| 2013 | vol. 2 | Nº2 University of Alicante
METACOGNITION: EXAMINING THE
COMPONENTS OF A FUZZY CONCEPT
Brianna M. Scott, Ph.D.
Matthew G. Levy, B.S.
Department of Psychology, University of Indianapolis.
Abstract: Metacognition loosely refers to one’s “thinking about thinking” and is often defined by its accompanying skills (such as monitoring and evaluating). Despite the tendency for researchers to use metacognition as an overarching umbrella term, cognitive and educational theorists argue as to whether metacognition is a single construct or made up of distinct, differentiable factors. Given the lack of clarity in the definition of metacognition and its potential components, the purpose of this investigation is to determine whether a two-factor model, representing knowledge and regulation of metacognition, or five-factor model, representing metacognitive knowledge, planning, monitoring, regulation/control, and evaluation, emerges following both exploratory and confirmatory factor analyses. Participants (N =644) from a select number of classes at a large Midwestern university we selected to complete the Metacognition Questionnaire, a 30 item survey designed to measure five components of metacognition that are rarely measured concurrently. An exploratory factor analysis (EFA) revealed a two-factor model resembling metacognitive knowledge and regulation. This two-factor model had
Further confirmatory factor analyses (CFA) showed that the two-factor model outperformed the five-factor model based on the fit indices. This study confirms that the componential view of metacognition should be based on the same two-factor model that has been used in previous literature. Educational implications of this study are discussed.
Keywords: Metacognitive knowledge; metacognitive regulation; factor analysis.
Corresponding author: Brianna M. Scott, Ph.D E-mail: [email protected] Submitted for publication 31/01/2013 Accepted for publication 29/03/2013 Educational Research eJournal-ISSN 2254-0385 © Faculty of Education. University of Alicante DOI: 10.5838/erej.2013.22.04
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1. Introduction strategies). Metacognitive knowledge is conceptualized as the knowledge that has Metacognition is a fuzzy concept but been accumulated over time about widely utilized by the research humans as cognitive beings and that community in multiple fields, including humans have goals, experiences, take psychology, education, learning sciences, action, and perform tasks. The concept of neuroscience, and clinical psychology. metacognitive knowledge can be further Metacognition is often defined by its’ broken down into three specific classes: accompanying skills—monitoring, person, task, and strategy. First, the evaluating, strategy use--or defined as an person class is everything that one knows umbrella term, for instance “thinking about oneself as a cognitive processor and about thinking.” Further, Flavell (1976) the knowledge that other people are also put forward that metacognition as “one’s cognitive in nature. Second, the task- knowledge concerning one’s own oriented class incorporates the knowledge cognitive processes and products” (p. of how the nature of the information one 232). Researchers from multiple fields encounters affects and constrains how take aspects of metacognition and apply one should deal with it (Flavell, 1979). them to their particular fields. However, it Lastly, the strategy class is the knowledge is still unclear if there is an umbrella of which strategies are appropriate to use concept with one major factor that can be in any specific situation. Flavell (1979) labeled metacognition or whether went on to explain that these three levels metacognition has clear and distinct of metacognitive knowledge always factors upon which researchers can base interact with one another. That is, one’s their future research. Expressing the same knowledge about people as cognitive concept using multiple terms (e.g., beings influences one’s understanding of executive skills, metacognitive beliefs, the nature of information within tasks and and judgments of learning (Veenman, how to handle that information with the Van Hout-Wolters & Afflerbach, 2006) appropriate strategies. Additionally, can confuse the construct keeping it Flavell made explicit that metacognitive consistently fuzzy and vague. Research knowledge is “not fundamentally different has shown that metacognitive skills are from other knowledge stored in long-term indeed connected to positive academic memory” (Flavell, 1979, p. 907). That is, outcomes (e.g., Everson & Tobias, 1998; metacognitive knowledge is not removed Isaacson et al., 2006; Tobias, Everson & from the general information processing Laitusis, 1999). Thus, providing a clearer model and the knowledge one has about picture of the nature of metacognition will one’s own thinking is stored just as any help grow the existing knowledge base other type of knowledge. surrounding the educational implications Metacognitive experiences are of this concept. The current study seeks to conceptualized as any conscious determine whether metacognition is a experience (cognitive or emotional) that single construct or made up of multiple accompanies any intellectual activity. factors that can easily be differentiated Flavell (1979) explains that: and applied to students’ educational "First, they can lead you to establish new experience. goals and to revise or abandon old ones. According to Flavell (1979), Experiences of puzzlement or failure can metacognition can be broken down into have any of these effects, for example. four categories: 1) metacognitive Second, metacognitive experiences can knowledge, 2) metacognitive experiences, affect your metacognitive knowledge 3) goals (or tasks), and 4) actions (or base by adding to it, deleting from it, or
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121 | 2013 | vol. 2 | Nº2 University of Alicante revising it…Finally, metacognitive most importantly he focused on the experiences can activate strategies aimed educational implications of metacognition at either of two types of goals-cognitive and on simplifying the categorization of or metacognitive." (p. 908). metacognition to two levels. Granted, These experiences lead people to stronger each level does incorporate a breadth of metacognitive abilities across all processes, but Schraw focused his categories, including goals and actions. attention on just two categories. As a Goals (or tasks) refer to the objectives of means of better understanding the a cognitive activity and action (or difference between cognition and strategies) refers to the cognitions or other metacognition, Schraw (1998) agreed behaviors employed to meet those goals. with Gamer’s (1987) position that 1) Therefore, each time one has a cognitive skills are necessary to perform a metacognitive experience their task, and 2) metacognition is necessary to metacognitive knowledge, goals and understand how the task was performed. actions are affected. This reasoning Another definition of metacognition was makes it very difficult, if not impossible, offered by Alexander, Carr and to study each aspect of metacognition Schwanenflugel (1995) who subdivided individually without accounting for the metacognition into three parts: 1) others. If one were to create a model of declarative metacognitive knowledge, 2) the multiple aspects of metacognition, she cognitive monitoring and 3) regulation of would need to allow for all the variables strategies. Although this definition is to be correlated with one another. One of similar to Flavell’s, the focus has been the reasons that this categorization of placed directly on knowledge, monitoring metacognition is important is because it and regulation of cognition. In fact, influenced researchers for decades and Flavell’s concepts of the three aspects of lead to an interest in dissecting the metacognitive knowledge, metacognitive concept of metacognition and many experiences, and goals, all very broad valiant attempts in the literature to make categories, are not found in Alexander et the term less all-encompassing and al.’s definition. By extracting these “fuzzy.” By categorizing metacognition concepts, Alexander et al. (1995) have into subcomponents, Flavell also opened been able to specify more exact processes the door to the training of specific that account for overall metacognition. metacognitive aspects in the classroom. Although more subcomponents of In order to help clarify the concept of metacognition have been accepted by the metacognition, multiple other definitions field (e.g., attention (Miller & Jordan, have been offered. For example, Schraw 1982), procedural metacognitive (2001) defined metacognition as knowledge (Schraw, 2001), planning knowledge and regulation of cognition. (Zimmerman, 1989), knowledge of Knowledge of cognition is further defined cognition and regulation/monitoring of as awareness and what students know that knowledge seem to be the two main about their own cognition or about components that have been studied cognition in general. Regulation of thoroughly. cognition is defined as a set of activities We believe that incorporating all that help students control their learning, respected components of metacognition attentional resources, use of strategies, into a single definition would be awareness of comprehension breakdowns, beneficial to the research community as a planning, monitoring, and evaluating their whole. One of the most influential own thinking. Schraw’s approach differs problems in metacognitive research is the from that of Flavell for several reasons but lack of clarity in the definition of
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metacognition and its components understanding while performing a task; (White, 1988). Although most research regulating one’s thinking by making the on metacognition breaks the construct proper adjustments; controlling thinking down into two components: knowledge to optimize performance; and evaluating of cognition and regulation of cognition cognitive processes after a solution has (e.g., Brown, 1978; Flavell, 1979; Schraw been found. Table 1 summarizes the & Moshman, 1995), these two categories definitions of each of the aspects of the further consist of several subcomponents. working definition of metacognition. A It involves knowledge of one’s own and related aspect of metacognition, others’ cognitive processes; planning metacognitive accuracy, is one’s ability to prior to performing a task; monitoring accurately predict his outcome on a one’s own thinking, learning and particular task.
Component Working Definition When Used in Learning Process Knowledge What individuals know Before, During, After about their own cognition and cognition in general.
Planning Recognizing the existence of Before a problem, defining the nature of the problem, and deciding on a strategy for solving the problema.
Monitoring The assessment of the During progress of one’s current thinking and work on a particular task.
Regulation/Control The conscious and non- During conscious decisions that one makes based on the output of one’s monitoring processes.
Evaluation The process of appraising After one’s work that has since been completed
Table 1. Metacognitive Terminology.
However, it is still unclear whether these one component of metacognition at a time are all separate components or if items in their studies. Research on representing them may fall under the metacognition has mainly focused on auspices of two larger components of metacognitive knowledge or regulation. metacognition - knowledge and Flavell (1979) put forward that the types regulation. Regardless of the number of of metacognitive knowledge in his components, most researchers have taken definition could not stand alone; there is a a componential view of metacognition, constant interplay among them. We rather than a uni-dimensional view. believe this concept is true for all Furthermore, most have focused on only components of metacognition. That is, the
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123 | 2013 | vol. 2 | Nº2 University of Alicante components of metacognition (e.g., of 30 total items, with roughly six items metacognitive knowledge, metacognition per construct. Future research can take regulation) should not be examined alone the results from this study as a baseline due to the interactions among them. The for the componential view of factor structure of metacognition is metacognition and utilize various unclear due to the contradictions in the methods for replication and validation. literature (e.g. Flavell, 1979; Schraw, The questionnaire used in the current 2001; Alexander, Carr & study was a compilation of three Schwanenflugel, 1995). However, it is existing sources: expected that after performing an 1) Metacognitive Awareness Inventory exploratory factor analysis (EFA) and a (MAI). Schraw and Dennison (1994) set follow-up confirmatory factor analysis out to create a questionnaire that (CFA), either two or five factors will have confirmed the theoretical existence of the best fit with the data. eight subcomponents of metacognition: 1) declarative knowledge, 2) procedural knowledge, 3) conditional knowledge, 2. Material and Methods 4) planning, 5) information 2.1. Participants management strategies, 6) monitoring, Participants totaled 644 undergraduate 7) debugging strategies, and 8) students from a large Midwestern evaluation of learning. However, the university. Students participated within final factor structure was best their regular final exam time in five represented by two factors: knowledge subject areas: chemistry (1 class), of cognition and regulation of cognition, biology (2 classes), astronomy (1 class), accounting for 65% of the sample history (2 classes) and education (2 variance. The resulting questionnaire classes). The average self-reported high consisted of 52-items on a Likert scale. school GPA for the sample was 3.59, The internal consistency for the and the sample consisted of 53.6% Knowledge of Cognition scale was .93 female and 46.4% male students. and for the Regulation of Cognition scale was .88. In two experiments, 2.2. Metacognition Questionnaire Schraw et al. (1994) found a significant A questionnaire was designed for this relationship between knowledge and study to measure five components of regulation of cognition (r=.54 and .45, metacognition, which are prevalent in respectively). the literature but rarely studied 2) Inventory of Metacognitive Self concurrently (i.e., knowledge, planning, Regulation (IMSR). Howard, McGee, monitoring, regulation/control, and Shia and Hong (2000) developed the evaluation). Although there are many IMSR from two existing measures: 1) options for measuring metacognition the junior MAI (Sperling, Howard, & (e.g., think aloud protocols, one on one Murphy, 2002), and the 2) How I Solve interviews, online measurement, etc.), a Problems survey (Fortunato, Hecht, questionnaire was most relevant for this Tittle, & Alvarez, 1991). An particular study given the need to exploratory factor analysis was run and perform a factor analysis to determine produced a five factor solution, the factor structure of metacognition as accounting for 56.3% of the sample a construct. The survey was created variance. The resulting measure (after using a state-trait model, and items were removing items that did not load well written from a state metacognitive on any factor) consisted of 23 items standpoint. The questionnaire consisted measured on a Likert scale. Because
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Howard et al. (2000) were interested in none of the existing measures creating a new measure specific to encompassed all of the components that metacognition in the context of have been theoretically derived and problem-solving, they examined the reported consistently in the literature remaining 23 items and revised or (knowledge, attention, monitoring, etc.). rewrote them to increase reliability, and Therefore, items were extracted from wrote additional items to clearly each of the three existing measures to demonstrate the existence of the five create a more complete measure that factors found in the initial analysis. The theoretically contains the five final version of the measure consisted of aforementioned components (see Table 37 items with a five point Likert scale. 1). Items from the MAI and IMSR In a second study, Howard et al. (2000) were modified where students would conducted another exploratory factor respond about a specific task they had analysis with the new measure again just performed (state-based), rather than revealing a five factor structure with general statements. Items were chosen eigenvalues over 1.12, accounting for for their relevance to five-theoretical 51.6% of the variance. The overall components of metacognition: 1) reliability for the measure was metacognitive knowledge, 2) alpha=.935, and the reliability for each monitoring, 3) planning, 4) evaluation, factor ranged from alpha=.720 to .867. and 5) regulation/control. The resulting The five factors were labeled as: 1) questionnaire consisted of 30 items knowledge of cognition, 2) objectivity, based on a 5-point Likert-type scale 3) problem representation, 4) subtask from strongly agree to strongly monitoring, and 5) evaluation. . disagree. 3) O’Neil’s Self-Assessment Questionnaire (SAQ). The SAQ was 2.3. Procedure created to measure four components of Students in each class were introduced metacognition (planning, monitoring, to the study by their instructor via email cognitive strategies, and awareness). The or in class 2-3 weeks prior to data measure was based on a state-trait model collection. Data collection occurred for metacognition, and the SAQ was during the students’ regularly scheduled written as a state metacognitive final exam period. Before they began measure. That is, the items were written their exam, all students were told that to elicit responses from students about a there was a consent form to be signed particular test they had just taken. This and questionnaire to be completed. It is in stark contrast to the first two was also made clear that participation measures (MAI and IMSR), which were was voluntary and they had the option designed for responses for general to complete the questionnaire after they metacognitive thinking. For 12th finished their exam. An incentive was graders, the reliability for each offered to the students who participated; component subscale (consisting of five one person who completed the survey items) ranged from .73 to .78. A factor from each class would be randomly analysis confirmed only one factor per chosen to win a monetary prize of $50. subscale (O’Neil & Abedi, 1996). The After finishing their final exam, overall reliability of the measure was students completed the informed not provided. consent form and filled out the Each of these existing measures offered questionnaire. The survey included items that matched with a variety of metacognitive items related to the final metacognitive components. However, exam, and this process took
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125 | 2013 | vol. 2 | Nº2 University of Alicante approximately 5-10 minutes. Any underlying structure of the data. Oblique questions that the students had about the rotation (promax method) was used to survey were addressed by raising their rotate the data due to previous research hands and the researcher helped them findings indicating that metacognitive individually with comprehension issues. components are typically moderately to strongly correlated with each other. Variable communalities were examined 3. Results to determine the variability that the 3.1. Characteristics of Questionnaire individual items were accounting for in Items the factors. Three communalities were After an examination of the Q-Q plots low (below .30), suggesting that those for each of the 30 items on the items did not explain much variance questionnaire, all items appeared to within the factors. However, these items follow a normal distribution. There were not removed from the analysis were no outliers for any of the items without examining the factor structure because the responses were restricted and factor loadings. Utilizing the 29 from 1 to 5 (Likert-type scale). items, the index of goodness of fit However, item 28 from the original (Kaiser-Meyer-Olkin test) was calculated, questionnaire was removed due to the yielding a coefficient of .92. This fact that it asked students if they asked established the data as suitable for factor for help when they did not understand analysis according to the .80 criterion put something on their final exam. This forth by Hair, Anderson, Tatham and could have been interpreted as asking a Black (1998). student next to them, which would be Three methods were used to determine considered cheating by instructors. the factor structure of the data. First, all Thus, the following analyses were factors with eigenvalues over 1.0 were performed with 29 items. extracted. Second, the interpretability of Missing Data. On each of the 29 items, the factors was assessed. Lastly, the scree there were between zero and six missing test (Cattell, 1966) was used to finalize data points. No pattern could be the suitability of the factor structure. discerned; thus it was determined that Using the first criteria, five components the data were missing at random. with eigenvalues over 1.0 were extracted, Because dropping all participants with accounting for 52.6% of the variance. any missing data would have decreased Inspection of the five components, the sample size by 36, a multiple however, revealed that the last three imputation procedure, through the components were not easily interpretable. LISREL 8.0, was carried out on all 29 The items that were originally included to items. Multiple imputation is a preferred load on each of the five factors did not do method for dealing with missing data so with any consistency. Thus, a two- even if the data is not missing at random factor model, as cited in the literature or completely at random (Tabachnick & (e.g., Brown, 1978; Flavell, 1979; Schraw Fidell, 2007). & Moshman, 1995), was tested by only extracting two factors in the subsequent 3.2. Exploratory Factor Analysis analysis. An exploratory factor analysis was The two-factor model was most performed on the survey data using SPSS appropriate and interpretable, and the 15.0; there were 640 participants with scree test confirmed this conclusion, complete data for the analysis. Principal which indeed suggested a two-factor axis factoring was used to reveal the model. The examination of the items
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126 | 2013 | vol. 2 | Nº2 University of Alicante loading on each of the factors did lend though this threshold is debated in the themselves to the constructs of literature (Delandshere, 4/3/08, personal metacognitive knowledge and communication). Last, the indices of metacognitive regulation; these two goodness of fit revealed a relatively good subcomponents of overall metacognition fit, with the non-normed fit index are well documented in the literature. (NNFI)=.93, the comparative fit index The two-factor model accounted for (CFI)=.93, and the root mean square error 40.2% of the variance, with eigenvalues approximation (RMSEA)=.08. These of 8.58 and 3.09 for the two factors, three indices of goodness of fit are a respectively. Comrey and Lee (1992) subset of a great many indices but are the established that factor coefficients of .71 recommended indices in the current were excellent, .63 were very good, .55 literature (e.g., Schrieber, Stage, King, were good, .45 were fair, and .32 were Nora & Barlow, 2006). Thus, the final poor. Thus, a conservative threshold was interpretation of the fit of the model was a decided to be between “good” and “fair” low to moderate fit with the data. for this particular analysis, set at .50. Post-hoc model modifications were Examination of the pattern matrix performed to find a better fitting model. revealed that eight items had factor The analysis suggested that two items’ coefficients below .50. These items were error variance should be correlated. After removed from the subsequent examining the two items, it was found confirmatory factor analysis model and that the items were more similar to each the future use of the factors as outcome other than similar to the other items in the measures in multiple regressions. factor. This provided the necessary evidence to follow the suggestions put 3.3. Confirmatory Factor Analysis forth as modification indices and correlate Two confirmatory factor analyses (CFA) the error variance between items 11 and based on the previous exploratory factor 12. The resulting model was a better fit analysis and the theoretical five-factor than the original. Again, the chi-square model were performed through LISREL results were significant, χ2 (366, 8.0. Although the five-factor model did N=640)=1594.44, p<.01. However, the not emerge from the EFA, it was still ratio of χ2 to degrees of freedom was important to examine the differences better than the original model, 4.35. The between the models to assess the best fit indices remained fairly stable with the fitting model. NNFI=.93, CFI=.94, and the The five-factor model based on the RMSEA=.08. Also, the original model original theoretical conception (utilizing had a model AIC of 2146.00 and the all 29 items) was estimated using the modified model had a model AIC of default of maximum likelihood. All 1955.37, a difference of 190.37. factors were hypothesized to be Schreiber et al. (2006) suggest that the moderately correlated. The results for the model AIC can be a comparison between adequacy of the five-factor model were models, with lower scores indicating a mixed. First, the chi-square results were better fit. Thus, it can be concluded that significant, which indicates a poor fit of the modified five-factor model is a better the model, χ2 (367, N=640)=1790.64, fit than the original five-factor model. p<.01. Second, the goodness of fit of the A two-factor confirmatory factor analysis model was tested through the ratio of χ2 was conducted to assess whether the five- to degrees of freedom, which was 4.88. factor or two-factor model was a better fit Ideally, the ratio should be 3.0 or below, to the data. The two-factor model was thus suggesting a moderately poor fit, based on the results from the exploratory
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127 | 2013 | vol. 2 | Nº2 University of Alicante factor analysis, utilizing the final 21 improve the fit of the model. After items. As with the EFA, the two latent examining the two items, it was found factors were hypothesized to be that the items were more similar to each moderately correlated. The default of other than similar to the other items in the maximum likelihood estimation was used factor. This provided the necessary to estimate the model. The results for the evidence to follow the suggestions put adequacy of the model were mixed. First, forth as modification indices and correlate the chi-square result was significant, the error variance between items 11 and which indicates a poor fit of the model, χ2 12. The chi-square for the new model was (188, N=640) =939.72, p<.01. Second, again significant, χ2 (187, N=640) the goodness of fit of the model was =714.13, p<.01. However, the fit indices tested through the ratio of χ2 to the after this modification revealed a better degrees of freedom, which was 5.00. fit, with the NNFI=.95, CFI=.95, and Last, the indices of goodness of fit RMSEA=.07. The change in χ2 between revealed a relatively good fit, with the the two models was significant, χ2change NNFI=.92, the CFI=.93 and the (1, N=640) =225.59, p<.01. Also, the RMSEA=.08. Thus, the final original model had a model AIC of interpretation of the fit of the model, like 1025.72 and the modified model had a the original five-factor model, was a low model AIC of 802.13, a difference of to moderate fit with the data. 223.59. Thus, it can be inferred from Post hoc model modifications were these results that the second model is performed to develop a better fitting stronger than the original. Table 2 model. Based on the modification indices, presents the results from the five-factor it was again suggested that items 11 and models and the two-factor models. 12’s error variance be correlated to
2 2 Model df χ χ /df RMSEA NNFI CFI AIC Original 5- 367 1790.64 4.88 .08 .93 .94 2146.00 factor Original 2- 188 939.72 5.00 .08 .92 .93 1025.72 factor Modified 366 1594.44 4.35 .08 .93 .94 1955.37 5-factor Modified 187 714.13 3.82 .07 .95 .95 802.13 2-factor Table 2. Fit Statistics for Maximum-Likelihood Confirmatory Factor Analyses
Although the modified two-factor into a five-factor structure, the last three model had slightly better degrees of factors did not make conceptual sense freedom to chi-square ratio and slightly and very few, if any, items loaded at an better fit indices than the modified five- acceptable level. Therefore, the factor model, both models are fairly modified two-factor model was similar. However, most literature has accepted. shown a two-factor model of Each of the two factors in the two factor metacognition, the exploratory factor model had moderately strong internal analysis revealed two factors, and the consistency as measured by Cronbach’s two-factor model is clearly more alpha. The reliabilities for each parsimonious. Also, when the component of metacognition are exploratory factor analysis was forced presented in Table 3.
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Metacognitive Factor Cronbach’s alpha (reliability)
Metacognitive Knowledge =.85
Metacognitive Regulation =.87 Table 3. Reliabilities of Metacognitive Knowledge and Metacognitive Regulation Factors.
4.2. Conclusions 4. Discussion and conclusions The main limitation for this study is the 4.1. Discussion use of a self-report questionnaire to The literature is mixed when it comes to measure metacognition. Multiple the definition and components of methods can be used to assess metacognition. Most research metacognition, such as think aloud acknowledges two main components: protocols (e.g., Rosenzweig, Krawec & knowledge and regulation (e.g., Schraw, Montague, 2011), verbal interviews (e.g., 2001), but it remained unclear whether Winne, 2010), and computer logs (e.g., this two-component model was driven by Veenman & Spaans, 2005), among questionnaires and methods that really others. However, each type of were only addressing those two measurement device for metacognition, components. Thus, we created a survey or any internal construct has both pros that incorporated these and other and cons. By combining three existing theoretically derived and consistently questionnaires to create one cited subcomponents of metacognition comprehensive version, we are remaining (planning, evaluation, and monitoring) to consistent with much of the literature and determine whether the two-component providing a useful tool for easily model stands or the five components measuring the two factors of emerge as independent factors. The metacognition. The educational results suggest that a two-factor model implications from this study are clear. does hold up when these other Providing clarity in the definition and subcomponents are introduced in the data. measurement of metacognition, The exploratory factor analysis produced educational psychologists and educators a convincing structure with items loading can continue their work in understanding on two factors that resembled the relationship between metacognition metacognitive knowledge and regulation. and academic achievement. Establishing Items from the planning and evaluation reliable and clear tools to measure subcomponents were split with some students’ metacognitive knowledge and loading on knowledge and some on regulation can assist everyday educators regulation. The monitoring items all in their quest of improving higher order loaded strongly on the regulation factor. thinking skills that are lacking in today’s Regardless of the split of the items, the classrooms. two-factor model outperformed the five- factor model in terms of the Scree Plot and interpretability of factors in the EFA and in terms of fit indices from the CFA References (See Table 2). The componential view of metacognition should be based on the Alexander, J. M., Carr, M., & two-factor model resultant here and in Schwanenflugel, P. J. (1995). much of the previous literature. Development of metacognition
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