COGNITIVE PREDICTORS ACADEMIC SKILLS 1
Executive Functions and Metacognitive Monitoring Are Not Interchangeable in
Educational Settings: Shared and Unique Contributions to Academic Outcomes
Rina Pak-ying Lai, Michelle R. Ellefson, and Claire Hughes
University of Cambridge
Author Notes
This manuscript is under peer review. This is not the final version.
Correspondence concerning this article should be addressed to Michelle R. Ellefson,
University of Cambridge Faculty of Education, 184 Hills Road, Cambridge, CB2 8PQ, United
Kingdom. Email: [email protected].
A joint-council award to the authors funded this research (ES/K010225/1: Economic and Social Research Council, Research Grants Council of HK). Thinking Games website development supported by the Institute of Educational Sciences, U.S. Department of
Education, through Grant R305A110932 to the University of Cambridge. The opinions expressed are those of the authors and do not represent the views of the Institute or the
U.S. Department of Education.
Electronic access to dataset from the entire project: http://reshare.ukdataservice.ac.uk/
852570/. The full data set for the externally funded project is available through the UK Data
Service at http://reshare.ukdataservice.ac.uk/852570/. Materials used for the executive function tasks are available from http://reshare.ukdataservice.ac.uk/852658/. A preprint has been uploaded to https://psyarxiv.com/4jhnz.
Initial analyses for this manuscript were conducted by Rina Pak-ying Lai in partial fulfillment of a masters degree. Thanks to (1) Geoff Martin for Thinking Games website programming; (2) Florrie Ng and Qian Wang for comments on the early research design; (3) COGNITIVE PREDICTORS ACADEMIC SKILLS 2
Annabel Amodia-Bidakowska, Emma Chatzispyridou, Yiming Han, Katherine Parkin, Annie
Raff, Antonia Zachariou for data collection assistance; (4) Rosie Blunt, Hannah Bush,
Claudia Chu, Shehnaz Dowlet, Ellie Frank, Anton Evans, Tanya Paes, Nelly Hu-Kwo for scoring and data entry assistance and (5) Ric Parkin for proof reading. COGNITIVE PREDICTORS ACADEMIC SKILLS 3
Abstract
Executive functions and metacognition are two cognitive predictors with well- established connections to academic performance. Despite sharing several theoretical characteristics, their overlap or independence concerning multiple academic outcomes remain under-researched. To address this gap, the present study applies a latent-variable approach to test a novel theoretical model that delineates the structural links between executive functions, metacognition, and academic outcomes. In whole-class sessions, 455 children aged 9 to 14 years (M = 11.92; SD = 0.93) completed four computerized executive function tasks (cognitive flexibility, inhibition, working memory, and planning), a self-reported metacognitive monitoring questionnaire, a general cognitive ability test, and three standardized tests of academic ability. The results suggest that executive functions and metacognitive monitoring are not interchangeable in the educational context and that they have both shared and unique contributions to diverse academic outcomes. The findings are important for elucidating the role between two domain-general cognitive skills (executive functions and metacognition) and domain-specific academic skills.
Keywords
Executive functions, metacognition, open data, open materials, inhibition, working memory, cognitive flexibility, planning, metacognitive monitoring, reasoning, numeracy, literacy COGNITIVE PREDICTORS ACADEMIC SKILLS 4
Executive Functions and Metacognitive Monitoring Are Not Interchangeable in
Educational Settings: Shared and Unique Contributions to Academic Outcomes
Among the various cognitive abilities that help explain individual differences in academic outcomes, the constructs of executive functions and metacognition have attracted research interests from diverse disciplines. Situated within the self-regulation framework, processes such as goal-directedness, behavioral regulation, inhibition and flexibility are fundamental skills that predict academic and learning outcomes. Despite their relevance, however, theoretical and methodological concerns constrain conclusions regarding how executive functions and metacognition relate to each other and whether they show overlapping or interacting associations with academic outcomes.
Traditionally, executive functions and metacognition have been examined separately in different research traditions, but recent studies have begun to consider their theoretical and developmental similarities, as well as methodological differences (e.g., Roebers, 2017;
Roebers et al., 2009). Both executive functions and metacognition are considered domain- general, “multi-faceted” constructs (e.g., Friedman et al., 2008; Georghiades, 2004); both regulate and monitor lower-order processes in a top-down approach (e.g., Diamond, 2013;
Roebers & Feurer, 2016); both are relevant to learning, problem-solving, and self-regulation
(e.g., Diamond, 2013; Zimmerman 2011); and both are underpinned by similar neuro- mechanisms in the prefrontal cortex (e.g., Ardila, 2008; Fleming et al., 2012). Reflecting these commonalities, they are often considered as similar conceptual phenomena.
Empirically, however, their unique and shared contributions to academic performance have yet to be examined. Addressing this gap is important from both theoretical and practical perspectives. Theoretically, it is necessary to establish whether executive functions and metacognition are simply different terms for the same phenomenon in the education context.
Practically, understanding the independence or interplay of effects is helpful for designing executive functions or metacognition interventions, which currently have quite modest results
(e.g., Serpell & Esposito, 2016). COGNITIVE PREDICTORS ACADEMIC SKILLS 5
The interdisciplinary nature of studying executive functions and metacognition raises a methodological challenge as these cognitive constructs have different historical roots
(Fernandez-Duque et al., 2000). The concept of executive functions stems from neuro- psychological research that relies on performance-based tasks and experimental methods.
In contrast, metacognition, particularly procedural metacognition, is rooted in developmental/ educational psychological research that often utilizes school-related assessment methods within a classroom setting (Roebers, 2017). As such, examining both constructs in an educationally valid context might help identify the difference between their research philosophies. More specifically, does the different measurement methods most appropriate for each respective construct drive different theories? In this study, we apply an interdisciplinary approach by combining an experimental method to measure executive functions and an educational psychology method to assess metacognition. This approach can inform current theoretical and applied perspectives on the links between executive functions and metacognition as well as their individual contribution to educational outcomes
(Garner, 2009).
Executive Functions
Executive functions are multifaceted and encompass a set of self-regulatory processes that are key to controlling thoughts and behaviors in novel or challenging situations (Hughes et al., 2010). These include many higher-order cognitive abilities, such as having mental flexibility, suppressing irrelevant information and responses (inhibition), manipulating and updating information in mind (working memory), and thinking ahead (planning) (Chan et al.,
2008).
Numerous studies have linked executive function skills to academic success; indeed, individual differences in executive functions explain 20-60% of the variance in learner’s academic outcomes (Roebers, 2017). The predictiveness of executive functions on literacy and numeracy achievement appears consistent across many studies (e.g., Jacob &
Parkinson, 2015; Mulder et al., 2017; Schmitt et al., 2017). The association between executive functions and academic outcomes is strong across measurement tools, academic COGNITIVE PREDICTORS ACADEMIC SKILLS 6 subjects, and developmental stages (Blair & Razza, 2007; Blair et al., 2014). However, these links are not always consistent after controlling for other factors. For example, Schmitt et al.
(2017) found that executive functions were predictive of both literacy and numeracy skills even after controlling for age and socioeconomic status. In contrast, Jacob & Parkinson
(2015) did not find these links after age, socioeconomic status, and general cognitive ability are controlled.
In younger children, executive functions contribute between 5% to 36% of the variance in early academic outcomes (Roebers, 2017). By and large, executive function skills explain about 25% of the variance in numeracy skills and 16% of the variance in literacy skills in preschooler and kindergarteners (Roebers, 2017). However, given that different components of executive functions develop rapidly and become progressively distinct with age (e.g., Best
& Miller, 2010; Huizinga et al., 2006), the links with academic domains could vary between early childhood and late childhood. To date, however, fewer studies have explored these links in late childhood and early adolescence, a developmental stage where components of these constructs improved significantly (Pureza et al., 2013). It remains ambiguous whether these links are also observed in these age groups that are less well researched albeit mark an important stage of cognitive development. Ellefson et al. (2020) found strong links between executive functions and numeracy skills in older children (ages 9 to 14 years) after accounting for age, socioeconomic and general cognitive ability. Yet, whether these links are consistent for literacy are yet to be investigated.
Metacognition
Metacognition encompasses the knowledge and regulation of cognition. It involves reasoning about one’s thoughts and utilizes higher-order thinking skills to facilitate learning
(Flavell, 1976). While procedural metacognition concerns introspection regarding the regulation of active cognitive processes, declarative metacognition concerns knowledge about cognition or cognitive processes (Roebers & Feurer, 2016). As this article is education-relevant, we focus on procedural metacognition in the learning context because they are related to academic performance. COGNITIVE PREDICTORS ACADEMIC SKILLS 7
Procedural metacognitive monitoring skills help students form judgement of learning
(Am I learning what I should be learning?), evaluate strategy for tasks at hand (Can I find a way to better understand what I don’t understand?), as well as initiating adaptive plans for learning (Do I ask for help when I have trouble learning?). Hence, these skills are relevant in an educational context, particularly during late childhood and adolescence where metacognition questionnaires focus on learning in school. Indeed, they have been found to have a long-term influence on both literacy and numeracy skills (Vrugt & Oort, 2008).
Furthermore, a meta-analysis conducted by Dignath et al. (2008) with 6- to 12-year-olds indicated that training these skills lead to improved performance in tests of literacy, numeracy, and science. However, the influence of metacognition may become attenuated across development. Vukman and Licardo (2010) found that metacognition explains 34% of variance of academic performance for primary school, as compared with 21% in secondary school, and just 14% in higher education. Indeed, it has been argued that procedural metacognition is fairly unstable as it is age-, task- and situation-dependent (Dunlosky &
Bjork, 2008). This conceptualization might contrast with the consistency of executive functions as a strong predictor of early and late academic outcomes (Bull & Lee, 2014;
Mazzocco & Kover, 2007). Hence, despite sharing conceptual characteristics, the roles of executive functions and metacognition might be distinguishable in educational contexts at certain developmental stages (i.e., late childhood) with differing effects.
Differing effects may be particularly evident when a specific component of metacognition is examined. While metacognitive monitoring operates from a bottom-up approach, metacognitive control works from a top-down approach. Hence, even within the construct of metacognition, control and monitoring may share different relations with executive functions. The majority of previous studies that investigated the relation between executive functions and metacognition have focused on the control processes at the task- level. Given this, less is known regarding the relations between executive functions and monitoring as well as its specific associations with diverse learning outcomes. Just as the effects of these constructs on outcomes change across different developmental stages, the COGNITIVE PREDICTORS ACADEMIC SKILLS 8 nature of the link between these the constructs might also change over the course of development.
Executive Functions, Metacognition, and Academic Outcomes
A paucity of studies investigates the link between executive functions and metacognition, especially when narrowed to executive functions, metacognitive monitoring, and academic outcomes. However, three different perspectives could be postulated regarding the contributions of executive functions and metacognition (in general) on diverse academic outcomes. First, if executive functions and metacognition share a common underlying mechanism (i.e., controlling), then they are likely to predict the same set of outcomes with similar strengths and explain similar variances (Borkowski & Burke, 1996;
Fernandez-Duque et al., 2000; Shimamura, 2000; Souchay & Isingrini 2004). However, even for academic outcomes that are predicted by both constructs (e.g., literacy in Roebers et al.,
2012), a finer-grained analysis may show contrasting strengths of association for specific aspects (e.g., vocabulary knowledge, reading comprehension, and phonological awareness).
Second, executive functions and metacognition may predict different sets of academic outcomes, with each having a distinct role in the same context. Against this view, however, previous studies have indicated that both constructs predict literacy and numeracy performance to a significant extent albeit with different measures used in relation to each construct. Third, and perhaps most plausibly, executive functions and metacognition may demonstrate both overlapping and unique contributions (Roebers et al., 2012), with differing explained varainces on certain academic outcomes. The latter view constitutes the basis of this study.
Generally, evidence for the contribution of executive functions and metacognition to numeracy is extensive and robust (Cortés Pascual et al., 2019). Findings regarding the links from executive functions and metacognition to literacy have been mixed, with recent findings suggesting executive functions could be more strongly related to numeracy than literacy skills (Blair & Razza, 2007; Schmitt et al., 2017). However, both constructs still appear to contribute to literacy. Although research regarding executive functions and metacognition COGNITIVE PREDICTORS ACADEMIC SKILLS 9 processes involved in learning has been motivated from an educational perspective, less is known about the mechanisms of these processes in other areas such as reasoning skills
(Ackerman & Thompson, 2017). Considering this, the current study extends from two well- studied outcomes by including word reasoning to investigate the mechanisms of the contribution of executive functions and metacognition in a boarder educational context.
The Current Study
The overarching aim of the present study is two-fold. Firstly, we extend previous work looking at the shared and unique contributions of executive functions and metacognition in young children to older children and adolescents. We do this by investigating the link between executive functions and procedural metacognitive monitoring with 9- to 14-year- olds. Secondly, controlling for general cognitive ability, we delineate the shared and unique contributions of executive functions and procedural metacognitive monitoring to three academic areas: word knowledge, word reasoning, and numeracy. In so doing, this study uniquely contributes to the literature by: (1) extending the developmental scope of previous research to include late childhood and early adolescence (2) adopting a dual focus on executive functions and procedural metacognitive monitoring as predictors of diverse academic outcomes within a large and representative sample of children; and (3) using the state-of-the-art measurement methods from experimental psychology (computerized executive function tasks) and from education (metacognitive monitoring self-report) for this interdisciplinary study. COGNITIVE PREDICTORS ACADEMIC SKILLS 10
Method
Participants
The sample is part of a larger cross-cultural data set, including 930 children from the
United Kingdom and Hong Kong (Ellefson et al., 2017). The participants were recruited from community centers, state and private schools in the United Kingdom. As a recompense for their time, each family received £20 and each child was given a small prize.
In this study, we focus only on the British cohort (n = 546) because the metacognitive monitoring questionnaire and word reasoning tasks in the full project were translated into
Cantonese for the Hong Kong sample, which could have created confounds for comparing across sites for this analysis. Analyses were conducted on participants who met all of the following selection criteria: (1) completed a metacognitive monitoring questionnaire; (2) completed at least one of the four executive function tasks; and (3) completed at least one of the four academic outcome measures. In total, 455 participants were included in the final analyses, with ages ranging from 9.28 to 14.87 years (Mage = 11.92; SDage = 0.93) and approximately equal numbers of males (n = 232; 49.9%) and females (n = 223; 48.8%, excluding 6 non-responses for gender). Within this final data set, every participant completed all of the academic outcome measures and only eight participants were missing any function measures (n = 1 missing two tasks; n = 7 missing one task).
Design and Procedures
Across one to two sessions, participants in this study completed four computerized executive function tasks, one metacognitive monitoring questionnaire, and four standardized pen-and-pencil tests of academic achievement (see Ellefson et al., 2017).
Most participants completed the tasks at school and others completed them at community locations (library) or the university laboratory. Breaks between tasks were encouraged and multiple sessions were offered to accommodate school schedules. Prior to the study, ethical approval was granted by the University ethics committee and informed written consent was received from parents before their children’s participation.
Materials COGNITIVE PREDICTORS ACADEMIC SKILLS 11
Executive Function Skills
The four tasks used to measure executive functions were computer-based tasks administered securely through the Thinking Games website as described in Ellefson et al.
(2017; open-access materials from http://reshare.ukdataservice.ac.uk/852658/. In all tasks, participants were instructed to complete the tasks as quickly as possible while still being correct. Accuracy and response times were collected for each trial in these tasks.
Cognitive Flexibility was measured using a child-friendly task-switching paradigm
(figure matching by Ellefson et al., 2006) adapted from Rogers and Monsell (1995). In this task, participants saw a bigger object in the center of the screen and two smaller objects at the bottom left- and right-hand side of the screen. The objects were either a triangle or circle in the color blue or red. Following an instruction at the top of the screen, participants selected one of the small objects that matched the big object using a button press.
Sometimes that instruction was to match base on shape, other times based on color. There was a total of 128 trials, presented across four sets of 32 trials each. One set of trials included all color, another all shape. The other two sets of trials alternated between the two in a color-color-shape-shape-color-color-shape-shape pattern.
Inhibition was measured using a simplified and child-friendly version of the Stop-Signal task (Logan, 1994). It started with an image of a soccer field with a ball positioned in the middle of the left and right side of the field. There was a total of 108 trials presented across three blocks of 36 trials each. The participant’s goal was to click on the left side of the field when the ball lands on the left side (54 trials) and click on the right side of the field (54 trials) when the ball landed on the right side. In addition, participants were instructed to refrain from pressing any keys when the sound of a whistle played (occurring for 20% of trails). The gap between the picture of the ball appearing and the whistle playing was faster or slower based participants’ accuracy during the previous inhibition trial. When inhibition was successful, then the whistle was played 50 msec later for the next inhibition trial. If inhibition was unsuccessful, then the whistle was played 50 msec sooner. COGNITIVE PREDICTORS ACADEMIC SKILLS 12
Working Memory was measured using a modified version of the Corsi Blocks Tasks
(Milner, 1971) that are appropriate for children. There are two parts: forwards and backwards. However, the forwards part of the task measures short-term memory more accurately than working memory (Richardson, 2007). Therefore, only backwards trials were considered here. In each trial, participants saw 9 boxes displayed on the screen. The boxes were highlighted in random order. The participants’ goal was to click on the boxes in the reverse order that they saw them light up. The length of the sequence increased after the first two practice trials as well as with successful recall. In total, there were 12 backward sequences, excluding the practice trials. If participants made 5 consecutive errors, the task automatically stopped.
Planning was measured using a computerized version of the Tower of Hanoi task from
Welsh (1991). There were two arrangements of disks on the screen in this task. Participants were asked to arrange the disks in the bottom to match the disks on the top efficiently and accurately. With each successful solution, the minimum number of moves and the difficulty increased. When participants wrongly placed a larger disk on a smaller disk, a reminder was given that the move was not allowed, the disk was returned to its location, and the erroneous move was recorded. Participants had a maximum of 20 moves to achieve the solution in each problem. Six attempts were given to achieve the two consecutive minimum move solutions before moving onto a more difficult configuration. The task ended when participants were unable to solve two consecutive problems within six attempts for a given configuration or when they had successfully solved all eight configurations.
Metacognitive Monitoring Practices
Metacognitive monitoring practices were measured by the Self-Regulated Learning
Scale based on the Goal Orientation and Learning Strategies Survey (Dowson & McInerney,
2004). This questionnaire includes 84 items that assess procedural metacognitive monitoring with the focus on academic goals and learning strategies in an educational context based on a five-point Likert-scale (1 = not true at all; 5 = very true). For example, “I check to see if I understand the things I am trying to learn”. To avoid participant fatigue, improve the COGNITIVE PREDICTORS ACADEMIC SKILLS 13 efficiency of the study, and minimize high correlations between items, we used an abridged set of 18 items to measure learning judgement, learning strategies, and learning initiation from Wang and Pomerantz (2009).
Academic Outcome Measures
Word reasoning was measured with the word reasoning subtest of the Wechsler
Intelligence Scale for children, 4th Edition (WISC-IV; Wechsler, 2004). This consists of 24 items that require participants to identify the concept described to them with clues. To answer correctly, participants needed to reason based on contextual clues and have developed vocabularies. Devine and Hughes (2013) administered this task in a group setting.
Word knowledge was measured using the McGraw Hill Vocabulary component of the
Ravens Progressive Matrices (Court & Raven, 1995). It consists of a total of 38 multiple- choice items, measuring vocabulary knowledge. For each target word, participants selected the best synonym from a list of six options.
Numeracy skills were measured using the Wide Range Achievement Test-Three
Edition (WRAT-3; Wilkinson, 1993). The items evaluated basic arithmetic calculations: addition, subtraction, multiplication, division as well as fractions, decimals, and algebra; it can be administered in a group setting (Snelbaker et al., 2001).
General Cognitive Ability
Overall general cognitive ability was measured using the Ravens Progressive
Standard Matrices (Raven et al.,1998). It is a standardized general cognitive ability test that measures non-verbal reasoning or one’s capacity for logical thinking (Jaarsveld et al., 2010).
It comprises problems in which a pattern is shown with a missing piece. Participants select from a set of options the piece they think best fits into the overall pattern. It is straightforward to administer this task in a group setting.
Data Processing and Analyses
The dataset and R scripts used for the analyses are openly available at https://osfi.io/r9cnu/. COGNITIVE PREDICTORS ACADEMIC SKILLS 14
To identify the structure of the Self-Regulated Learning Scale used to measure metacognitive monitoring, a principal component analysis (PCA) was conducted with orthogonal rotation (promax) (KMO = .93; Bartlett’s test of sphericity, χ2 (153) = 3431.12, p <
.001). Three factors were extracted and were identified as learning judgement (α = .83; e.g.,
“I often check to see if I understand what I have read”), learning strategies (α = .84; e.g.,
“When I want to learn things for school, I pick out the most important parts first”), and learning initiation (α= .80; e.g., “If I have trouble learning something, I ask for help”).
In this study, an efficiency score was computed by combining accuracy and response time measures. The efficiency score is a metric of task performance that accounts for speed- accuracy and trade-offs and the ceiling effects on accuracy that are associated with the type of executive function measures used here (e.g., Ellefson et al., 2017). A speed-accuracy trade-off occurs when participants have longer reaction times in order to get accurate responses, or vice versa (Liu & Watanabe, 2012). Davidson et al., (2006) found that these trade-offs are not the same across ages with a sample from 4 to 14 years. Isquith et al.
(2005) reported the reliabilty ofefficiency scores for executive function measures in children.
A number of subsequent executive function studies involving children and/or adults have also used efficiency metrics (e.g., Ellefson et al., 2017; 2020; Kennet et al., 2001; Wiebe et al., 2008). Here, there were statistically significant positive correlations between accuracy and response time to correct trials for all four executive function tasks: inhibition (r = .16 p
< .001), working memory (r = .16 p < .001), cognitive flexibility (r = .14 p = .002) and planning (r = .70 p < .001). As such, we used the efficiency score below to represent performance in the executive function tasks:
Totalnumber correct Efficiency= correct trials ¿ Meanreactiontime ¿
We converted the full data set to z scores and adopted procedures used by Ellefson et al. (2017) and Wiebe et al. (2008) to generate age-adjusted executive functions and COGNITIVE PREDICTORS ACADEMIC SKILLS 15 metacognitive monitoring variables. Those procedures included three main elements. First, a linear hierarchical regression was used to predict executive function and metacognitive monitoring scores from age: age (model one). age2 (model two), and age3 (model three).
Second, the age-adjusted scores were residualized on the best-fitting age terms and converted to z scores. Third, a correlation matrix was used to confirm the lack of associations between age, age-adjusted executive function and metacognitive monitoring variables. For missing data, a mean imputation was used to replace missing values.
Results
Descriptive Statistics and Correlation Matrix
Table 1 includes the descriptive statistics (mean and standard deviations) and zero- order correlations among all variables measured in raw scores (except for factor scores).
The results suggest that all executive function components are interrelated, and similar results for metacognitive monitoring. COGNITIVE PREDICTORS ACADEMIC SKILLS
Table 1.
The means, standard deviations, and zero-order correlations for all variables
Variable M SD 1 2 3 4 5 6 7 8 9 10 Executive Functions 1. Flexibility 95.66 26.45 2. Working Memory 2.94 1.00 .40*** 3. Inhibition 109.35 43.60 .39*** .22*** 4. Planning 1.24 0.45 .30*** .39*** .11** Metacognitive Monitoring 5. Learning Judgement 0.00 1.00 -.05 -.05 .12 -.08 6. Learning Strategies 0.00 1.00 -.06 .04 -.01 -.03 .66*** 7. Learning Initiation 0.00 1.00 -.09 -.03 -.02 -.05 .53*** .48*** Academic Outcomes 8. Word Reasoning 16.46 3.21 .30*** .35*** .14** .23*** .02 .07 .01 9. Word Knowledge 19.58 4.89 .28*** .35*** .14** .20*** .03 .02 .01 .57*** 10. Numeracy 34.40 5.85 .38*** .34*** .20*** .23*** .13* .08 .09 .52*** .51*** General Cognitive Ability 11. Ravens 40.05 9.24 .38*** .31*** .21*** .21*** .03 -.07 -.02 .35*** .36*** .62***
Notes. Variables 1-4 are raw efficiency scores (untransformed, unstandardized); variables 5-7 are factor scores (necessarily standardized); variables 8-11 are raw accuracy scores (untransformed, unstandardized). The standardized scores are used in subsequent analyses only. *p < .05; **p < .01, ***p < .001. COGNITIVE PREDICTORS ACADEMIC SKILLS
The Measurement Model
A two-factor model of executive functions and metacognitive monitoring was hypothesized and tested using confirmatory factor analysis (CFA) with the lavaan package
(Rosseel, 2012) in R (R core team, 2019). The resulting model is shown in Figure 1 and
Table 2. The CFA model achieves a good model fit, χ2(13) = 23.84, p < .05.
Figure 1
The CFA Measurement Model
Notes. The single headed arrows represent the factor loadings for each measured variable. The double headed arrow represents the correlation between the two latent variables. COGNITIVE PREDICTORS ACADEMIC SKILLS
Table 2
The CFA Measurement Model: Confidence Intervals, Path coefficients, and Goodness of Fit
Indices
Confidence Intervals
Factor Loadings Path Coefficient Upper bound Lower bound
Executive Functions:
Flexibility .74*** 1.00 1.00
Inhibition .53*** .50 .92
Working Memory .50*** .47 .87
Planning .35*** .30 .64
Metacognitive Monitoring:
Learning Judgement .83*** 1.00 1.00
Learning Strategy .78*** .79 1.09
Learning Initiation .59*** .59 .84
Notes. All Goodness-of-fit indices are within the acceptable values: 2 = 23.84; df =13; Comparative Fit Index (CFI = .98); Normal fit index (NFI = .96); Tucker-Lewis index (TLI = .97); Akaike Information Criterion (AIC = 8436); Bayesian Information Criterion (BIC = 8498); Root Mean Square Error of Approximation (RMSEA = .04); Standardized Root Mean Square Residual (SRMSR = .03). COGNITIVE PREDICTORS ACADEMIC SKILLS
The Structural Model
A theoretical model was tested to elucidate the contribution of executive functions and metacognitive monitoring to different academic outcomes with general cognitive ability being controlled for (Figure 2). Partialling out the variance from general cognitive ability allows for a closer establishment of the unique variance each construct contributes to academic outcomes.
Other than the academic outcomes, residuals between flexibility and inhibition as well as working memory and planning are also correlated. The first set of correlated residuals are based on (Ellefson et al., 2020; Xu et al., 2020); the latter set is based on the substantial amount of studies suggesting the role of working memory in planning tasks (e.g., Tower of
Hanoi, Baughman & Cooper 2007; Goela et al., 2001; Numminen et al., 2001;). There are two reasons to which the residuals of the indicators of metacognitive monitoring were not correlated. First, the indicators of the latent metacognitive monitoring are factors scores derived from PCA. An assumption that underlies PCA is that only the common variance rather than unique variance makes up the total variance, hence residuals of the factors scores are not correlated (Preacher & MacCallum, 2003). Second, unlike executive functions or standardized academic outcomes which are measured by individual tasks, metacognitive monitoring was measured using a single questionnaire that has three distinctive subscales.
In this instance, correlating their residuals might not be as relevant as the latent executive functions variable but could be problematic to the model.
As illustrated by Table 3, the model yielded excellent model-fit (2(31) = 35.31, p = .27;
CFI = .99; NFI = .97; TLI = .99; RMSEA = .02; SRMR = .03). executive functions and metacognitive monitoring do not share any significant relationship (r = -.05, p = .38). The latent executive functions variable significantly and positively predicted all three academic outcomes: word knowledge (β = .29, p < .001), word reasoning (β = .33, p < .001), and numeracy (β = .31, p < .001). On the other hand, latent metacognitive monitoring only significantly and positively predicted numeracy (β = .15, p < .001). The results suggest that COGNITIVE PREDICTORS ACADEMIC SKILLS both executive functions and metacognitive monitoring predicted numeracy and executive functions have a unique positive contribution to word knowledge and word reasoning.
To evaluate further the effects of age, we used the age median (11.91) to split the full data into a younger and older age group. We ran two additional full SEM models and found the same pattern of results. As such, only results from the full dataset are presented here.
Figure 2
The Full SEM Model: Unique and Shared Contributions of Executive Functions and
Metacognitive Monitoring to Academic Outcomes
Notes. Solid lines indicate statistically significant links and dashed lines indicate non-
significant estimates. COGNITIVE PREDICTORS ACADEMIC SKILLS
Table 3
The Measurement Model: Confidence Intervals, Path coefficients, and Goodness of Fit
Indices
Confidence Intervals
Latent Variable Links Path Coefficient Upper bound Lower bound
General Cognitive Ability to
Executive Functions .51*** .24 .40
Metacognitive Monitoring -.02 -.10 .07
Executive Functions to
Word Reasoning .33*** .28 .77
Word Knowledge .29*** .20 .72
Numeracy .31*** .27 .71
Metacognitive Monitoring to
Word Reasoning .09 -.004 .22
Word Knowledge .07. -.03 .20
Numeracy .15*** .09 .28
General Cognitive Ability to
Word Reasoning .17*** .05 .28
Word Knowledge .17*** .04 .29
Numeracy .40*** .31 .50
Notes. Goodness of Fit Indices: 2 = 38.76; df =30; Comparative Fit Index (CFA = .99); Normal fit index (NFI = .97); Tucker-Lewis index (TLI = .99); Akaike Information Criterion (AIC = 11762); Bayesian Information Criterion (BIC = 11906); Root Mean Square Error of Approximation (RMSEA = .03); Standardized Root Mean Square Residual (SRMSR = .03). COGNITIVE PREDICTORS ACADEMIC SKILLS
Discussion
This study is one of the first to apply SEM to examine whether executive functions and metacognitive monitoring have unique roles as predictors of educational outcomes in older children and young adolescents. Two main findings emerged from the sample in this study:
(1) executive functions and self-reported metacognitive monitoring do not share a significant relationship; and (2) despite common associations with numeracy, executive functions have a unique positive role in word knowledge and word reasoning.
Executive Functions and Metacognitive Monitoring Links
Our first research question concerned the relationship between executive functions and metacognitive monitoring. As noted earlier, the existing literature on this topic has focused on younger school-aged children. Extending the developmental scope of this work, we examined executive functions and metacognitive monitoring in children aged 9 to 14 years. In contrast to previous studies conducted in younger age groups (García et al., 2016;
Roebers et al., 2012), our results did not show any statistically significant relation between the latent measure of executive functions and metacognitive monitoring. Although more empirical evidence is needed, this finding reinforces the dynamic nature of the relationship between the constructs, which undergoes changes and evolution across different developmental stages. For example, Bryce et al. (2015) found a stronger association between executive functions and metacognition in 5 year-olds than in 7- year-olds. On the other hand, Roebers et al. (2012) found no association between latent executive functions and latent metacognitive monitoring in 8-year-old children. It is plausible that despite sharing conceptual similarities, the relation of these constructs are inconstant over the course of development. As each construct undergoes its own growth at different stage, the relationship between executive functions and metacognitive monitoring may also evolve as suggested by some evidence in younger age groups mentioned above. In this regard, the current finding extends theories based on extensive theoretical work on preschoolers to less-integrated work on older children, thereby contributing to a developmental account of the complex relation and chronological evolution between executive functions and metacognition. Other COGNITIVE PREDICTORS ACADEMIC SKILLS than a developmental explanation, the finding in this study may also be attributed to both methodological and theoretical constraints.
Methodological Constraints
The results may be due to contrasting methods to measure executive functions and metacognitive monitoring. This study integrated the most appropriate measurement methods for each construct in their respective field while considering the constraints of data collection with a large sample size. However, this approach could have compromised their comparability. That is, while computerized executive functions tasks and self-reported metacognitive monitoring questionnaires might provide well-accepted measure for each construct, they necessitate different response modalities (non-verbal/ external vs. verbal/ internal). For example, García et al. (2016) relied on family and teacher reports of executive functions and self-reported metacognition questionnaires while Roebers et al. (2012) used a mix of performance-based and computerized tasks for both executive functions and metacognition. These authors used measures that tap similar modalities and found statistically significant relationships between executive functions and metacognition in elementary school students.
Other than providing a plausible explanation for the lack of link we found in this study, the contrast between measurement methods raises an interesting methodological question: how to compensate for the different measurement methods of executive functions and metacognition as well as the impact this issue has on findings. Investigations of the relationship between executive functions and metacognition are still scarce. As more experimental psychologists share the interest to understand executive functions and metacognition in the educational context, evaluating the impact of measurement tools might be a promising avenue for future research.
Although not explicitly related to the relation between executive functions and metacognition, support for the importance of contrasting methodology comes from a study by Williams et al. (2017) that suggests self-reported attentional control and behavioral measurements of related cognitive abilities are largely unrelated in young adults. It could be COGNITIVE PREDICTORS ACADEMIC SKILLS the case that bias in self-reported questionnaires may have a larger effect on older children and young adolescents due to overconfidence in the accuracy of their responses and overestimation in their self-perceptions (Shin et al., 2007). Nevertheless, further studies involving parallel measures of assessment are needed to fully explore the nature of the links between executive functions and metacognitive monitoring. For example, it would be useful to include questionnaire measures like the BRIEF (Behavioral Rating Inventory of Executive
Function, Gioia et al., 2000) to measure executive functions as well as recently developed computerized tests of metacognitive monitoring (Veenman et al., 2014).
Theoretical Constraints
Theoretically, executive functions and metacognition are generally considered to be closely associated. However, our findings suggest that this relation may vary according to the exact metacognition component considered. Recent findings have suggested that executive functions and procedural metacognitive control, rather than monitoring share the regulation of cognitive processes (Roebers & Feurer, 2016). This may explain the lack of association between executive functions and metacognitive monitoring in this study. For example, Roebers et al. (2012) found that latent executive functions in 8-year-olds was associated with metacognitive control but not with monitoring. They explain that while monitoring works at the micro-level, both metacognitive control and executive functions share the executive nature and work at the macro-level (top-down) in regulating processes.
The executive function tasks in this study required active response inhibition processes which might engage the control processes (at the object level) of metacognition more so than the more passive and introspective processes of monitoring (Bryce et al., 2015).
Although the majority of previous work suggests a general association between executive functions and metacognition, the different measurement methods and the specific processes studied make results difficult to compare. To better understand their links, future studies should investigate both metacognitive control and monitoring alongside executive functions with various measurement methods.
Executive Functions, Metacognitive Monitoring, and Academic Outcomes COGNITIVE PREDICTORS ACADEMIC SKILLS
Overall, our findings confirm the links between a variety of academic outcomes with both executive functions and metacognitive monitoring. Our results suggest that, accounting for general cognitive ability, both constructs have a shared contribution to numeracy performance with executive functions having a unique contribution to word knowledge and reasoning. Aligning with previous findings and conclusions (e.g., Bryce et al., 2015; Garner,
2009), executive functions and metacognitive monitoring don’t appear to be identical in the educational context.
Shared Contributions
Previous studies of toddlers and younger children provide extensive evidence that the relationship between executive functions and numeracy may be stronger than that amongst executive functions and other academic outcomes (Cragg & Gilmore, 2014; Mulder et al.,
2017; Schmitt et al., 2017). We did not find this pattern in older children and adolescents
(ages 9 to 14 years). Instead, the strength of the links between executive functions and academic outcomes appears consistent.
The link between metacognition and numeracy is equally robust in the literature, particular in intervention studies (de Boer et al., 2018; Kramarski et al., 2002). However, a recent meta-analysis of metacognition intervention for numeracy reveals most of these studies focus on metacognition strategies/ strategy instructions and mathematical problem- solving (e.g., Babakhani, 2011; Desoete et al., 2003; Maqsud, 1998). These metacognition strategies involve monitoring and reflecting on learning process, which is how metacognitive monitoring is considered in this study (reflections on self-monitoring and strategy formulation). Although not an intervention study by nature, our findings align with previous results and extend to standardized mathematical achievement test.
Unique Contributions
Concerning unique contributions, executive function skills positively contribute to both word knowledge and word reasoning. The present findings further support the domain- general nature of executive functions in learning. Previous research has delineated these associations in a componential approach. For example, inhibition and working memory have COGNITIVE PREDICTORS ACADEMIC SKILLS been consistent predictors of reading and mathematics (Blair & Razza, 2007; Bull & Scerif,
2001; St Clair-Thompson & Gathercole, 2006). As a latent variable approach was adopted in this study, we were not able to distinguish the specific links between executive function components and the outcomes we tested. However, the present study aimed to be more comprehensive in our outcome measurement to including word reasoning. This link contributes to the body of work that demonstrates the domain-general executive function skills in academic settings and aligns with the relationship observed in ages 9-12 in van der
Sluis et al., (2007). Less expected is the lack of associations between metacognitive monitoring and word knowledge/reasoning. Even though it is a domain-general construct like
EF, metacognitive monitoring was not found to associate with these two outcomes. It may be due to the specific self-reported questionnaire of metacognitive monitoring used in the study, which measures monitoring from a learning perspective. While word reasoning may underpin many academic skills, it is not by itself a subject area and so might not depend upon self- reported metacognitive monitoring in the learning context (e.g., exam strategies and setting learning goals). Lastly, the lack of associations with receptive word knowledge may suggest the larger roles of other predictors, such as phonological short-term memory and semantic long-term memory (Gathercole et al., 1999; Masoura & Gathercole, 2005). In the same regard, metacognitive monitoring may be more useful during the process of learning rather than retrieving learned words. Support for this view comes from Goh and Hu (2014), who found a significant negative correlation between metacognition and learners’ overall word knowledge. Thus, future studies could look beyond the retention of words to focus on other areas such as acquisition of new words.
Conclusion
Empirical evidence on the association between executive functions, metacognition and academic outcomes are well-established. However, the question regarding whether these two domain-general constructs are interchangeable in the educational contexts remains equivocal. Integrating executive functions and metacognitive monitoring in the same model enable the elucidation of their individual impact, thereby providing theoretical and conceptual COGNITIVE PREDICTORS ACADEMIC SKILLS clarity to their roles as academic predictors. By understanding their contribution to three different educational outcomes (word knowledge, word reasoning, and numeracy), our study provides a finer-grained picture of the processes underpinning variation in academic performance beyond general cognitive ability. It is hoped that our findings are helpful for future intervention studies to foster learning skills across late childhood and early adolescence. COGNITIVE PREDICTORS ACADEMIC SKILLS
References
Ackerman, R., & Thompson, V. A. (2017). Meta-reasoning: Monitoring and control of
thinking and reasoning. Trends in Cognitive Sciences, 21, 607-617.
https://doi.org/10.1016/j.tics.2017.05.004
Ardila, A. (2008). On the evolutionary origins of executive functions. Brain and Cognition, 68,
92-99. https://doi.org/10.1016/j.bandc.2008.03.003
Babakhani, N. (2011). The effect of teaching the cognitive and meta-cognitive strategies
(self-instruction procedure) on verbal math problem-solving performance of primary
school students with verbal problem-solving difficulties. Procedia - Social and
Behavioral Sciences, 15, 563-570. https://doi.org/10.1016/j.sbspro.2011.03.142
Baughman, F., & Cooper, R. P. (2007). Inhibition and young children's performance on the
Tower of London task. Cognitive Systems Research, 8, 216-226.
https://doi.org/10.1016/j.cogsys.2007.06.004
Best, J. R., & Miller, P. H. (2010). A developmental perspective on executive function. Child
Development, 81, 1641-1660. https://doi.org/10.1111/j.1467-8624.2010.01499.x
Blair, C., Raver, C. C., & Berry, D. J., & the Family Life Project Investigators. (2014). Two
approaches to estimating the effect of parenting on the development of executive
function in early childhood. Developmental Psychology, 50, 554-565.
https://doi.org/10.1037/a0033647
Blair, C., & Razza, R. P. (2007). Relating effortful control, executive function, and false belief
understanding to emerging math and literacy ability in kindergarten. Child
Development, 78, 647-663. https://doi.org/10.1111/j.1467-8624.2007.01019.x
Borkowski, J. G., & Burke, J. E. (1996). Theories, models, and measurements of executive
functioning: An information processing perspective. In G. R. Lyon & N. A. Krasnegor
(Eds.), Attention, memory, and executive function (pp. 235-261). Paul Brookes.
Bryce, D., Whitebread, D., & Szcs, D. (2015). The relationships among executive functions,
metacognitive skills and educational achievement in 5 and 7 year-old children. COGNITIVE PREDICTORS ACADEMIC SKILLS
Metacognition and Learning, 10, 181-198. http://dx.doi.org/10.1007/s11409-014-9120-
4
Bull, R., & Lee, K. (2014). Executive functioning and mathematics achievement. Child
Development Perspectives, 8, 36-41. https://doi.org/10.1111/cdep.12059
Bull, R., & Scerif, G. (2001). Executive functioning as a predictor of children’s mathematics
ability: Inhibition, switching, and working memory. Developmental Neuropsychology,
19, 273-293. https://doi.org/10.1207/S15326942DN1903_3
Chan, R. C .K., Shum, D., Toulopoulou, T., & Chen, E. Y. H. (2008). Assessment of
executive functions: review of instruments and identification of critical issues.
Archives of Clinical Neuropsychology, 23, 201-216.
https://doi.org/10.1016/j.acn.2007.08.010
Cortés Pascual, A., Moyano Munoz, N., & Quilez Robres, A. (2019). The relationship
between executive functions and academic performance in primary education:
Review and meta-analysis. Frontiers in Psychology, 10, 1582.
https://doi.org/10.3389/fpsyg.2019.01582
Court, J. H., & Raven, J. (1995). Manual for Raven’s Progressive Matrices and Vocabulary
Scales. Harcourt Assessment.
Cragg, L., & Gilmore, C. (2014). Skills underlying mathematics: The role of executive
function in the development of mathematics proficiency. Trends in Neuroscience and
Education, 3, 63-68. https://doi.org/10.1016/j.tine.2013.12.001
Davidson, M. C., Amso, D., Anderson, L. C., & Diamond, A. (2006). Development of
cognitive control and executive functions from 4 to 13 years: Evidence from
manipulations of memory, inhibition, and task switching. Neuropsychologia, 44, 2037-
2078. https://doi.org/10.1016/j.neuropsychologia.2006.02.006
Desoete, A., Roeyers, H., & De Clercq, A. (2003). Can offline metacognition enhance
mathematical problem solving? Journal of Educational Psychology, 95, 188-200.
https://doi.org/10.1037/0022-0663.95.1.188 COGNITIVE PREDICTORS ACADEMIC SKILLS
de Boer, H., Donker, A. S., Kostons, D. D., & van der Werf, G. P. (2018). Long-term effects
of metacognitive strategy instruction on student academic performance: A meta-
analysis. Educational Research Review, 24, 98-115.
https://doi.org/10.1016/j.edurev.2018.03.002
Devine, R. T., & Hughes, C. (2013). Silent films and strange stories: Theory of mind, gender,
and social experiences in middle childhood. Child Development, 84, 989-1003. https://
doi.org/10.1111/cdev.12017
Diamond, A. (2013). Executive functions. Annual Review of Psychology, 64, 135-168.
https://doi.org/10.1146/annurev-psych-113011-143750
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, 101-129.
https://doi.org/10.1016/j.edurev.2008.02.003
Dowson, M., & McInerney, D. (2004). The development and validation of the goal orientation
and learning strategies survey (GOAL-S). Educational and Psychological
Measurement, 64, 290-310. https://doi.org/10.1177/0013164403251335
Dunlosky, J., & Bjork, R. A. (2008). The integrated nature of metamemory and memory. In J.
Dunlosky & R. A. Bjork (Eds.), Handbook of metamemory and memory (p. 11-28).
Psychology Press.
Ellefson, M. R., Zachariou, A., Ng, F. F.-Y, Wang, Q., & Hughes, C. (2020). Do executive
functions mediate the link between socioeconomic status and numeracy skills? A
cross-site comparison of Hong Kong and the United Kingdom. Journal of Experimental
Child Psychology, 194, 104734. https://doi.org/10.1016/j.jecp.2019.104734
Ellefson, M. R., Ng, F. F.-Y., Wang, Q., & Hughes, C. (2017). Efficiency of executive
function: A two-generation cross-cultural comparison of samples from Hong Kong and
the United Kingdom. Psychological Science, 28, 555-566.
https://doi.org/10.1177/0956797616687812 COGNITIVE PREDICTORS ACADEMIC SKILLS
Ellefson, M. R., Shapiro, L. R., & Chater, N. (2006). Asymmetrical switch costs in children.
Cognitive Development, 21, 108-130. https://doi.org/10.1016/j.cogdev.2006.01.002
Fernandez-Duque, D., Baird, J. A., & Posner, M. I. (2000). Executive attention and
metacognitive regulation. Consciousness and Cognition, 9, 288-307.
https://doi.org/10.1006/ccog.2000.0447
Flavell, J. H. (1976). Metacognitive aspects of problem solving. In L. B. Resnick (Ed.), The
nature of intelligence (pp. 231-235). Lawrence Erlbaum.
Fleming, S. M., Huijgen, J., & Dolan, R. J. (2012). Prefrontal contributions to metacognition
in perceptual decision making. The Journal of Neuroscience, 32, 6117-6125.
https://doi.org/10.1523/JNEUROSCI.6489-11.2012
Friedman, N. P., Miyake, A., Young, S. E., DeFries, J. C., Corley, R. P., & Hewitt, J. K.
(2008). Individual differences in executive functions are almost entirely genetic in
origin. Journal of Experimental Psychology: General, 137, 201-225.
https://doi.org/10.1037/0096-3445.137.2.201
Gathercole, S. E., Service, E., Hitch, G. J., Adams, A., & Martin, A. J. (1999). Phonological
short-term memory and vocabulary development: Further evidence on the nature of
the relationship. Applied Cognitive Psychology, 13, 65-77.
https://doi.org/10.1002/(SICI)1099-0720(199902)13:1<65::AID-ACP548>3.0.CO;2-O
García, T., Rodríguez, C., González-Castro, P., Álvarez-García, D., & González-Pienda, J.-
A. (2016). Metacognition and executive functioning in elementary school. Anales De
Psicología / Annals of Psychology, 32, 474-483.
https://doi.org/10.6018/analesps.32.2.202891
Garner, J. K. (2009). Conceptualizing the relations between executive functions and self-
regulated learning. Journal of Psychology: Interdisciplinary and Applied, 143, 405-
426 https://doi.org/10.3200/JRLP.143.4.405-426
Georghiades, P. (2004). From the general to the situated: Three decades of metacognition.
International Journal of Science Education, 26, 365-383.
https://doi.org/10.1080/0950069032000119401 COGNITIVE PREDICTORS ACADEMIC SKILLS
Gioia, G. A., Isquith, P. K., Guy, S. C., & Kenworhty, L. (2000). Test review: Behaviour rating
inventory of executive function. Child Neuropsychology, 6, 234-238.
https://doi.org/10.1076/chin.6.3.235.3152
Goela, V., Pullara, S. D., & Grafman, J. (2001). A computational model of frontal lobe
dysfunction: Working memory and the Tower of Hanoi task. Cognitive Science, 25,
287-313. https://doi.org/10.1207/s15516709cog2502_4
Goh, C., & Hu, G. W. (2014). Exploring the relationship between metacognitive awareness
and listening performance with questionnaire data. Language Awareness, 23, 255-
274. https://doi.org/10.1080/09658416.2013.769558
Hughes, C., Ensor, R., Wilson, A., & Graham, A. (2010). Tracking executive function across
the transition to school: A latent variable approach. Developmental Neuropsychology,
35, 20-36. https://doi.org/10.1080/87565640903325691
Huizinga, M., Dolan, C. V., & van der Molen, M. W. (2006). Age-related change in executive
function: Developmental trends and a latent variable analysis. Neuropsychologia, 44,
2017-2036. https://doi.org/10.1016/j.neuropsychologia.2006.01.010
Jaarsveld, S., Lachmann, T., Hamel, R., & van Leeuwen, C. (2010). Solving and creating
Raven Progressive Matrices: Reasoning in well- and ill-defined problem spaces.
Creativity Research Journal, 22, 304-319.
https://doi.org/10.1080/10400419.2010.503541
Jacob, R, & Parkinson, J. (2015). The potential for school-based interventions that target
executive function to improve academic achievement: A review. Review of
Educational Research, 85, 512-552. https://doi.org/10.3102/0034654314561338
Kennet, S., Eimer, M., Spence, C., & Driver, J. (2001). Tactile-visual links in exogenous
spatial attention under different postures: Convergent evidence from psychophysics
and ERPs. Journal of Cognitive Neuroscience, 13, 462-478.
https://doi.org/10.1162/08989290152001899 COGNITIVE PREDICTORS ACADEMIC SKILLS
Kramarski, B., Mevarech, Z. R., & Arami, M. (2002). The effects of meta-cognitive training on
solving mathematical authentic tasks. Educational Studies in Mathematics, 49, 225-
250.
Liu, C. Watanabe, T. (2010). Accounting for speed-accuracy tradeoff in visual perceptual
learning. Journal of Vision, 10, 1111, https://doi.org/10.1167/10.7.1111
Logan, G. D. (1994). On the ability to inhibit thought and action: A user’s guide to the stop
signal paradigm. In D. Dagenbach & T. H. Carr (Eds.), Inhibitory processes in
attention, memory, and language (pp. 189-239). Academic Press.
Masoura, E. V., & Gathercole, S. E. (2005). Phonological short-term memory skills and new
word learning in young Greek children. Memory, 13, 422-429.
https://doi.org/10.1037//0012-1649.33.6.966
Maqsud, M. (1998). Effects of metacognitive instruction on mathematics achievement and
attitude towards mathematics of low mathematics achievers. Educational Research,
40, 237-243. https://doi.org/10.1080/0013188980400210
Mazzocco, M. M., & Kover, S. T. (2007). A Longitudinal Assessment of Executive Function
Skills and Their Association with Math Performance. Child Neuropsychology, 13, 18-
45. https://doi.org/10.1080/09297040600611346
Milner, B. (1971). Interhemispheric differences in the localization of psychological processes
in man. British Medical Bulletin, 27, 272-277.
https://doi.org/10.1093/oxfordjournals.bmb.a070866
Mulder, H., Verhagen, J., Van der Ven, S. H. G., Slot, P. L., & Leseman, P. P. M. (2017).
Early executive function at age two predicts emergent mathematics and literacy at age
five. Frontiers in Psychology, 8, 1706. https://doi.org/10.3389/fpsyg.2017.01706
Numminen, H., Lehto, J. E., & Ruoppila, I. (2001). Tower of Hanoi and working memory in
adult persons with intellectual disability. Research in Developmental Disabilities, 22,
373-387. https://doi.org/10.1016/S0891-4222(01)00078-6 COGNITIVE PREDICTORS ACADEMIC SKILLS
Preacher, K. J., & MacCallum, R. C. (2003). Repairing tom Swift's electric factor analysis
machine. Understanding Statistics, 2, 13-43.
https://doi.org/10.1207/S15328031US0201_02
Pureza, J. R., Gonçalves, H. A., Branco, L., Grassi-Oliveira, R., & Fonseca, R. P. (2013).
Executive functions in late childhood: Age differences among groups. Psychology &
Neuroscience, 6, 79-88. http://dx.doi.org/10.3922/j.psns.2013.1.12
Raven, J., Raven, J. C., & Court, J. H. (1998). Manual for Raven’s progressive matrices and
vocabulary scales. Section 1: General overview. Harcourt Assessment.
R core team. (2019). R: A language and environment for statistical computing. R Foundation
for Statistical Computing. https://www.R-project.org/
Richardson, J. T. E. (2007). Measures of short-term memory: A historical review. Cortex, 43,
635-650. https://doi.org/10.1016/S0010-9452(08)70493-3
Roebers, C. M. (2017). Executive function and metacognition: Towards a unifying framework
of cognitive self-regulation. Developmental Review, 45, 31-51.
https://doi.org/10.1016/j.dr.2017.04.001
Roebers, C. M., Cimeli, P., Röthlisberger, M., & Neuenschwander, R. (2012). Executive
functioning, metacognition, and self-perceived competence in elementary school
children: An explorative study on their interrelations and their role for school
achievement. Metacognition and Learning, 7, 151-173. https://doi.org/10.1007/s11409-
012-9089-9
Roebers, C. M., & Feurer, E. (2016). Linking executive functions and procedural
metacognition. Child Development Perspectives, 10, 39-44.
https://doi.org/10.1111/cdep.12159
Roebers, C. M., Schmid, C., & Roderer, T. (2009). Metacognitive monitoring and control
processes involved in primary school children’s test performance. The British Journal
of Educational Psychology, 79, 749-767. https://doi.org/10.1348/978185409X429842 COGNITIVE PREDICTORS ACADEMIC SKILLS
Rogers, R. D., & Monsell, S. (1995). Costs of a predictable switch between simple cognitive
tasks. Journal of Experimental Psychology: General, 124, 207-231.
https://doi.org/10.1037/0096-3445.124.2.207
Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of
Statistical Software, 48, 1-36. https://doi.org/10.18637/jss.v048.i02
Schmitt, S. A., Geldhof, G. J., Purpura, D. J., Duncan, R., & McClelland, M. M. (2017).
Examining the relations between executive function, math, and literacy during the
transition to kindergarten: A multi-analytic approach. Journal of Educational
Psychology, 109, 1120-1140. https://doi.org/10.1037/edu0000193
Serpell, Z. N., & Esposito, A. G. (2016). Development of executive functions. Policy Insights
from the Behavioral and Brain Sciences, 3, 203-210.
https://doi.org/10.1177/2372732216654718
Shimamura, A. P. (2000). Toward a cognitive neuroscience of metacognition.
Consciousness and Cognition, 9, 313-323. https://doi.org/10.1006/ccog.2000.0450
Shin, H., Bjorklund, D. F., & Beck, E. F. (2007). The adaptive nature of children’s
overestimation in a strategic memory task. Cognitive Development, 22, 197-212.
https://doi.org/10.1016/j.cogdev.2006.10.001.
Snelbaker, A. J., Wilkinson, G. S., Robertson, G. J., & Glutting, J. J. (2001). Wide range
achievement test 3 (WRAT3). In W. I. Dorfman & M. Hersen (Eds.), Understanding
psychological assessment: Perspectives on individual differences (pp. 259-274).
Springer.
Souchay, C., & Isingrini, M. (2004). Age related differences in metacognitive control: Role of
executive functioning. Brain and Cognition, 56, 89-99.
https://doi.org/10.1016/j.bandc.2004.06.002
St Clair-Thompson, H. L., & Gathercole, S. E. (2006). Executive functions and achievements
in school: Shifting, updating, inhibition, and working memory. The Quarterly Journal of
Experimental Psychology, 59, 745-759.
https://doi.org/10.1080%2F17470210500162854 COGNITIVE PREDICTORS ACADEMIC SKILLS van der Sluis, S., de Jong, P. F., & van der Leij, A. (2007). Executive functioning in children,
and its relations with reasoning, reading, and arithmetic. Intelligence, 35, 427-449.
https://doi.org/10.1016/j.intell.2006.09.001
Veenman, M. V. J., Bavelaar, L., de Wolf, L., & van Haaren, M. G. P. (2014). The on-line
assessment of metacognitive skills in a computerized learning environment. Learning
and Individual Differences, 29, 123-130. https://doi.org/10.1016/j.lindif.2013.01.003
Vrugt, A., & Oort, F. J. (2008). Metacognition, achievement goals, study strategies and
academic achievement: Pathways to achievement. Metacognition and Learning, 3,
123-146. https://doi.org/10.1007/s11409-008-9022-4
Vukman, K. B., & Licardo, M. (2010). How cognitive, metacognitive, motivational and
emotional self-regulation influence school performance in adolescence and early
adulthood. Educational Studies, 36, 259-268.
https://doi.org/10.1080/03055690903180376
Wang, Q., & Pomerantz, E. M. (2009). The motivational landscape of early adolescence in
the US and China: A longitudinal study. Child Development, 80, 1272-1287.
https://doi.org/10.1111/j.1467-8624.2009.01331.x
Wechsler, D. (2004). Wechsler Intelligence Scale for Children (4th ed.). Harcourt
Assessment.
Welsh, M. C. (1991). Rule-guided behaviour and self-monitoring on the Tower of Hanoi disk-
transfer task. Cognitive Development, 6, 59-76. https://doi.org/10.1016/0885-
2014(91)90006-Y
Wiebe, S. A., Espy, K. A., & Charak, D. (2008). Using confirmatory factor analysis to
understand executive control in preschool children: I. Latent structure.
Developmental Psychology, 44, 575-587. https://doi.org/10.1037/0012-1649.44.2.575
Wilkinson, G. S. (1993). Wide range achievement test 3. Wide Range Inc.
Williams, P. G., Rau, H. K., Suchy, Y., Thorgusen, S. R., & Smith, T. W. (2017). On the
validity of self-report assessment of cognitive abilities: Attentional control scale COGNITIVE PREDICTORS ACADEMIC SKILLS
associations with cognitive performance, emotional adjustment, and personality.
Psychological Assessment, 29, 519-530. https://doi.org/10.1037/pas0000361
Xu, C., Ellefson, M. R., Ng, F., Wang, Q., & Hughes, C. (2020). An East-West contrast in
executive function: Measurement invariance of computerized tasks in school-aged
children and adolescents. Journal of Experimental Child Psychology, 199, Article
104929. https://doi.org/10.1016/j.jecp.2020.104929
Yeniad N., Malda M., Mesman J., van Ijzendoorn M. H., Pieper S. (2013). Shifting ability
predicts math and reading performance in children: a meta-analytical study. Learning
and Individual Differences, 23,1-9. https://doi.org/10.1016/j.lindif.2012.10.004
Zimmerman, B. J. (2011). Motivational sources and outcomes of self-regulated learning and
performance. In B. J. Zimmerman & D. H. Schunk (Eds.), Educational psychology
handbook series. Handbook of self-regulation of learning and performance (pp. 49-64).
Routledge.