. Learning and Individual Differences. 15, 159-176. doi/10.1007/s11409-006-6893-0
Metacognitive awareness is a cognitive set in which negative thoughts/feelings are experienced as mental events, rather than as the self. The authors hypothesized that (1) reduced metacognitive awareness would be associated with vulnerability to depression and (2) cognitive therapy (CT) and mindfulness-based CT (MBCT) would reduce depressive relapse by increasing metacognitive awareness. They found (1) accessibility of metacognitive sets to depressive cues was less in a vulnerable group (residually depressed patients) than in nondepressed controls; (2) accessibility of metacognitive sets predicted relapse in residually depressed patients; (3) where CT reduced relapse in residually depressed patients, it increased accessibility of metacognitive sets; and (4) where MBCT reduced relapse in recovered depressed patients, it increased accessibility of metacognitive sets. CT and MBCT may reduce relapse by changing relationships to negative thoughts rather than by changing belief in thought content. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Teasdale, J. D., Moore, R. G., Hayhurst, H., Pope, M., Williams, S., & Segal, Z. V. (2002). Metacognitive awareness and prevention of relapse in depression: empirical evidence. Journal of consulting and clinical psychology, 70(2), 275. doi/10.1037/0022-006X.70.2.275
Rumination has attracted increasing theoretical and empirical interest in the past 15 years. Previous research has demonstrated significant relationships between rumination, depression, and metacognition. Two studies were conducted to further investigate these relationships and test the fit of a clinical metacognitive model of rumination and depression in samples of both depressed and nondepressed participants. In these studies, we collected cross-sectional data of rumination, depression, and metacognition. The relationships among variables were examined by testing the fit of structural equation models. In the study on depressed participants, a good model fit was obtained consistent with predictions. There were similarities and differences between the depressed and nondepressed samples in terms of relationships among metacognition, rumination, and depression. In each case, theoretically consistent paths between positive metacognitive beliefs, rumination, negative metacognitive beliefs, and depression were evident. The conceptual and clinical implications of these data are discussed.
Papageorgiou, C., & Wells, A. (2003). An empirical test of a clinical metacognitive model of rumination and depression. Cognitive therapy and research, 27(3), 261-273. doi/10.1023/A:1023962332399
Anderson, M. L., & Perlis, D. R. (2005). Logic, self-awareness and self-improvement: The metacognitive loop and the problem of brittleness. Journal of Logic and Computation, 15(1), 21-40.
Cheshire, A., Ball, L. J., & Lewis, C. N. (2005). Self-explanation, feedback and the development of analogical reasoning skills: Microgenetic evidence for a metacognitive processing account. In Proceedings of the Twenty-Second Annual Conference of the Cognitive Science Society, ed. BG Bara, L. Barsalou & M. Bucciarelli (pp. 435-41).
Feldhusen, J. F. (1995). Creativity: A knowledge base, metacognitive skills, and personality factors. The Journal of Creative Behavior, 29(4), 255-268.
Hrbáčková, K., Hladík, J., and Vávrová, S., (2012). The relationship between locus of control, metacognition, and academic success. Procedia – Social and Behavioural Sciences. 69, 1805 – 1811.
Ku, K. Y. L., Ho, I. T., (2010). Metacognitive strategies that enhance critical thinking. Metacognitive Learning, 5, 251 – 267
Kuhn, D., Katz, J. B., & Dean, Jr, D. (2004). : Developing reason. Thinking & Reasoning, 10(2), 197-219.
Magno, C., (2010). The role of metacognitive skills in developing critical thinking. Metacognitive Learning, 5, 137 – 156.
Metcalfe, J., (1996). Metacognitive processes. In E. L. Bjork, & R. A. Bjork (Eds.), Memory (pp. 381 – 407). San Diego, CA: Academic Press.
Moshman, D. (2000). Diversity in reasoning and rationality: Metacognitive and developmental considerations. Educational Psychology Papers and Publications, 46.
Nix, D., & Spiro, R. (Eds.). (1990). Cognition, education and multimedia: Exploring ideas in high technology. Routledge.
O’Brien, E. (2013). Easy to Retrieve but Hard to Believe Metacognitive Discounting of the Unpleasantly Possible. Psychological science, 24(6), 844-851.
Papageorgiou, C., & Wells, A. (2003). An empirical test of a clinical metacognitive model of rumination and depression. Cognitive therapy and research, 27(3), 261-273.
Pesut, D. J. (1990). Creative thinking as a self-regulator metacognitive process – A model for education, training, and further research. The Journal of Creative Behaviour, 24(2), 105 – 110
Rezvan, S., Ahmadi, S. A., & Abedi, M. R. (2006). The effects of metacognitive training on the academic achievement and happiness of Esfahan University conditional students. Counselling Psychology Quarterly, 19(4), 415-428.
Scherer-Dickson, N. (2004). Current developments of metacognitive concepts and their clinical implications: mindfulness-based cognitive therapy for depression. Counselling Psychology Quarterly, 17(2), 223-234.
Scott, W. A. (1962). Cognitive complexity and cognitive flexibility. Sociometry, 405-414
Stanovich, K. E. (2009). What intelligence tests miss: The psychology of rational thought. Yale University Press.
Swanson, H. L. (1990). Influence of metacognitive knowledge and aptitude on problem solving. Journal of educational psychology, 82(2), 306.
Teasdale, J. D., Moore, R. G., Hayhurst, H., Pope, M., Williams, S., & Segal, Z. V. (2002). Metacognitive awareness and prevention of relapse in depression: empirical evidence. Journal of consulting and clinical psychology, 70(2), 275.
Veenman, M. V. J., Prins, F. J., & Elshout, J. J. (2002). Initial learning in a complex computer simulated environment. The role of metacognitive skills and intellectual ability. Computers in Human Behavior. 18, 327-342.
Veenman, M. V. J., & Spaans, M. A. (2005). Relation between intellectual and metacognitive skills: Age and task differences. Learning and Individual Differences. 15, 159-176.