Metacognition across domains: Is the association between arithmetic and metacognitive monitoring domain-specific?

Abstract

Metacognitive monitoring is a critical predictor of arithmetic in primary school. One outstanding question is whether this metacognitive monitoring is domain-specific or whether it reflects a more general performance monitoring process. To answer this conundrum, we investigated metacognitive monitoring in two related, yet distinct academic domains: arithmetic and spelling. This allowed us to investigate whether monitoring in one domain correlated with monitoring in the other domain, and whether monitoring in one domain was predictive of performance in the other, and vice versa. Participants were 147 typically developing 8-9-year-old children (Study 1) and 77 typically developing 7-8-year-old children (Study 2), who were in the middle of an important developmental period for both metacognitive monitoring and academic skills. Pre-registered analyses revealed that within-domain metacognitive monitoring was an important predictor of arithmetic and spelling at both ages. In 8-9-year-olds the metacognitive monitoring measures in different academic domains were predictive of each other, even after taking into account academic performance in these domains. Monitoring in arithmetic was an important predictor of spelling performance, even when arithmetic performance was controlled for. Likewise, monitoring in spelling was an important predictor of arithmetic performance, even when spelling performance was controlled for. In 7-8-year-olds metacognitive monitoring was domain-specific, with neither correlations between the monitoring measures, nor correlations between monitoring in one domain and performance in the other. Taken together, these findings indicate that more domain-general metacognitive monitoring processes emerge over the ages from 7 to 9.

Full text

RESEARCH ARTICLE
Metacognition across domains: Is the
association between arithmetic and
metacognitive monitoring domain-specific?
Elien BellonID
1
*, Wim Fias
2
, Bert De Smedt
1
1Parenting and Special Education Research Unit, KU Leuven, Leuven, Belgium, 2Experimental
Psychology, Ghent University, Gent, Belgium
*elien.bellon@kuleuven.be
Abstract
Metacognitive monitoring is a critical predictor of arithmetic in primary school. One outstanding
question is whether this metacognitive monitoring is domain-specific or whether it reflects a
more general performance monitoring process. To answer this conundrum, we investigated
metacognitive monitoring in two related, yet distinct academic domains: arithmetic and spell-
ing. This allowed us to investigate whether monitoring in one domain correlated with monitor-
ing in the other domain, and whether monitoring in one domain was predictive of performance
in the other, and vice versa. Participants were 147 typically developing 8-9-year-old children
(Study 1) and 77 typically developing 7-8-year-old children (Study 2), who were in the middle
of an important developmental period for both metacognitive monitoring and academic skills.
Pre-registered analyses revealed that within-domain metacognitive monitoring was an impor-
tant predictor of arithmetic and spelling at both ages. In 8-9-year-olds the metacognitive moni-
toring measures in different academic domains were predictive of each other, even after
taking into account academic performance in these domains. Monitoring in arithmetic was an
important predictor of spelling performance, even when arithmetic performance was con-
trolled for. Likewise, monitoring in spelling was an important predictor of arithmetic perfor-
mance, even when spelling performance was controlled for. In 7-8-year-olds metacognitive
monitoring was domain-specific, with neither correlations between the monitoring measures,
nor correlations between monitoring in one domain and performance in the other. Taken
together, these findings indicate that more domain-general metacognitive monitoring pro-
cesses emerge over the ages from 7 to 9.
Introduction
“Learn from your mistakes” is an old saying that (grand)parents teach their children. This goes
back to the premise that making mistakes is associated with learning. Noticing your mistakes
is an example of monitoring your cognition. This monitoring of cognition is a facet of meta-
cognition, a concept first introduced by Flavell [1]. One critical component of metacognition
is procedural metacognition. This is a self-reflecting, higher-order cognitive process, which
indicates how people monitor and control their cognition during ongoing cognitive processes
[2,3]. Metacognitive monitoring is an important aspect of procedural metacognition and is
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OPEN ACCESS
Citation: Bellon E, Fias W, De Smedt B (2020)
Metacognition across domains: Is the association
between arithmetic and metacognitive monitoring
domain-specific? PLoS ONE 15(3): e0229932.
https://doi.org/10.1371/journal.pone.0229932
Editor: Je
´ro
ˆme Prado, French National Center for
Scientific Research (CNRS) & University of Lyon,
FRANCE
Received: September 27, 2019
Accepted: February 17, 2020
Published: March 12, 2020
Peer Review History: PLOS recognizes the
benefits of transparency in the peer review
process; therefore, we enable the publication of
all of the content of peer review and author
responses alongside final, published articles. The
editorial history of this article is available here:
https://doi.org/10.1371/journal.pone.0229932
Copyright: ©2020 Bellon et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: An anonymized
dataset will be available on the Open Science
Framework (https://osf.io/ypue4/?view_only=

defined as the subjective self-assessment of how well a cognitive task will be/is/has been per-
formed [3,4].
Two recent studies found evidence for metacognitive monitoring (i.e., reflecting procedural
metacognition) as an important predictor of arithmetic achievement [5,6]. To determine the role
of metacognitive monitoring, these authors asked children on a trial-by-trial basis to report their
judgement of the accuracy of their answers during an arithmetic task. Both studies found that
successful appraisal of the accuracy of one’s arithmetic judgement is a powerful predictor of
arithmetic performance in primary school children. To date, however, it is unclear whether the
results regarding the strength of the role of metacognitive monitoring in arithmetic are specific
to the arithmetic domain, or whether they are reflective of a more general role of metacognitive
monitoring in academic performance; an outstanding question on which this study will focus.
Metacognition has been regarded as a fundamental skill influencing cognitive performance
and learning in domains as diverse as arithmetic, memory, reading, perception, and many oth-
ers (e.g., [6–18]). The importance of metacognition that was found in existing research in differ-
ent (cognitive) domains is not surprising, as metacognitive aspects, such as knowing the limits
of your own knowledge and being able to regulate that knowledge, are essential components of
self-regulated and successful learning [16], enabling learners to improve their cognitive achieve-
ments. For example, good metacognition allows learners to correctly allocate study-time, check
answers when they feel unsure about the correctness of the answer or provide a learning
moment when an error is detected. Besides being considered a global ability playing a role in a
large range of domains, metacognition, and consequently metacognitive monitoring, is usually
considered to be a domain-general cognitive process that is correlated across content domains.
This suggests that people who are good at evaluating their performance for one task, also tend
to be good at evaluating their performance for another task (e.g., [19,20]). There is, however,
evidence suggesting that this domain-generality only emerges over development. Geurten and
colleagues [19] recently observed that metacognition is first domain-specific and then general-
izes across domains as children mature. They found a gradual shift from domain-specific
towards domain-general metacognition across the arithmetic and memory domains in children
aged between 8 and 13. In adults, more evidence for the domain-generality has been observed.
Veenman and colleagues [21] and Schraw and colleagues [20,22] found that metacognitive
measures are correlated across unrelated (cognitive) tasks. More specifically, Schraw and col-
leagues [20] found significant correlations between metacognitive measures across eight differ-
ent domains ranging from historic knowledge to knowledge of caloric values of food. This
domain-general hypothesis in adults is also supported by brain imaging data that show that
adults’ metacognitive abilities for different types of tasks partially depend on common neural
structures, such as the prefrontal cortex [23] and precuneus [24].
However, domain-specific knowledge and skills also seem to be important for metacognitive
monitoring. For example, in young children (ages 5 to 8 years), Vo and colleagues [25] showed
that metacognition in the numerical domain was unrelated to metacognition in the emotional
domain, suggesting young children’s metacognition is domain-specific. Based on their empirical
findings, Schraw and colleagues [20] suggested that in adults metacognitive monitoring within a
specific domain is governed by general metacognitive processes in addition to domain-specific
knowledge. Lo¨ffler, Von Der Linden and Schneider [26] documented a twofold effect of exper-
tise on monitoring in soccer: Although domain-specific knowledge enhances monitoring per-
formance in some situations, more optimistic estimates (presumably due to the application of a
familiarity heuristic) typically reduce monitoring accuracy in experts. Likewise, in mathematics,
metacognitive monitoring has been found to be a function of domain-specific ability (e.g.,
[27,28]). Taken together, the existing research also illustrates the importance of domain-specific
knowledge and skills for metacognitive monitoring.
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ce9f97af0e3149c28942a43499eafd32) after
publication.
Funding: This research was supported by the Fund
for Scientific Research Flanders grant G.0638.17
The funders had no role in study design, data
collection and analysis, decision to publish or
preparation of the manuscript.
Competing interests: The authors have declared
that no competing interests exist.

This issue of domain-specificity is a longstanding debate within the metacognitive literature
(e.g., [19–22,29]), both on the behavioural and brain-imaging level. Yet, in children, the results
are scarce and rather inconclusive, with different results for various age groups as well as meta-
cognitive measures.
Firstly, age-related differences in the results on domain-specificity of metacognition in chil-
dren are not surprising, as a critical development in monitoring is observed during early to late
childhood (e.g., [19,30]). For example, in (early) primary school, metacognitive monitoring
accuracy is found to increase (e.g., [30–35]). In the same developmental time period of these
age-related improvements in monitoring of cognition, there are also important age-related
improvements in academic skills, such as arithmetic and spelling. The age-related metacogni-
tive improvements are recognized to underlie several aspects of cognitive development in vari-
ous domains (e.g., improvements in accuracy; e.g., [30]). Furthermore, based on their empirical
findings, Geurten and colleagues [19] conclude that a gradual shift toward domain-general
metacognition occurs in children between 8 and 13 years, and that metacognition is no more
bound by task content and domain knowledge after the age of 10. Against this background and
to thoroughly investigate the domain-specificity question in children, the current research spe-
cifically recruited 8–9 year-olds (third grade; Study 1) and 7–8 year-olds (second grade; Study
2), who are in the middle of this important developmental period for both metacognitive moni-
toring and academic skills.
Secondly, the different results on domain-specificity of metacognition in children for differ-
ent metacognitive measures may in part be due to different aspects of metacognition being
investigated. Metacognition includes both declarative and procedural metacognition. As meta-
cognition encompasses different aspects, it is not surprising that these different aspects of
metacognition follow different developmental paths [34] and that they are differently associ-
ated with domain-specific knowledge and skills. A recent study by Bellon and colleagues [5],
for example, found that within-domain metacognitive monitoring was associated with arith-
metic performance, while declarative metacognitive knowledge was not. The authors suggest
this might indicate that children’s metacognition is more domain-specific than it is domain-
general. Yet, the authors based their suggestion on results on different aspects of metacogni-
tion, which were measured fundamentally differently (i.e., online, trial-by-trial reports for
metacognitive monitoring vs. general questionnaire for declarative metacognitive knowledge),
making testing the domain-specificity hypothesis as well as making strong claims about
domain-specificity of metacognition troublesome.
To overcome these issues, the current research specifically focused on the monitoring
aspect of metacognition. Extending the existing body of data, we included, in addition to the
metacognitive monitoring measure in arithmetic, the same metacognitive monitoring measure
in another domain of academic learning, i.e., spelling. By including metacognitive monitoring
measures in two domains, and, importantly, by using the exact same paradigm to measure it,
the current study was able to investigate the question of domain-specificity more thoroughly.
The paradigm to measure metacognitive monitoring was the same as in Bellon et al. [5] and
Rinne and Mazzocco [6]. Spelling was included as a second domain to maximize the compara-
bility of the two tasks in which metacognitive monitoring was measured. Arithmetic and
spelling are quintessential domains in primary school and in both domains primary school
children go through crucial developmental steps. Based on the children’s curriculum, we were
able to select age-appropriate items. This allowed us to thoroughly investigate whether the
results on the role of metacognitive monitoring in arithmetic are specific to the arithmetic
domain or not.
Based on the outstanding issues outlined above, this study aims to extend and deepen our
knowledge on the domain-specificity of the role of metacognition in different academic
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domains in middle childhood. Specifically, this study will investigate whether metacognitive
monitoring is domain-specific or not by investigating (a) the associations between within-
domain metacognitive monitoring and arithmetic and spelling; (b) whether metacognitive
monitoring in one domain is associated with and/or predicted by metacognitive monitoring in
the other domain; (c) whether performance in one domain is associated with and/or predicted
by metacognitive monitoring in the other domain, and (d) these questions in two different age
groups in primary school to fully grasp potential transitional periods in the domain-specificity
of metacognitive monitoring.
If, on the one hand, metacognition is highly domain-general, then metacognitive monitor-
ing in the arithmetic and spelling tasks will be correlated and predictive of each other, even
when controlled for academic performance–as arithmetic and spelling are highly related
domains; and metacognitive monitoring in one domain will be associated with and predicts
academic performance in the other domain. If, on the other hand, metacognition is highly
domain-specific, then the associations described above will be non-significant (frequentist sta-
tistics) and Bayes factors will be close to zero (Bayesian statistics; see below). These questions
are investigated in two different age groups for which, based on the existing literature, different
predictions can be made on the extent to which metacognitive monitoring is domain-general.
By selecting participants in these two age groups, we aimed to capture an important period in
the development of (domain-generality of) metacognitive monitoring. In Study 1, we investi-
gated these questions in 8-9-year-olds, for which domain-generality of metacognitive monitor-
ing was predicted (third grade). Study 2 investigated these questions in younger children,
namely 7-8-year-olds, for which more domain-specificity of metacognitive monitoring was
predicted (second grade).
Study 1: Metacognitive monitoring in arithmetic and spelling in 8-
9-year-olds (third grade)
Methods
Participants. Participants were 147 typically developing Flemish 8–9 year-olds (third
grade; 69 girls; M
age
= 8 years, 10 months; SD = 3 months; [8 years 4 months—9 years 4
months]), without a diagnosis of a developmental disorder, and who came from a dominantly
middle-to-high socio-economic background. This study was approved by the social and socie-
tal ethics committee of KU Leuven. For every participant, written informed parental consent
was obtained.
Procedure. All participants participated in four test sessions, which took place at their
own school during regular school hours. They all completed the tasks in the same order. In
the context of a larger project, all children first participated in an individual session of which
the data are not included in the current manuscript. Second, a session in small groups of eight
children took place, including the computerized spelling task and motor speed task. Third, a
second session in small groups took place, including the computerized arithmetic task and
motor speed tasks. Fourth, in a group session in the classroom, the standardized arithmetic
and spelling tests and the test of intellectual ability were administered. Sessions were separated
by one to three days on average; they were never adjacent. Below we describe the key variables
and control variables used to answer our research questions. The full cognitive testing battery
is posted on the Open Science Framework (OSF) page of this project (https://osf.io/ypue4/?
view_only=ce9f97af0e3149c28942a43499eafd32).
Materials. Materials consisted of written standardized tests and computer tasks designed
with Open Sesame [36]. Arithmetic and spelling skills were assessed with both a custom com-
puterized task and a standardized test (i.e., Arithmetic: Tempo Test Arithmetic [37]; Spelling:
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standardized dictation [38]). The computerized tasks for arithmetic and spelling were specifi-
cally designed to be as similar as possible, to minimize the possibility that the results on
domain-specificity of metacognition were due to differences in paradigm. Both tasks were
multiple choice tasks with specifically selected age-appropriate items (i.e., single digit addition
and multiplication for arithmetic; three specific Dutch spelling rules for spelling). After a first
introductory block, in the second block of each task, participants had to report their judgment
on the accuracy of their academic answer after each trial, using the same metacognitive moni-
toring measure in both tasks.
Arithmetic. Custom computerized arithmetic task. This single-digit task included addi-
tion and multiplication items, and comprised all combinations of the numbers 2 to 9
(n= 36). The task consisted of two blocks, i.e., one introductory block without (n= 12) and
one with (n= 60) a metacognitive monitoring measure (see below). Stimuli were pseudo-
randomly divided into the two blocks and children were given a short break between blocks.
Each block was preceded by four practice trials to familiarize the child with the task require-
ments. Performance on the practice items was not included in the performance measures. In
both blocks, addition items were presented first (n= 6 in the first block; n= 30 in the second
block). After a short instruction slide indicating multiplication items would follow, the mul-
tiplication items were presented (n= 6 in the first block; n= 30 in the second block). The
position of the numerically largest operand was balanced. Each item was presented with two
possible solutions, one on the left and one on the right side of the screen. In half of the items,
the correct solution was presented on the left side of the screen. Incorrect solutions for the
addition items were created by adding or subtracting 1 or 2 to the solution (n= 7 for every
category), or by using the answer to the corresponding multiplication item (e.g., 6 + 3 with
incorrect solution 18; n= 8). The incorrect solutions for the multiplication items were table
related, i.e., solution -1 or +1 one of the operands (e.g., 6 ×3 = 24; n= 7 for every category),
or the answer to the corresponding addition (e.g., 8 ×2 = 10; n= 8). Each trial started with a
250 ms fixation point in the centre of the screen and after 750 ms the stimulus appeared in
white on a black background. The stimuli remained visible until response. The children had
to indicate which of the presented solutions for the problem was correct (by pressing the
corresponding key). The response time and answer were registered via the computer. Perfor-
mance measures were both accuracy and the response time for correct answers in the second
block (n= 60).
Standardized arithmetic task. Arithmetic fluency was assessed by the Tempo Test Arithme-
tic (TTA; [37]); a standardized pen-and-paper test of arithmetical fluency, which comprises
five columns of arithmetic items (one column per operation and a mixed column), each
increasing in difficulty. Participants got one minute per column to provide as many correct
answers as possible. The performance measure was the total number of correctly solved items
within the given time (i.e., total score over the five columns).
Spelling. Custom computerized spelling task. Spelling performance was measured with a
computerized task consisting of two blocks, i.e., one introductory block without (n= 12) and
one with (n= 60) a metacognitive monitoring measure (see below). Stimuli were pseudo-ran-
domly divided into the two blocks and children were given a short break between blocks. Each
block was preceded by six practice trials to familiarize the child with the task requirements.
Performance on the practice items was not included in the performance measures. The items
consisted of a Dutch word with a missing part, that was replaced by an underscore (e.g., ‘ko_ie’
for ‘koffie’), presented with two possible solutions, one on the left and one on the right side
of the screen. We used three specific Dutch spelling rules, which were the focus of spelling
instruction at the participants’ age. Firstly, the rule of open and closed syllables was used, on
the basis of which one can figure out if one or two vowels or consonants have to be written.
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Secondly, the extension rule was used, on the basis of which one can figure out if words with a
[t] sound at the end of the word are written with a ‘t’ or a ‘d’. To correctly spell these two types
of words, children can either use these rules, or when they have extensive experience with
these words, retrieve the correct spelling from their memory. Flemish third graders have the
most experience with the extension rule, and are in the learning phase for the open and closed
syllables rule. Stepwise, they go from learning the rule and using the procedure to spell the
words, towards automatization of the correct spelling and thus retrieving it from memory.
This spelling development is analogous to arithmetic development in third grade (i.e., from
procedure use to retrieval). A third category of words was added for which no rule is available,
but only retrieval from long-term memory is possible (i.e., au/ou-words; ei/ij-words). The
diphthongs in these words have the same pronunciation, but are spelt differently (e.g., ‘reis’ vs.
‘wijs’ have both the [εi] sound)–there is no rule to determine whether one or the other diph-
thong should be used and children have to learn this by heart. All items were selected from
curriculum-based glossaries. Incorrect solutions were created by using the related distractor
(n= 14 for each category), namely one or two vowels or consonants for the open and closed
syllables rule (e.g., koffie: ‘ko_ie’ with options ‘f’ or ‘ff’), ‘t’ or ‘d’ for the extension rule (e.g.,
kast: ‘kas_’ with options ‘t’ or ‘d’), and the related diphthong for the to-be-retrieved words
(e.g., konijn: ‘kon_n’ with options ‘ei’ or ‘ij’). In half of the items, the correct solution was pre-
sented on the left side of the screen. Each trial started with a 250 ms fixation point in the centre
of the screen and after 750 ms children were presented on audiotape with the word. Then, the
visual stimulus appeared in white on a black background. The stimuli remained visible until
response. The children had to indicate which of the presented solutions for the problem was
correct (by pressing the corresponding key; i.e., left/right key). The response time and answer
were registered via the computer. Performance measures were both accuracy and the response
time for correct answers in the second block (n= 60).
Standardized spelling task. Spelling ability was also measured with a standardized dictation
[38]. We administered the subtest for children at the end of third grade, which includes age-
appropriate, curriculum-based items. The experimenter read aloud 43 sentences and the par-
ticipants had to write one word down that was repeated two times after the sentence was read.
The performance measure was the total number of correctly written words.
Metacognitive monitoring. In the second block of the arithmetic and the spelling task
(n= 60 for each task), a metacognitive monitoring measure was added to the items. Children
had to report their judgment on the accuracy of their answer to the item on a trial-by-trial
basis (e.g., [5,6]). After giving their answer to the arithmetic/spelling problem, children had to
indicate if they thought their answer was Correct,Incorrect, or if they Did not know. We used
emoticons in combination with the options (e.g., ☺and Correct) to make the task more attrac-
tive and feasible for children (Fig 1). Children had to respond by pressing the key correspond-
ing to their metacognitive judgment (i.e., indicated with corresponding emoticon stickers).
Metacognitive monitoring skills were operationalised as calibration of this judgment (i.e., the
alignment between one’s judgment in the accuracy of their answer to a problem and the actual
accuracy of the answer). Namely, a calibration score of 2 was obtained if their metacognitive
judgment corresponded to their actual performance (i.e., metacognitively judged as Correct
and indeed correct academic answer; metacognitively judged as Incorrect and indeed incorrect
academic answer), a calibration score of 0 if their metacognitive judgement did not correspond
to their actual performance (i.e., metacognitively judged as Correct and in fact incorrect aca-
demic answer; metacognitively judged as Incorrect and in fact correct academic answer), and a
calibration score of 1 if children indicated they Did not know about their academic answer.
The metacognitive monitoring score per child was the mean of all calibration scores (i.e., cali-
bration score per arithmetic/spelling item; n= 60 per domain) and was calculated for each task
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separately. The higher the calibration scores, the better the metacognitive monitoring skills. To
familiarize the children with the task, practice items were presented in each task.
Control variables. Intellectual ability. Intellectual ability was assessed through the Raven’s
Standard Progressive Matrices [39]. Children were given 60 multiple-choice items in which
they had to complete a pattern. The performance measure was the number of correctly solved
patterns.
Motor speed. A motor speed task was included as a control for children’s response speed on
the keyboard [5]. Two shapes were simultaneously presented on either side of the screen and
children had to indicate which of the two shapes was filled by pressing the corresponding key
(i.e., left/right key). All shapes were similar in size and each shape occurred four times filled
and four times non-filled, yielding 20 trials. The position of the filled shape was balanced.
After fixation, stimuli appeared until response. Three practice trials were included to familiar-
ize the children with the task. The performance measure was the average response time of cor-
rect responses.
Data analysis. A comprehensive analyses plan was preregistered on the OSF page of this
project (https://osf.io/ypue4/?view_only=ce9f97af0e3149c28942a43499eafd32). The key analy-
ses to answer our research questions are presented below; the results of the remaining preregis-
tered analyses can be found in the supplementary materials (S1 File).
We ran frequentist analyses using both uni- and multivariate techniques, as well as Bayesian
analyses. Frequentist analyses allowed us to explore our data by means of a well-known method
to gauge statistical support for the hypotheses of interest. Bayesian statistics allowed us to test
the degree of support for a hypothesis (i.e., degree of strength of evidence in favour of or against
any given hypothesis), expressed as the Bayes factor (BF; the ratio between the evidence in sup-
port of the alternative hypothesis over the null hypothesis (BF
10
)). Although Bayes factors
Fig 1. Example of metacognitive monitoring question after arithmetic/spelling item.
https://doi.org/10.1371/journal.pone.0229932.g001
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provide a continuous measure of degree of evidence, there are some conventional approximate
guidelines for interpretation ([40] for a classification scheme): BF
10
= 1 provides no evidence
either way, BF
10
>1 anecdotal, BF
10
>3 moderate, BF
10
>10 strong, BF
10
>30 very strong
and BF
10
>100 decisive evidence for the alternative hypothesis; BF
10
<1 anecdotal, BF
10
<
0.33 moderate, BF
10
<0.10 strong, BF
10
<0.03 very strong and BF
10
<0.01 decisive evidence
for the null hypothesis. By adding these Bayesian analyses, we deepened our findings from the
traditional analyses, as we were able to identify evidence in favour of the null hypothesis, conse-
quently, identify which hypothesis is most plausible (i.e., alternative hypothesis vs. null hypoth-
esis) and which predictors are the strongest. This is particularly relevant for the current study
because we can compare the strength of evidence in favour of the domain-general hypothesis
(i.e., association between metacognitive monitoring measures in different domains; association
between performance and metacognitive monitoring across domains) versus the domain-spe-
cific hypothesis (i.e., no association between metacognitive monitoring measures in different
domains; no association between performance and metacognitive monitoring across domains).
To answer our research questions, we used correlation and regression analyses. For the
Bayesian analyses, we used a default prior with prior width set to 1 for Pearson correlations
and to .354 for the linear regression analyses. For Bayesian regressions, a BF
inclusion
was calcu-
lated for every predictor in the model, which represents the change from prior to posterior
odds (i.e., BF
10
), where the odds concern all the models with a predictor of interest to all mod-
els without that predictor (i.e., a Bayes factor for including a predictor averaged across the
models under consideration).
As planned in the preregistration, we excluded a child’s performance on a task if this perfor-
mance was more than three standard deviations from the mean of the task (i.e., �3% of the
data per task). Due to unforeseen circumstances during data collection (e.g., school bell ring-
ing), we additionally excluded some data at the item level (i.e., <0.57% of items per task) that
were considered to be measurement errors, i.e., when the data point was an outlier (i.e., more
than three standard deviations from the mean) on both the item level (i.e., compared to the
general mean of the item) and on the subject level (i.e., compared to the personal mean of the
subject).
Results
The descriptive statistics of all measures are presented in S1 Appendix. Additionally, Pearson
correlation coefficients of all variables under study were calculated (S2 Appendix). Although
not originally pre-registered, we additionally re-calculated all analyses below with chronologi-
cal age as an additional control variable. Considering chronological age within grade in the
analyses reported below did not change the interpretation of the results (S3 Appendix).
The role of metacognitive monitoring in arithmetic and spelling performance. Pearson
correlation coefficients of the associations between metacognitive monitoring and the aca-
demic skills are presented in Table 1.
Metacognitive monitoring in the arithmetic task (MM
arith
) was significantly correlated with
arithmetic accuracy (Arithmetic
acc
) and the tempo test arithmetic (TTA), with Bayes factors
indicating decisive evidence in favour of the associations, even when controlling for intellectual
ability. There was no significant correlation with response time for correct arithmetic answers
(Arithmetic
rt
) and the Bayes factor indicated moderate evidence in favour of no association.
Metacognitive monitoring in the spelling task (MM
spell
) was significantly correlated with
spelling accuracy (Spelling
acc
) and dictation, with Bayes factors indicating decisive evidence in
favour of the associations, even when controlling for intellectual ability. There was no
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significant correlation with response time for correct spelling answers (Spelling
rt
) and the
Bayes factor indicated moderate evidence in favour of no association.
Based on the absence of significant (frequentist statistics) and supported (Bayesian statis-
tics) associations with our response time performance measures (Arithmetic
rt
and Spelling
rt
),
and because these measures only take into account data for correct answers, losing important
information on performance and possibly overestimating performance, the response time per-
formance measures will not be considered in further analyses.
Domain-specificity of the role of metacognitive monitoring. To examine domain-speci-
ficity of the role of metacognition, we first investigated the association between MM
arith
and
MM
spell
with correlation and regression analyses. Specifically, we investigated whether MM
arith
and MM
spell
were correlated, even when controlling for intellectual ability and academic per-
formance in both domains. Controlling for intellectual ability and performance in both stan-
dardized academic tasks was necessary, to make sure the observed associations between
MM
arith
and MM
spell
were not (entirely) driven by their shared reliance on intellectual ability
or by the high correlation between both academic domains.
Secondly, we studied the role of MM
spell
in arithmetic performance and MM
arith
in spelling
performance with correlation and regression analyses. In other words, cross-domain correla-
tions between academic performance in one domain and metacognitive monitoring in the
other domain were calculated. As performance in the arithmetic and spelling tasks was highly
correlated, the cross-domain associations of metacognitive monitoring and academic perfor-
mance might rely on the correlation between the academic tasks. Therefore, we used regres-
sion models to investigate whether metacognitive monitoring in arithmetic uniquely predicted
spelling performance on top of arithmetic performance, and vice versa.
In a final step, we investigated the unique contribution of cross-domain metacognitive
monitoring to performance over within-domain metacognitive monitoring using regression
models including metacognitive monitoring in both domains as predictors for academic
performance.
Associations between metacognitive monitoring in different domains. MM
arith
and MM
spell
were significantly correlated, even when controlling for intellectual ability, and arithmetic and
spelling performance on the standardized tasks (r= .42; p<.001; BF
10
>100). Regression
analyses confirmed that metacognitive monitoring in one domain was uniquely predicted by
Table 1. Correlation analyses of metacognitive monitoring and academic performance measures in 8-9-year-olds (Grade 3).
Arithmetic Spelling
Custom task–
Accuracy
a
Custom task—
RT
b
Standardized task
(TTA)
a
Custom task—
Accuracy
a
Custom task
-RT
b
Standardized task
(dictation)
a
Metacognitive
Monitoring
Arithmetic
r.84 -.08 .38 .45 .11 .26
p<.001 .38 <.001 <.001 .20 .003
BF
10
>100 0.16 >100 >100 0.24 9.65
Spelling
r.48 -.19 .33 .91 -.02 .66
p<.001 0.03 <.001 <.001 .79 <.001
BF
10
>100 1.18 >100 >100 0.11 >100
a
Controlled for intellectual ability.
b
Controlled for intellectual ability and motor speed on the keyboard.
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metacognitive monitoring in the other domain, even when simultaneously considered with
intellectual ability and performance on the standardized tasks in both academic domains (see
Table 2). Additional post-hoc analyses that were not preregistered indicated that the results
were the same when including academic achievement as measured with accuracy in the com-
puterized academic tasks instead of academic achievement as measured with the standardized
academic tasks.
Cross-domain performance associations of metacognitive monitoring.Table 1 shows cross-
domain correlations between academic performance and metacognitive monitoring in the
other domain. MM
arith
was significantly correlated with both spelling performance measures
(i.e., Spelling
acc
and dictation), with a Bayes factor indicating moderate to decisive evidence.
MM
spell
was significantly correlated with both arithmetic performance measures (i.e., Arith-
metic
acc
and TTA), with a Bayes factor indicating decisive evidence.
We further investigated whether metacognitive monitoring in arithmetic uniquely pre-
dicted spelling performance on top of arithmetic performance; and vice versa. Namely, we
predicted arithmetic performance based on MM
spell
and dictation, and spelling performance
based on MM
arith
and TTA (Table 3). These regression analyses showed that, even when per-
formance in the academic domain was taken into account, metacognitive monitoring in that
domain remained a significant and supported predictor of academic performance in the other
domain (all ps<.05; all BFs
10
>5).
When metacognitive monitoring scores in both domains were considered simultaneously
to predict academic performance (using regression analyses), only the role of metacognitive
monitoring within the domain itself remained significant (frequentist statistics) and supported
(Bayesian statistics). Namely, when MM
arith
and MM
spell
were used to predict arithmetic per-
formance, only MM
arith
was a significant and supported predictor (Arithmetic
acc
:p<.001;
BF
inclusion
>100; TTA: p= .001; BF
inclusion
>100), not MM
spell
(Arithmetic
acc
:p= .41; BF
inclu-
sion
= 0.18; TTA: p= .10; BF
inclusion
= 1.36). On the other hand, when MM
arith
and MM
spell
were used to predict spelling performance, only MM
spell
was a significant and supported pre-
dictor (Spelling
acc
:p<.001; BF
inclusion
>100; Dictation: p<.001; BF
inclusion
>100), not
MM
arith
(Spelling
acc
:p= .38; BF
inclusion
= .06; Dictation: p= .61; BF
inclusion
= .24).
Interim discussion
The results of Study 1 reveal that within-domain metacognitive monitoring was an important
predictor of both arithmetic and spelling performance. Monitoring measures in both domains
Table 2. Regression analyses of MM
arith
and MM
spell
performance with metacognitive monitoring in the other
domain and standardized task performance in both domains as predictors.
MM
arith
βt p BF
inclusion
Intellectual ability .16 2.12 .04 2.90
TTA .26 3.62 <.001 72.57
Dictation -.14 -1.50 .14 1.46
MM
spell
.51 5.26 <.001 >100
MM
spell
βt p BF
inclusion
Intellectual ability .07 1.07 .29 0.38
Dictation .55 8.77 <.001 >100
TTA .01 0.13 .90 0.25
MM
arith
.34 5.26 <.001 >100
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were highly correlated and predictive of one another, even after controlling for intellectual
ability and performance on both academic tasks. Both monitoring measures correlated with
performance in the other academic domain, ever after controlling for performance within the
domain (e.g., significant correlation of MM
arith
with spelling performance, controlled for
arithmetic performance). When monitoring within the domain was added above monitoring
across-domain, only monitoring within the domain remained a significant predictor of aca-
demic performance. Taken together, these results provide substantial evidence for domain-
generality of metacognitive monitoring in academic domains in 8-9-year-olds, in addition to
the importance of some degree of domain-specificity in monitoring skills.
These results leave the question of whether this domain-generality is the result of a shift
(e.g., [19]) in early primary school unanswered. One possibility is that the 8-9-year-olds already
went through an important transition regarding domain-generality of metacognitive monitor-
ing, but that such domain-generality is not observed at younger ages. On the other hand, it is
possible that no shift to domain-generality has occurred because also at a younger age, domain-
generality can be observed. To test this, we additionally recruited a new sample of children that
were one year younger, i.e. 7-8-year-olds (Study 2). The same research questions as in Study 1
were studied using the exact same paradigm. This allowed us to test whether domain-generality
is already observed at younger ages or not.
Study 2: Metacognitive monitoring in arithmetic and spelling in 7-
8-year-olds (second grade)
Methods
Participants. Participants were 77 typically developing Flemish 7–8 year-olds (second
grade; 49 girls; M
age
= 7 years, 8 months; SD = 4 months; [7 years 1 month—8 years 8
months]), without a diagnosis of a developmental disorder, and who came from a dominantly
middle-to-high socio-economic background. For every participant, written informed parental
consent was obtained.
Procedure. The procedure was the same as in Study 1.
Materials. Materials were the same as in Study 1. The items in the custom arithmetic and
spelling tasks were adapted from Study 1 to be age appropriate for second graders. Namely, for
arithmetic, only single-digit addition was administered (n= 30); for spelling only two specific
Dutch spelling rules were used (i.e., extension rule and to be retrieved words with diphthongs;
n= 30). The standardized arithmetic task was exactly the same as in Study 1. As for the
Table 3. Regression analyses of arithmetic performance (i.e., arithmetic
acc
and TTA) and spelling performance (i.e., spelling
acc
and dictation) with metacognitive
monitoring in the other domain and standardized task performance in the other domain as predictors.
Arithmetic
Arithmetic
acc
TTA
βt p BF
inclusion
βt p BF
inclusion
MM
spell
.54 5.18 <.001 5.03 .24 2.11 .04 >100
Dictation -.06 -.54 .59 2.07 .19 1.73 .09 0.37
Spelling
Spelling
acc
Dictation
βt p BF
inclusion
βt p BF
inclusion
MM
arith
.47 5.89 <.001 >100 .23 2.66 .009 10.84
TTA .12 1.46 .15 0.86 .25 2.95 .004 23.59
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standardized dictation, we administered the subtest for children in the middle of second
grade, which includes age-appropriate, curriculum-based items [38] (n= 42).
Data analysis. For this follow-up study, we carried out the same analyses as preregistered
for Study 1 (https://osf.io/ypue4/?view_only=ce9f97af0e3149c28942a43499eafd32). The same
exclusion criteria for data as in Study 1 were applied. Less than 4% of the data per task was
excluded as an outlier; less than 0.90% of the items per task were excluded as a measurement
error.
Results
The descriptive statistics of all measures are presented in S1 Appendix. Additionally, Pearson
correlation coefficients of all variables under study were calculated (S2 Appendix). Although
not originally pre-registered, we additionally re-calculated all analyses below with chronologi-
cal age as an additional control variable. Considering chronological age within grade in the
analyses reported below did not change the interpretation of the results (S3 Appendix).
The role of metacognitive monitoring in arithmetic and spelling performance. Pearson
correlation coefficients of the associations between metacognitive monitoring and the aca-
demic skills are presented in Table 4.
MM
arith
was significantly correlated with all three arithmetic performance measures. Bayes
factors indicate that the evidence for an association with Arithmetic
acc
and the TTA is decisive,
while there is only anecdotal evidence for an association with Arithmetic
rt
.
MM
spell
was significantly correlated with both Spelling
acc
and dictation, with Bayes factors
indicating moderate to decisive evidence for an association. There was no significant correla-
tion with Spelling
rt
and the Bayes factor indicated moderate evidence in favour of no
association.
Based on the same rationale as Study 1, the response time performance measures were not
considered in further analyses.
Domain-specificity of the role of metacognitive monitoring. MM
arith
and MM
spell
were
not significantly correlated after controlling for intellectual ability (r= .14, p= .28). The Bayes
factor indicated there was moderate evidence in favour for no association (BF
10
= 0.28).
Hence, further control analyses (i.e., in line with Study 1 in which the correlation between
MM
arith
and MM
spell
was also controlled for performance on the TTA and Dictation) were not
performed.
Table 4 shows cross-domain correlations between academic performance and metacog-
nitive monitoring in the other domain. MM
arith
was not significantly correlated with any
of the spelling performance measures. Bayes factors indicated moderate evidence in
favour of no association. MM
spell
was not significantly related to any of the arithmetic
measures. Bayes factors indicated anecdotal to moderate evidence in favour of no
association.
Interim discussion
The results of Study 2 revealed that within-domain metacognitive monitoring was an impor-
tant predictor of both arithmetic and spelling performance. Monitoring measures in both
domains were not correlated, and both monitoring measures did not correlate with perfor-
mance in the other academic domain. These results provide substantial evidence for domain-
specificity of metacognitive monitoring in academic domains in 7-8-year-olds (second grad-
ers). No domain-general effect of metacognitive monitoring was observed, in contrast to the 8-
9-year-olds (third grade children; Study 1).
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General discussion
Two recent studies found evidence for within-domain metacognitive monitoring as an impor-
tant predictor of arithmetic [5,6]. One outstanding question is whether these results regarding
the role of metacognitive monitoring in arithmetic are specific to the arithmetic domain, or
whether they are reflective of a more general role of metacognitive monitoring in academic per-
formance. This study adds to the existing literature in an important way by (a) investigating
metacognitive monitoring in two related, yet distinct academic domains, (b) studying whether
monitoring in one domain was associated with and predictive of monitoring in the other domain
(and vice versa), and (c) studying whether monitoring in one domain was associated with and
predictive of performance in the other domain (and vice versa), and importantly by (d) doing
this in two important age groups, namely children aged 8–9 (Study 1) and 7–8 (Study 2), who
are in an important developmental phase for both academic performance and metacognition,
using the exact same paradigm in both age groups and both domains.
Our results reveal that within-domain metacognitive monitoring was an important predic-
tor of both arithmetic and spelling performance in both 8-9-year-olds (Study 1) and 7-8-year-
olds (Study 2). Although metacognitive monitoring in arithmetic and spelling were highly cor-
related and predictive of one another in 8-9-year-olds (Study 1), they were not in younger 7-
8-year-old children (Study 2). In 8-9-year-olds, but not in 7-8-year-olds, both monitoring
measures correlated with performance in the other academic domain, even after controlling
for performance within the domain (e.g., significant correlation of MM
arith
with spelling per-
formance, controlled for arithmetic performance). These results provide evidence for the
emergence of domain-generality of metacognitive monitoring between second and third
grade (i.e., 7-9-year-olds).
Our results nicely replicate associations between metacognitive monitoring and academic
performance (e.g., [5,6,11,41,42]). Combining the data of both studies, we are able to confirm
the theoretically assumed development of metacognition from highly domain- and situation-
specific to more flexible and domain-general with practice and experience [43]. Our results
regarding a possible underlying domain-general element of metacognitive monitoring in mid-
dle primary school children (8-9-year-olds) are in line with the existing literature in older ages
and/or other domains (e.g., [19–21]). For example, Schraw and colleagues [20,22] and
Table 4. Correlation analyses of metacognitive monitoring and academic performance measures in 7-8-year-olds (Grade 2).
Arithmetic Spelling
Custom task–
Accuracy
a
Custom task—
RT
b
Standardized task
(TTA)
a
Custom task—
Accuracy
a
Custom task
-RT
b
Standardized task
(dictation)
a
Metacognitive
Monitoring
Arithmetic
r.74 .30 .47 .11 .06 .20
p<.001 .02 <.001 .38 .66 .11
BF
10
>100 2.60 >100 0.23 0.17 0.53
Spelling
r.03 .11 .05 .89 .03 .32
p.84 .40 .69 <.001 .82 .01
BF
10
0.16 0.11 0.17 >100 0.16 4.12
a
Controlled for intellectual ability.
b
Controlled for intellectual ability and motor speed on the keyboard.
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Veenman and colleagues [21] found evidence for domain-generality of metacognitive moni-
toring in adults; Geurten et al. [19] observed a shift to domain-general metacognition between
8 and 13 across the arithmetic and memory domain. Our results also show the importance of
domain-specific knowledge for metacognitive performance, as was previously found in non-
academic domains (i.e., soccer) by for example Lo¨ffler and colleagues [26], in very young chil-
dren by Vo and colleagues [25], and in 12-year-olds in mathematics by Lingel and colleagues
[28]. Our results add to this body of research that, domain-generality of metacognitive moni-
toring emerges between the ages of 7-to-9, yet that domain-specific knowledge and skills
remain important for metacognitive monitoring, even in highly related academic domains.
Schraw and colleagues [20] note that when performance is correlated among domains (i.e.,
as they were in Study 1), correlated metacognitive monitoring scores (i.e., as they were in Study
1) pose no serious threat to the assumption that monitoring is domain-specific. However,
when they are correlated after removing the variation attributable to performance scores, as we
did using partial correlations and regression analyses, this outcome cannot be explained on the
basis of domain-specific knowledge and a domain-general argument needs to be invoked. As
both monitoring performances remained significantly correlated after removing the variation
attributable to performance scores, our results indicate that in 8-9-year-olds (Study 1) there
might be an underlying domain-general element of metacognition within both metacognitive
monitoring scores. This was not observed in 7-8-year-olds (Study 2). All in all, these results
point to the emergence of domain-generality of metacognitive monitoring in between second
(7–8 yo) and third (8–9 yo) grade of primary school.
Our results still provide some evidence for a domain-specific element of metacognitive
monitoring in 8-9-year-olds. Although metacognitive monitoring across-domain was an
important predictor of performance, the associations with monitoring within-domain were
significantly larger than with monitoring across-domain. Once monitoring within a domain
was considered, the predictive power of monitoring across-domain was no longer significant/
supported. These results suggest the continuing importance of domain-specific knowledge
and skills. This domain-specific element could explain the additional predictive power of mon-
itoring within-domain in addition to metacognitive monitoring across-domain.
Based on the important role that metacognitive monitoring was found to have in arithmetic
performance [5,6], the current study investigated the domain-specificity question of metacog-
nition by also including spelling performance. We deliberately included a different, yet corre-
lated skill within the academic domain to thoroughly investigate the extent to which
metacognition might be domain-specific. This is different from existing research, where the
domain-specificity question was investigated in very distant domains. For example, Vo and col-
leagues [25] investigated domain-specificity in the numerical domain versus emotion discrimi-
nation. The use of spelling next to arithmetic made it possible to use the exact same paradigm
to measure metacognitive monitoring and maximize the comparability of the two tasks. The
fact that the computerized tasks for arithmetic and spelling were specifically designed to be as
similar as possible, minimized the possibility that the results on domain-specificity of metacog-
nition were due to differences in paradigms. By including standardized arithmetic and spelling
tasks, which are not as similar to each other and measure performance in an ecologically valid
way, we minimized the possibility that the results on domain-generality of metacognition were
due to similarities in paradigms. While there is substantial evidence in the current studies for
the emergence of domain-general metacognitive monitoring processes, the results also indicate
that, even in highly related domains, domain-specific knowledge and skills are important for
metacognitive monitoring in primary school children.
Although the custom arithmetic and spelling task were designed with age-appropriate
items, a slight difference in task difficulty was present, with the computerized spelling tasks
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being more difficult than the arithmetic tasks. Schraw and colleagues [20] pointed out that
task difficulty, as a characteristic of the test environment, might have an important influence
on metacognitive monitoring. They found that, with different task difficulty levels, metacogni-
tive monitoring in adults was mostly domain-specific, yet, once tests were matched on test
characteristics, monitoring was mostly domain-general. To make sure our results were not
influenced by this slight difference in task difficulty, we selected, post-hoc, a subset of items
per task (n= 40 for Study 1; n= 20 for Study 2) that were matched on task difficulty (i.e., t-test
comparing accuracy in arithmetic and spelling selection: Study 1: t(138) = 0.12, p= .91; Study
2: t(71) = 0.36, p= .72). These post-hoc exploratory results show that our findings on metacog-
nitive monitoring and its specificity did not change when restricting the analyses to those
items that were matched in task difficulty.
Performance measures of arithmetic and spelling were accuracy in the computerized tasks,
and widely-used, standardized pen-and-paper tasks. As accuracy data were a fundamental part
of our metacognitive monitoring scoring, in the interpretation of the results, the largest focus
should be on the standardized measures, as metacognitive monitoring was measured indepen-
dently from these measures. The computerized and the standardized tasks were both age-
appropriate measures, yet the standardized tasks focused less on specific items of the curricu-
lum (i.e., only single-digit arithmetic in the computerized arithmetic task; only specific Dutch
spelling rules in the computerized spelling task), for which reason they were more wide-ranged
and valid measures of children’s arithmetic and spelling skills. The standardized tasks were the
most ecologically valid measures, assessing arithmetic and spelling performance as they are
assessed in the classroom. Including these standardized tasks in the design is an essential asset
of this study compared to the existing literature (e.g., [5,6]), as we were able to generalize our
results from the role of metacognitive monitoring within the task, to within the domain, inde-
pendently from the task in which monitoring was measured.
Although the driving mechanisms for the gradual development from domain-specificity to
domain-generality of metacognitive monitoring cannot be determined on the basis of the cur-
rent study, it is important to reflect on why metacognition shifts to being more domain-gen-
eral around the ages 8–9. The existing literature offers some theoretical possibilities, albeit
speculatively, that should be investigated in future research.
The development from more domain-specificity of metacognitive monitoring towards
more domain-generality in this age group is likely reflective of a gradual transition that occurs
in the development of primary school children (e.g., [33]). In early stages of this development,
children’s metacognitive monitoring might still be highly dependent on the (characteristics of
the) specific stimuli, while over development, through experiences of failure and success, and
with practice in assessing one’s performance as well as in (academic) tasks, monitoring might
become more generic. These hypotheses and our results can also be interpreted within the
dual-process framework of metacognition (e.g., [44–46]), which Geurten et al. [19] used to
interpret their findings. According to this dual-process framework of metacognition [44–46],
metacognitive judgments can, on the one hand, be experience-based, i.e., based on fast and
automatic inferences made from a variety of cues that reside from immediate feedback from
the task and that are then heuristically used to guide decisions. As such, these metacognitive
judgments are task-dependent and probably difficult to generalize across domains. On the
other hand, metacognitive judgments can be information-based, i.e., based on conscious and
deliberate inferences, in which various pieces of information retrieved from memory are con-
sulted and weighted in order to reach an advised judgment. These conscious and effortful
judgments are more likely to generalize to other domains. Taken together with the current
results, this dual-processing model of metacognition may suggest that 7–8 year-old (second
grade) children preferentially rely on automatic inferences when making judgments, while
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improvements of metacognitive abilities may enable 8–9 year-old children (third grade) to rely
more often on conscious and deliberate information-based processes.
Another explanation for the gradual shift from domain-specificity to domain-generality of
metacognition could be that this development might be associated with the development in
other general cognitive functions, such as working memory capacity or intellectual ability. For
example, Veenman and colleagues [47] found that metacognitive skills develop alongside, but
not entirely as part of intellectual ability. Growth in these other general cognitive functions
might enable a shift from domain-specificity to domain-generality of metacognition.
Finally, the development from domain-specificity towards domain-generality might also be
driven by education, as teachers instruct children on assessing their own performance, which
is at first very focussed on specific tasks. Over development, children might internalise this
into a semantic network of their own abilities, which in turn might generalise to other tasks
and thus become more general.
It is essential to note that none of the above-mentioned hypotheses can be empirically eval-
uated within the current study. The focus of the current study was on whether a development
toward domain-generality in metacognitive monitoring occurs in primary school children, in
related academic domains, and, secondly when this occurs. The question on how, i.e., what
mechanisms lie behind this, and why this is the case at this age, are important questions for
future research.
Future research should also examine the question of domain-specificity of metacognition
longitudinally, investigating the potential shift from domain-specificity to domain-generality
in the same group of primary school children. Such a research design will allow one to investi-
gate the directions of the associations between metacognition and academic performance and
how these associations evolve over time. Furthermore, brain-imaging research in children
could be very useful to investigate the question of domain-specificity of metacognition, by, for
example, testing whether metacognitive abilities for different types of tasks (partially) depend
on common neural structures such as the prefrontal cortex, as has been observed in adults
(e.g., [23]).
To conclude, the results of this study show that metacognitive monitoring of performance
is an important predictor of academic skills in primary school children. While in young pri-
mary school children (7-8-year-olds), this process is domain-specific, in slightly older children
(8-9-year-olds), this is a predominantly domain-general process, in which metacognitive mon-
itoring of performance is an important predictor of academic skills independently of the aca-
demic task and domain it is measured in, even in highly related domains. Besides depending
on domain-general metacognitive processes, metacognitive monitoring remains to be depen-
dent of domain-specific performance and knowledge. Knowing whether metacognition is
rather domain-specific or domain-general, and when domain-generality emerges, is of impor-
tance for educators, as this might impact on how they provide instructions in metacognitive
monitoring, namely for each task or domain separately (i.e., domain-specific metacognition)
or concurrently in different tasks and domains (expecting it to transfer to new domains;
domain-general metacognition).
Supporting information
S1 Appendix. Descriptive statistics.
(DOCX)
S2 Appendix. All intercorrelations.
(DOCX)
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S3 Appendix. Analyses with chronological age.
(DOCX)
S1 File.
(DOCX)
Acknowledgments
We would like to thank all children and schools for their participation.
Author Contributions
Conceptualization: Elien Bellon, Wim Fias, Bert De Smedt.
Data curation: Elien Bellon.
Formal analysis: Elien Bellon.
Funding acquisition: Elien Bellon, Wim Fias, Bert De Smedt.
Investigation: Elien Bellon, Wim Fias, Bert De Smedt.
Methodology: Elien Bellon, Wim Fias, Bert De Smedt.
Project administration: Elien Bellon.
Resources: Wim Fias, Bert De Smedt.
Software: Elien Bellon.
Supervision: Wim Fias, Bert De Smedt.
Validation: Elien Bellon, Wim Fias, Bert De Smedt.
Visualization: Elien Bellon.
Writing – original draft: Elien Bellon.
Writing – review & editing: Elien Bellon, Wim Fias, Bert De Smedt.
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