Strategies for teaching metacognition in classrooms

Subscribe to the center for universal education bulletin, david owen and do david owen history and politics teacher - melbourne high school - victoria, australia alvin vista alvin vista former brookings expert @alvin_vista.

November 15, 2017

This is the third piece in a six-part  blog series  on  teaching 21st century skills , including  problem solving ,  metacognition , critical thinking , and collaboration , in classrooms.

Metacognition is thinking about thinking. It is an increasingly useful mechanism to enhance student learning, both for immediate outcomes and for helping students to understand their own learning processes. So metacognition is a broad concept that refers to the knowledge and thought processes regarding one’s own learning. Importantly, there is research evidence (e.g., Moely and colleagues, 1995 ; Schraw, 1998 ) that metacognition is a teachable skill that is central to other skills sets such as problem solving, decisionmaking, and critical thinking. Reflective thinking, as a component of metacognition, is the ability to reflect critically on learning experiences and processes in order to inform future progress.

David Owen, who teaches history and politics at Melbourne High School in Victoria, Australia, discusses a simple but effective approach to encourage student self-reflection:

I have rethought some of my classroom strategies this year. I teach at a secondary school which prides itself on its high level of student achievement, and I had always believed my students performed accordingly. They always ask for help before, during, and after class. Their varied queries could be superficial knowledge-based questions or more general questions about their progress, but I’d always read this habit as a sign that my students had an open mindset: they were inquisitive, cared about their learning, and charted their progress.

But having students asking a million questions of the teacher poses another challenge entirely, which can be framed: “Why aren’t students asking these questions of themselves?”

Recent shifts in pedagogy have emphasized the importance of encouraging students to figure out how to be independent, self-regulated learners. The teacher cannot be there to hold their hand beyond school. This demands that students reflect on their learning in meaningful ways. It also requires students be critical analysts of their own thinking in order to overcome complex or unexpected problems.

I’ve begun to highlight strategies which might better encourage this kind of metacognition. For younger adolescents, I’ve found that “Exit Tickets” are an opportunity for students to reflect on what they have accomplished and what they could improve on. Exit Tickets are a family of feedback tools that students complete for a few minutes at the end of each lesson. They prompt students to think about how and what they learn, as well as what challenges they are still facing.

“Traffic Lights” is a simple yet effective Exit Ticket which emphasizes three key factors:

  • When students encountered a challenge;
  • When students had thought differently about something; and
  • When students were learning well.

Over time I’ve found myself more interested in student responses to the Yellow Light, because it requires students to think about how they were thinking, rather than when (the emphasis of the Red and Green lights). The Yellow Light encourages reflective thinking as well as “thinking about thinking, or what is known as metacognition.” The possibilities for Exit Tickets are numerous and easily adaptable to the content and specific skills taught in any lesson.

Another example of reflective, self-directed learning which is suited to group work is setting a classroom rule that groups ask a question together , rather than individually. What this means is that rather than immediately ask the teacher for help, a student who has encountered a problem must consult with their group first. If the group cannot collectively find the solution, they can raise their hands simultaneously—a sign that the question has been fielded to the group already. There are various ways to modify this rule: highlighting with traffic-light colors, like in the Exit Ticket activity, is one such example.

For older students, setting a few rules before requesting aid from the teacher has seen their self-directed learning—and my feedback—improve markedly. I have emphasized that students seek specific feedback concerning their trial exams. I ask students to ensure they have highlighted and annotated their responses before seeing me. This approach shifts student thinking from the simplistic “submission to feedback” principle towards a more involved process, where students must consider what feedback they would want, what advice they would give themselves, and where they think they need to improve. This approach encourages the students to independently exercise control over their learning and progress, thereby making them more independent and self-directed learners.

Evidence supporting the impact of metacognition suggests that students applying metacognitive strategies to learning tasks outperform those who do not ( Mason, Boldrin & Ariasi, 2010 ; Dignath & Buettner, 2008 ). The classroom approaches that David Owen uses in his classroom demonstrates one way of developing parts of this important complex skill. Interestingly, although these skills are so important in our modern world, the approaches discussed here are practical, do not require 21st century technology or resources, and can be applied in almost any classroom setting.

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  • Review Article
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  • Published: 08 June 2021

Metacognition: ideas and insights from neuro- and educational sciences

  • Damien S. Fleur   ORCID: 1 , 2 ,
  • Bert Bredeweg   ORCID: 1 , 3 &
  • Wouter van den Bos 2 , 4  

npj Science of Learning volume  6 , Article number:  13 ( 2021 ) Cite this article

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  • Human behaviour
  • Interdisciplinary studies

Metacognition comprises both the ability to be aware of one’s cognitive processes (metacognitive knowledge) and to regulate them (metacognitive control). Research in educational sciences has amassed a large body of evidence on the importance of metacognition in learning and academic achievement. More recently, metacognition has been studied from experimental and cognitive neuroscience perspectives. This research has started to identify brain regions that encode metacognitive processes. However, the educational and neuroscience disciplines have largely developed separately with little exchange and communication. In this article, we review the literature on metacognition in educational and cognitive neuroscience and identify entry points for synthesis. We argue that to improve our understanding of metacognition, future research needs to (i) investigate the degree to which different protocols relate to the similar or different metacognitive constructs and processes, (ii) implement experiments to identify neural substrates necessary for metacognition based on protocols used in educational sciences, (iii) study the effects of training metacognitive knowledge in the brain, and (iv) perform developmental research in the metacognitive brain and compare it with the existing developmental literature from educational sciences regarding the domain-generality of metacognition.


Metacognition is defined as “thinking about thinking” or the ability to monitor and control one’s cognitive processes 1 and plays an important role in learning and education 2 , 3 , 4 . For instance, high performers tend to present better metacognitive abilities (especially control) than low performers in diverse educational activities 5 , 6 , 7 , 8 , 9 . Recently, there has been a lot of progress in studying the neural mechanisms of metacognition 10 , 11 , yet it is unclear at this point how these results may inform educational sciences or interventions. Given the potential benefits of metacognition, it is important to get a better understanding of how metacognition works and of how training can be useful.

The interest in bridging cognitive neuroscience and educational practices has increased in the past two decades, spanning a large number of studies grouped under the umbrella term of educational neuroscience 12 , 13 , 14 . With it, researchers have brought forward issues that are viewed as critical for the discipline to improve education. Recurring issues that may impede the relevance of neural insights for educational practices concern external validity 15 , 16 , theoretical discrepancies 17 and differences in terms of the domains of (meta)cognition operationalised (specific or general) 15 . This is important because, in recent years, brain research is starting to orient itself towards training metacognitive abilities that would translate into real-life benefits. However, direct links between metacognition in the brain and metacognition in domains such as education have still to be made. As for educational sciences, a large body of literature on metacognitive training is available, yet we still need clear insights about what works and why. While studies suggest that training metacognitive abilities results in higher academic achievement 18 , other interventions show mixed results 19 , 20 . Moreover, little is known about the long-term effects of, or transfer effects, of these interventions. A better understanding of the cognitive processes involved in metacognition and how they are expressed in the brain may provide insights in these regards.

Within cognitive neuroscience, there has been a long tradition of studying executive functions (EF), which are closely related to metacognitive processes 21 . Similar to metacognition, EF shows a positive relationship with learning at school. For instance, performance in laboratory tasks involving error monitoring, inhibition and working memory (i.e. processes that monitor and regulate cognition) are associated with academic achievement in pre-school children 22 . More recently, researchers have studied metacognition in terms of introspective judgements about performance in a task 10 . Although the neural correlates of such behaviour are being revealed 10 , 11 , little is known about how behaviour during such tasks relates to academic achievement.

Educational and cognitive neuroscientists study metacognition in different contexts using different methods. Indeed, while the latter investigate metacognition via behavioural task, the former mainly rely on introspective questionnaires. The extent to which these different operationalisations of metacognition match and reflect the same processes is unclear. As a result, the external validity of methodologies used in cognitive neuroscience is also unclear 16 . We argue that neurocognitive research on metacognition has a lot of potential to provide insights in mechanisms relevant in educational contexts, and that theoretical and methodological exchange between the two disciplines can benefit neuroscientific research in terms of ecological validity.

For these reasons, we investigate the literature through the lenses of external validity, theoretical discrepancies, domain generality and metacognitive training. Research on metacognition in cognitive neuroscience and educational sciences are reviewed separately. First, we investigate how metacognition is operationalised with respect to the common framework introduced by Nelson and Narens 23 (see Fig. 1 ). We then discuss the existing body of evidence regarding metacognitive training. Finally, we compare findings in both fields, highlight gaps and shortcomings, and propose avenues for research relying on crossovers of the two disciplines.

figure 1

Meta-knowledge is characterised as the upward flow from object-level to meta-level. Meta-control is characterised as the downward flow from meta-level to object-level. Metacognition is therefore conceptualised as the bottom-up monitoring and top-down control of object-level processes. Adapted from Nelson and Narens’ cognitive psychology model of metacognition 23 .

In cognitive neuroscience, metacognition is divided into two main components 5 , 24 , which originate from the seminal works of Flavell on metamemory 25 , 26 . First, metacognitive knowledge (henceforth, meta-knowledge) is defined as the knowledge individuals have of their own cognitive processes and their ability to monitor and reflect on them. Second, metacognitive control (henceforth, meta-control) consists of someone’s self-regulatory mechanisms, such as planning and adapting behaviour based on outcomes 5 , 27 . Following Nelson and Narens’ definition 23 , meta-knowledge is characterised as the flow and processing of information from the object level to the meta-level, and meta-control as the flow from the meta-level to the object level 28 , 29 , 30 (Fig. 1 ). The object-level encompasses cognitive functions such as recognition and discrimination of objects, decision-making, semantic encoding, and spatial representation. On the meta-level, information originating from the object level is processed and top-down regulation on object-level functions is imposed 28 , 29 , 30 .

Educational researchers have mainly investigated metacognition through the lens of Self-Regulated Learning theory (SRL) 3 , 4 , which shares common conceptual roots with the theoretical framework used in cognitive neuroscience but varies from it in several ways 31 . First, SRL is constrained to learning activities, usually within educational settings. Second, metacognition is merely one of three components, with “motivation to learn” and “behavioural processes”, that enable individuals to learn in a self-directed manner 3 . In SRL, metacognition is defined as setting goals, planning, organising, self-monitoring and self-evaluating “at various points during the acquisition” 3 . The distinction between meta-knowledge and meta-control is not formally laid down although reference is often made to a “self-oriented feedback loop” describing the relationship between reflecting and regulating processes that resembles Nelson and Narens’ model (Fig. 1 ) 3 , 23 . In order to facilitate the comparison of operational definitions, we will refer to meta-knowledge in educational sciences when protocols operationalise self-awareness and knowledge of strategies, and to meta-control when they operationalise the selection and use of learning strategies and planning. For an in-depth discussion on metacognition and SRL, we refer to Dinsmore et al. 31 .

Metacognition in cognitive neuroscience

Operational definitions.

In cognitive neuroscience, research in metacognition is split into two tracks 32 . One track mainly studies meta-knowledge by investigating the neural basis of introspective judgements about one’s own cognition (i.e., metacognitive judgements), and meta-control with experiments involving cognitive offloading. In these experiments, subjects can perform actions such as set reminders, making notes and delegating tasks 33 , 34 , or report their desire for them 35 . Some research has investigated how metacognitive judgements can influence subsequent cognitive behaviour (i.e., a downward stream from the meta-level to the object level), but only one study so far has explored how this relationship is mapped in the brain 35 . In the other track, researchers investigate EF, also referred to as cognitive control 30 , 36 , which is closely related to metacognition. Note however that EF are often not framed in metacognitive terms in the literature 37 (but see ref. 30 ). For the sake of concision, we limit our review to operational definitions that have been used in neuroscientific studies.

Metacognitive judgements

Cognitive neuroscientists have been using paradigms in which subjects make judgements on how confident they are with regards to their learning of some given material 10 . These judgements are commonly referred to as metacognitive judgements , which can be viewed as a form of meta-knowledge (for reviews see Schwartz 38 and Nelson 39 ). Historically, researchers mostly resorted to paradigms known as Feelings of Knowing (FOK) 40 and Judgements of Learning (JOL) 41 . FOK reflect the belief of a subject to knowing the answer to a question or a problem and being able to recognise it from a list of alternatives, despite being unable to explicitly recall it 40 . Here, metacognitive judgement is thus made after retrieval attempt. In contrast, JOL are prospective judgements during learning of one’s ability to successfully recall an item on subsequent testing 41 .

More recently, cognitive neuroscientists have used paradigms in which subjects make retrospective metacognitive judgements on their performance in a two-alternative Forced Choice task (2-AFC) 42 . In 2-AFCs, subjects are asked to choose which of two presented options has the highest criterion value. Different domains can be involved, such as perception (e.g., visual or auditory) and memory. For example, subjects may be instructed to visually discriminate which one of two boxes contains more dots 43 , identify higher contrast Gabor patches 44 , or recognise novel words from words that were previously learned 45 (Fig. 2 ). The subjects engage in metacognitive judgements by rating how confident they are relative to their decision in the task. Based on their responses, one can evaluate a subject’s metacognitive sensitivity (the ability to discriminate one’s own correct and incorrect judgements), metacognitive bias (the overall level of confidence during a task), and metacognitive efficiency (the level of metacognitive sensitivity when controlling for task performance 46 ; Fig. 3 ). Note that sensitivity and bias are independent aspects of metacognition, meaning that two subjects may display the same levels of metacognitive sensitivity, but one may be biased towards high confidence while the other is biased towards low confidence. Because metacognitive sensitivity is affected by the difficulty of the task (one subject tends to display greater metacognitive sensitivity in easy tasks than difficult ones and different subjects may find a task more or less easy), metacognitive efficiency is an important measure as it allows researchers to compare metacognitive abilities between subjects and between domains. The most commonly used methods to assess metacognitive sensitivity during retrospective judgements are the receiver operating curve (ROC) and meta- d ′. 46 Both derive from signal detection theory (SDT) 47 which allows Type 1 sensitivity, or d’ ′ (how a subject can discriminate between stimulus alternatives, i.e. object-level processes) to be differentiated from metacognitive sensitivity (a judgement on the correctness of this decision) 48 . Importantly, only comparing meta- d ′ to d ′ seems to give reliable assessments metacognitive efficiency 49 . A ratio of 1 between meta- d’ ′ and d’ ′, indicates that a subject was perfectly able to discriminate between their correct and incorrect judgements. A ratio of 0.8 suggests that 80% of the task-related sensory evidence was available for the metacognitive judgements. Table 1 provides an overview of the different types of tasks and protocols with regards to the type of metacognitive process they operationalise. These operationalisations of meta-knowledge are used in combination with brain imaging methods (functional and structural magnetic resonance imaging; fMRI; MRI) to identify brain regions associated with metacognitive activity and metacognitive abilities 10 , 50 . Alternatively, transcranial magnetic stimulation (TMS) can be used to temporarily deactivate chosen brain regions and test whether this affects metacognitive abilities in given tasks 51 , 52 .

figure 2

a Visual perception task: subjects choose the box containing the most (randomly generated) dots. Subjects then rate their confidence in their decision. b Memory task: subjects learn a list of words. In the next screen, they have to identify which of two words shown was present on the list. The subjects then rate their confidence in their decision.

figure 3

The red and blue curves represent the distribution of confidence ratings for incorrect and correct trials, respectively. A larger distance between the two curves denotes higher sensitivity. Displacement to the left and right denote biases towards low confidence (low metacognitive bias) and high confidence (high metacognitive bias), respectively (retrieved from Fig. 1 in Fleming and Lau 46 ). We repeat the disclaimer of the original authors that this figure is not a statistically accurate description of correct and incorrect responses, which are typically not normally distributed 46 , 47 .

A recent meta-analysis analysed 47 neuroimaging studies on metacognition and identified a domain-general network associated with high vs. low confidence ratings in both decision-making tasks (perception 2-AFC) and memory tasks (JOL, FOK) 11 . This network includes the medial and lateral prefrontal cortex (mPFC and lPFC, respectively), precuneus and insula. In contrast, the right anterior dorsolateral PFC (dlPFC) was specifically involved in decision-making tasks, and the bilateral parahippocampal cortex was specific to memory tasks. In addition, prospective judgements were associated with the posterior mPFC, left dlPFC and right insula, whereas retrospective judgements were associated with bilateral parahippocampal cortex and left inferior frontal gyrus. Finally, emerging evidence suggests a role of the right rostrolateral PFC (rlPFC) 53 , 54 , anterior PFC (aPFC) 44 , 45 , 55 , 56 , dorsal anterior cingulate cortex (dACC) 54 , 55 and precuneus 45 , 55 in metacognitive sensitivity (meta- d ′, ROC). In addition, several studies suggest that the aPFC relates to metacognition specifically in perception-related 2-AFC tasks, whereas the precuneus is engaged specifically in memory-related 2-AFC tasks 45 , 55 , 56 . This may suggest that metacognitive processes engage some regions in a domain-specific manner, while other regions are domain-general. For educational scientists, this could mean that some domains of metacognition may be more relevant for learning and, granted sufficient plasticity of the associated brain regions, that targeting them during interventions may show more substantial benefits. Note that rating one’s confidence and metacognitive sensitivity likely involve additional, peripheral cognitive processes instead of purely metacognitive ones. These regions are therefore associated with metacognition but not uniquely per se. Notably, a recent meta-analysis 50 suggests that domain-specific and domain-general signals may rather share common circuitry, but that their neural signature varies depending on the type of task or activity, showing that domain-generality in metacognition is complex and still needs to be better understood.

In terms of the role of metacognitive judgements on future behaviour, one study found that brain patterns associated with the desire for cognitive offloading (i.e., meta-control) partially overlap with those associated with meta-knowledge (metacognitive judgements of confidence), suggesting that meta-control is driven by either non-metacognitive, in addition to metacognitive, processes or by a combination of different domain-specific meta-knowledge processes 35 .

Executive function

In EF, processes such as error detection/monitoring and effort monitoring can be related to meta-knowledge while error correction, inhibitory control, and resource allocation can be related to meta-control 36 . To activate these processes, participants are asked to perform tasks in laboratory settings such as Flanker tasks, Stroop tasks, Demand Selection tasks and Motion Discrimination tasks (Fig. 4 ). Neural correlates of EF are investigated by having subjects perform such tasks while their brain activity is recorded with fMRI or electroencephalography (EEG). Additionally, patients with brain lesions can be tested against healthy participants to evaluate the functional role of the impaired regions 57 .

figure 4

a Flanker task: subjects indicate the direction to which the arrow in the middle points. b Stroop task: subjects are presented with the name of colour printed in a colour that either matches or mismatches the name. Subjects are asked to give the name of the written colour or the printed colour. c Motion Discrimination task: subjects have to determine in which direction the dots are going with variating levels of noise. d Example of a Demand Selection task: in both options subjects have to switch between two tasks. Task one, subjects determine whether the number shown is higher or lower than 5. Task two, subjects determine whether the number is odd or even. The two options (low and high demand) differ in their degree of task switching, meaning the effort required. Subjects are allowed to switch between the two options. Note, the type of task is solely indicated by the colour of the number and that the subjects are not explicitly told about the difference in effort between the two options (retrieved from Fig. 1c in Froböse et al. 58 ).

In a review article on the neural basis of EF (in which they are defined as meta-control), Shimamura argues that a network of regions composed of the aPFC, ACC, ventrolateral PFC (vlPFC) and dlPFC is involved in the regulations of cognition 30 . These regions are not only interconnected but are also intricately connected to cortical and subcortical regions outside of the PFC. The vlPFC was shown to play an important role in “selecting and maintaining information in working memory”, whereas the dlPFC is involved in “manipulating and updating information in working memory” 30 . The ACC has been proposed to monitor cognitive conflict (e.g. in a Stroop task or a Flanker task), and the dlPFC to regulate it 58 , 59 . In particular, activity in the ACC in conflict monitoring (meta-knowledge) seems to contribute to control of cognition (meta-control) in the dlPFC 60 , 61 and to “bias behavioural decision-making toward cognitively efficient tasks and strategies” (p. 356) 62 . In a recent fMRI study, subjects performed a motion discrimination task (Fig. 4c ) 63 . After deciding on the direction of the motion, they were presented additional motion (i.e. post-decisional evidence) and then were asked to rate their confidence in their initial choice. The post-decisional evidence was encoded in the activity of the posterior medial frontal cortex (pMFC; meta-knowledge), while lateral aPFC (meta-control) modulated the impact of this evidence on subsequent confidence rating 63 . Finally, results from a meta-analysis study on cognitive control identified functional connectivity between the pMFC, associated with monitoring and informing other regions about the need for regulation, and the lPFC that would effectively regulate cognition 64 .

Online vs. offline metacognition

While the processes engaged during tasks such as those used in EF research can be considered as metacognitive in the sense that they are higher-order functions that monitor and control lower cognitive processes, scientists have argued that they are not functionally equivalent to metacognitive judgements 10 , 11 , 65 , 66 . Indeed, engaging in metacognitive judgements requires subjects to reflect on past or future activities. As such, metacognitive judgements can be considered as offline metacognitive processes. In contrast, high-order processes involved in decision-making tasks such as used in EF research are arguably largely made on the fly, or online , at a rapid pace and subjects do not need to reflect on their actions to perform them. Hence, we propose to explicitly distinguish online and offline processes. Other researchers have shared a similar view and some have proposed models for metacognition that make similar distinctions 65 , 66 , 67 , 68 . The functional difference between online and offline metacognition is supported by some evidence. For instance, event-related brain potential (ERP) studies suggest that error negativities are associated with error detection in general, whereas an increased error positivity specifically encodes error that subjects could report upon 69 , 70 . Furthermore, brain-imaging studies suggest that the MFC and ACC are involved in online meta-knowledge, while the aPFC and lPFC seem to be activated when subjects engage in more offline meta-knowledge and meta-control, respectively 63 , 71 , 72 . An overview of the different tasks can be found in Table 1 and a list of different studies on metacognition can be found in Supplementary Table 1 (organised in terms of the type of processes investigated, the protocols and brain measures used, along with the brain regions identified). Figure 5 illustrates the different brain regions associated with meta-knowledge and meta-control, distinguishing between what we consider to be online and offline processes. This distinction is often not made explicitly but it will be specifically helpful when building bridges between cognitive neuroscience and educational sciences.

figure 5

The regions are divided into online meta-knowledge and meta-control, and offline meta-knowledge and meta-control following the distinctions introduced earlier. Some regions have been reported to be related to both offline and online processes and are therefore given a striped pattern.

Training metacognition

There are extensive accounts in the literature of efforts to improve EF components such as inhibitory control, attention shifting and working memory 22 . While working memory does not directly reflect metacognitive abilities, its training is often hypothesised to improve general cognitive abilities and academic achievement. However, most meta-analyses found that training methods lead only to weak, non-lasting effects on cognitive control 73 , 74 , 75 . One meta-analysis did find evidence of near-transfer following EF training in children (in particular working memory, inhibitory control and cognitive flexibility), but found no evidence of far-transfer 20 . According to this study, training on one component leads to improved abilities in that same component but not in other EF components. Regarding adults, however, one meta-analysis suggests that EF training in general and working memory training specifically may both lead to significant near- and far-transfer effects 76 . On a neural level, a meta-analysis showed that cognitive training resulted in decreased brain activity in brain regions associated with EF 77 . According to the authors, this indicates that “training interventions reduce demands on externally focused attention” (p. 193) 77 .

With regards to meta-knowledge, several studies have reported increased task-related metacognitive abilities after training. For example, researchers found that subjects who received feedback on their metacognitive judgements regarding a perceptual decision-making task displayed better metacognitive accuracy, not only in the trained task but also in an untrained memory task 78 . Related, Baird and colleagues 79 found that a two-week mindfulness meditation training lead to enhanced meta-knowledge in the memory domain, but not the perceptual domain. The authors link these results to evidence of increased grey matter density in the aPFC in meditation practitioners.

Research on metacognition in cognitive science has mainly been studied through the lens of metacognitive judgements and EF (specifically performance monitoring and cognitive control). Meta-knowledge is commonly activated in subjects by asking them to rate their confidence in having successfully performed a task. A distinction is made between metacognitive sensitivity, metacognitive bias and metacognitive efficacy. Monitoring and regulating processes in EF are mainly operationalised with behavioural tasks such as Flanker tasks, Stroop tasks, Motion Discrimination tasks and Demand Selection tasks. In addition, metacognitive judgements can be viewed as offline processes in that they require the subject to reflect on her cognition and develop meta-representations. In contrast, EF can be considered as mostly online metacognitive processes because monitoring and regulation mostly happen rapidly without the need for reflective thinking.

Although there is some evidence for domain specificity, other studies have suggested that there is a single network of regions involved in all meta-cognitive tasks, but differentially activated in different task contexts. Comparing research on meta-knowledge and meta-control also suggest that some regions play a crucial role in both knowledge and regulation (Fig. 5 ). We have also identified a specific set of regions that are involved in either offline or online meta-knowledge. The evidence in favour of metacognitive training, while mixed, is interesting. In particular, research on offline meta-knowledge training involving self-reflection and metacognitive accuracy has shown some promising results. The regions that show structural changes after training, were those that we earlier identified as being part of the metacognition network. EF training does seem to show far-transfer effects at least in adults, but the relevance for everyday life activity is still unclear.

One major limitation of current research in metacognition is ecological validity. It is unclear to what extent the operationalisations reviewed above reflect real-life metacognition. For instance, are people who can accurately judge their performance on a behavioural task also able to accurately assess how they performed during an exam? Are people with high levels of error regulation and inhibitory control able to learn more efficiently? Note that criticism on the ecological validity of neurocognitive operationalisations extends beyond metacognition research 16 . A solution for improving validity may be to compare operationalisations of metacognition in cognitive neuroscience with the ones in educational sciences, which have shown clear links with learning in formal education. This also applies to metacognitive training.

Metacognition in educational sciences

The most popular protocols used to measure metacognition in educational sciences are self-report questionnaires or interviews, learning journals and thinking-aloud protocols 31 , 80 . During interviews, subjects are asked to answer questions regarding hypothetical situations 81 . In learning journals, students write about their learning experience and their thoughts on learning 82 , 83 . In thinking-aloud protocols, subjects are asked to verbalise their thoughts while performing a problem-solving task 80 . Each of these instruments can be used to study meta-knowledge and meta-control. For instance, one of the most widely used questionnaires, the Metacognitive Awareness Inventory (MAI) 42 , operationalises “Flavellian” metacognition and has dedicated scales for meta-knowledge and meta-control (also popular are the MSLQ 84 and LASSI 85 which operate under SRL). The meta-knowledge scale of the MAI operationalises knowledge of strategies (e.g., “ I am aware of what strategies I use when I study ”) and self-awareness (e.g., “ I am a good judge of how well I understand something ”); the meta-control scale operationalises planning (e.g., “ I set a goal before I begin a task ”) and use of learning strategies (e.g., “ I summarize what I’ve learned after I finish ”). Learning journals, self-report questionnaires and interviews involve offline metacognition. Thinking aloud, though not engaging the same degree self-reflection, also involves offline metacognition in the sense that online processes are verbalised, which necessitate offline processing (see Table 1 for an overview and Supplementary Table 2 for more details).

More recently, methodologies borrowed from cognitive neuroscience have been introduced to study EF in educational settings 22 , 86 . In particular, researchers used classic cognitive control tasks such as the Stroop task (for a meta-analysis 86 ). Most of the studied components are related to meta-control and not meta-knowledge. For instance, the BRIEF 87 is a questionnaire completed by parents and teachers which assesses different subdomains of EF: (1) inhibition, shifting, and emotional control which can be viewed as online metacognitive control, and (2) planning, organisation of materials, and monitoring, which can be viewed as offline meta-control 87 .

Assessment of metacognition is usually compared against metrics of academic performance such as grades or scores on designated tasks. A recent meta-analysis reported a weak correlation of self-report questionnaires and interviews with academic performance whereas think-aloud protocols correlated highly 88 . Offline meta-knowledge processes operationalised by learning journals were found to be positively associated with academic achievement when related to reflection on learning activities but negatively associated when related to reflection on learning materials, indicating that the type of reflection is important 89 . EF have been associated with abilities in mathematics (mainly) and reading comprehension 86 . However, the literature points towards contrary directions as to what specific EF component is involved in academic achievement. This may be due to the different groups that were studied, to different operationalisations or to different theoretical underpinnings for EF 86 . For instance, online and offline metacognitive processes, which are not systematically distinguished in the literature, may play different roles in academic achievement. Moreover, the bulk of research focussed on young children with few studies on adolescents 86 and EF may play a role at varying extents at different stages of life.

A critical question in educational sciences is that of the nature of the relationship between metacognition and academic achievement to understand whether learning at school can be enhanced by training metacognitive abilities. Does higher metacognition lead to higher academic achievement? Do these features evolve in parallel? Developmental research provides valuable insights into the formation of metacognitive abilities that can inform training designs in terms of what aspect of metacognition should be supported and the age at which interventions may yield the best results. First, meta-knowledge seems to emerge around the age of 5, meta-control around 8, and both develop over the years 90 , with evidence for the development of meta-knowledge into adolescence 91 . Furthermore, current theories propose that meta-knowledge abilities are initially highly domain-dependent and gradually become more domain-independent as knowledge and experience are acquired and linked between domains 32 . Meta-control is believed to evolve in a similar fashion 90 , 92 .

Common methods used to train offline metacognition are direct instruction of metacognition, metacognitive prompts and learning journals. In addition, research has been done on the use of (self-directed) feedback as a means to induce self-reflection in students, mainly in computer-supported settings 93 . Interestingly, learning journals appear to be used for both assessing and fostering metacognition. Metacognitive instruction consists of teaching learners’ strategies to “activate” their metacognition. Metacognitive prompts most often consist of text pieces that are sent at specific times and that trigger reflection (offline meta-knowledge) on learning behaviour in the form of a question, hint or reminder.

Meta-analyses have investigated the effects of direct metacognitive instruction on students’ use of learning strategies and academic outcomes 18 , 94 , 95 . Their findings show that metacognitive instruction can have a positive effect on learning abilities and achievement within a population ranging from primary schoolers to university students. In particular, interventions lead to the highest effect sizes when they both (i) instructed a combination of metacognitive strategies with an emphasis on planning strategies (offline meta-control) and (ii) “provided students with knowledge about strategies” (offline meta-knowledge) and “illustrated the benefits of applying the trained strategies, or even stimulated metacognitive reasoning” (p.114) 18 . The longer the duration of the intervention, the more effective they were. The strongest effects on academic performance were observed in the context of mathematics, followed by reading and writing.

While metacognitive prompts and learning journals make up the larger part of the literature on metacognitive training 96 , meta-analyses that specifically investigate their effectiveness have yet to be performed. Nonetheless, evidence suggests that such interventions can be successful. Researchers found that metacognitive prompts fostered the use of metacognitive strategies (offline meta-control) and that the combination of cognitive and metacognitive prompts improved learning outcomes 97 . Another experiment showed that students who received metacognitive prompts performed more metacognitive activities inside the learning environment and displayed better transfer performance immediately after the intervention 98 . A similar study using self-directed prompts showed enhanced transfer performance that was still observable 3 weeks after the intervention 99 .

Several studies suggest that learning journals can positively enhance metacognition. Subjects who kept a learning journal displayed stronger high meta-control and meta-knowledge on learning tasks and tended to reach higher academic outcomes 100 , 101 , 102 . However, how the learning journal is used seems to be critical; good instructions are crucial 97 , 103 , and subjects who simply summarise their learning activity benefit less from the intervention than subjects who reflect about their knowledge, learning and learning goals 104 . An overview of studies using learning journals and metacognitive prompts to train metacognition can be found in Supplementary Table 3 .

In recent years, educational neuroscience researchers have tried to determine whether training and improvements in EF can lead to learning facilitation and higher academic achievement. Training may consist of having students continually perform behavioural tasks either in the lab, at home, or at school. Current evidence in favour of training EF is mixed, with only anecdotal evidence for positive effects 105 . A meta-analysis did not show evidence for a causal relationship between EF and academic achievement 19 , but suggested that the relationship is bidirectional, meaning that the two are “mutually supportive” 106 .

A recent review article has identified several gaps and shortcoming in the literature on metacognitive training 96 . Overall, research in metacognitive training has been mainly invested in developing learners’ meta-control rather than meta-knowledge. Furthermore, most of the interventions were done in the context of science learning. Critically, there appears to be a lack of studies that employed randomised control designs, such that the effects of metacognitive training intervention are often difficult to evaluate. In addition, research overwhelmingly investigated metacognitive prompts and learning journals in adults 96 , while interventions on EF mainly focused on young children 22 . Lastly, meta-analyses evaluating the effectiveness of metacognitive training have so far focused on metacognitive instruction on children. There is thus a clear disbalance between the meta-analyses performed and the scope of the literature available.

An important caveat of educational sciences research is that metacognition is not typically framed in terms of online and offline metacognition. Therefore, it can be unclear whether protocols operationalise online or offline processes and whether interventions tend to benefit more online or offline metacognition. There is also confusion in terms of what processes qualify as EF and definitions of it vary substantially 86 . For instance, Clements and colleagues mention work on SRL to illustrate research in EF in relation to academic achievement but the two spawn from different lines of research, one rooted in metacognition and socio-cognitive theory 31 and the other in the cognitive (neuro)science of decision-making. In addition, the MSLQ, as discussed above, assesses offline metacognition along with other components relevant to SRL, whereas EF can be mainly understood as online metacognition (see Table 1 ), which on the neural level may rely on different circuitry.

Investigating offline metacognition tends to be carried out in school settings whereas evaluating EF (e.g., Stroop task, and BRIEF) is performed in the lab. Common to all protocols for offline metacognition is that they consist of a form of self-report from the learner, either during the learning activity (thinking-aloud protocols) or after the learning activity (questionnaires, interviews and learning journals). Questionnaires are popular protocols due to how easy they are to administer but have been criticised to provide biased evaluations of metacognitive abilities. In contrast, learning journals evaluate the degree to which learners engage in reflective thinking and may therefore be less prone to bias. Lastly, it is unclear to what extent thinking-aloud protocols are sensitive to online metacognitive processes, such as on-the-fly error correction and effort regulation. The strength of the relationship between metacognitive abilities and academic achievement varies depending on how metacognition is operationalised. Self-report questionnaires and interviews are weakly related to achievement whereas thinking-aloud protocols and EF are strongly related to it.

Based on the well-documented relationship between metacognition and academic achievement, educational scientists hypothesised that fostering metacognition may improve learning and academic achievement, and thus performed metacognitive training interventions. The most prevalent training protocols are direct metacognitive instruction, learning journals, and metacognitive prompts, which aim to induce and foster offline metacognitive processes such as self-reflection, planning and selecting learning strategies. In addition, researchers have investigated whether training EF, either through tasks or embedded in the curriculum, results in higher academic proficiency and achievement. While a large body of evidence suggests that metacognitive instruction, learning journals and metacognitive prompts can successfully improve academic achievement, interventions designed around EF training show mixed results. Future research investigating EF training in different age categories may clarify this situation. These various degrees of success of interventions may indicate that offline metacognition is more easily trainable than online metacognition and plays a more important role in educational settings. Investigating the effects of different methods, offline and online, on the neural level, may provide researchers with insights into the trainability of different metacognitive processes.

In this article, we reviewed the literature on metacognition in educational sciences and cognitive neuroscience with the aim to investigate gaps in current research and propose ways to address them through the exchange of insights between the two disciplines and interdisciplinary approaches. The main aspects analysed were operational definitions of metacognition and metacognitive training, through the lens of metacognitive knowledge and metacognitive control. Our review also highlighted an additional construct in the form of the distinction between online metacognition (on the fly and largely automatic) and offline metacognition (slower, reflective and requiring meta-representations). In cognitive neuroscience, research has focused on metacognitive judgements (mainly offline) and EF (mainly online). Metacognition is operationalised with tasks carried out in the lab and are mapped onto brain functions. In contrast, research in educational sciences typically measures metacognition in the context of learning activities, mostly in schools and universities. More recently, EF has been studied in educational settings to investigate its role in academic achievement and whether training it may benefit learning. Evidence on the latter is however mixed. Regarding metacognitive training in general, evidence from both disciplines suggests that interventions fostering learners’ self-reflection and knowledge of their learning behaviour (i.e., offline meta-knowledge) may best benefit them and increase academic achievement.

We focused on four aspects of research that could benefit from an interdisciplinary approach between the two areas: (i) validity and reliability of research protocols, (ii) under-researched dimensions of metacognition, (iii) metacognitive training, and (iv) domain-specificity vs. domain generality of metacognitive abilities. To tackle these issue, we propose four avenues for integrated research: (i) investigate the degree to which different protocols relate to similar or different metacognitive constructs, (ii) implement designs and perform experiments to identify neural substrates necessary for offline meta-control by for example borrowing protocols used in educational sciences, (iii) study the effects of (offline) meta-knowledge training on the brain, and (iv) perform developmental research in the metacognitive brain and compare it with the existing developmental literature in educational sciences regarding the domain-generality of metacognitive processes and metacognitive abilities.

First, neurocognitive research on metacognitive judgements has developed robust operationalisations of offline meta-knowledge. However, these operationalisations often consist of specific tasks (e.g., 2-AFC) carried out in the lab. These tasks are often very narrow and do not resemble the challenges and complexities of behaviours associated with learning in schools and universities. Thus, one may question to what extent they reflect real-life metacognition, and to what extent protocols developed in educational sciences and cognitive neuroscience actually operationalise the same components of metacognition. We propose that comparing different protocols from both disciplines that are, a priori, operationalising the same types of metacognitive processes can help evaluate the ecological validity of protocols used in cognitive neuroscience, and allow for more holistic assessments of metacognition, provided that it is clear which protocol assesses which construct. Degrees of correlation between different protocols, within and between disciplines, may allow researchers to assess to what extent they reflect the same metacognitive constructs and also identify what protocols are most appropriate to study a specific construct. For example, a relation between meta- d ′ metacognitive sensitivity in a 2-AFC task and the meta-knowledge subscale of the MAI, would provide external validity to the former. Moreover, educational scientists would be provided with bias-free tools to assess metacognition. These tools may enable researchers to further investigate to what extent metacognitive bias, sensitivity and efficiency each play a role in education settings. In contrast, a low correlation may highlight a difference in domain between the two measures of metacognition. For instance, metacognitive judgements in brain research are made in isolated behaviour, and meta-d’ can thus be viewed to reflect “local” metacognitive sensitivity. It is also unclear to what extent processes involved in these decision-making tasks cover those taking place in a learning environment. When answering self-reported questionnaires, however, subjects make metacognitive judgements on a large set of (learning) activities, and the measures may thus resemble more “global” or domain-general metacognitive sensitivity. In addition, learners in educational settings tend to receive feedback — immediate or delayed — on their learning activities and performance, which is generally not the case for cognitive neuroscience protocols. Therefore, investigating metacognitive judgements in the presence of performance or social feedback may allow researchers to better understand the metacognitive processes at play in educational settings. Devising a global measure of metacognition in the lab by aggregating subjects’ metacognitive abilities in different domains or investigating to what extent local metacognition may affect global metacognition could improve ecological validity significantly. By investigating the neural correlates of educational measures of metacognition, researchers may be able to better understand to what extent the constructs studied in the two disciplines are related. It is indeed possible that, though weakly correlated, the meta-knowledge scale of the MAI and meta-d’ share a common neural basis.

Second, our review highlights gaps in the literature of both disciplines regarding the research of certain types of metacognitive processes. There is a lack of research in offline meta-control (or strategic regulation of cognition) in neuroscience, whereas this construct is widely studied in educational sciences. More specifically, while there exists research on EF related to planning (e.g. 107 ), common experimental designs make it hard to disentangle online from offline metacognitive processes. A few studies have implemented subject reports (e.g., awareness of error or desire for reminders) to pin-point the neural substrates specifically involved in offline meta-control and the current evidence points at a role of the lPFC. More research implementing similar designs may clarify this construct. Alternatively, researchers may exploit educational sciences protocols, such as self-report questionnaires, learning journals, metacognitive prompts and feedback to investigate offline meta-control processes in the brain and their relation to academic proficiency and achievement.

Third, there is only one study known to us on the training of meta-knowledge in the lab 78 . In contrast, meta-knowledge training in educational sciences have been widely studied, in particular with metacognitive prompts and learning journals, although a systematic review would be needed to identify the benefits for learning. Relative to cognitive neuroscience, studies suggest that offline meta-knowledge trained in and outside the lab (i.e., metacognitive judgements and meditation, respectively) transfer to meta-knowledge in other lab tasks. The case of meditation is particularly interesting since meditation has been demonstrated to beneficiate varied aspects of everyday life 108 . Given its importance for efficient regulation of cognition, training (offline) meta-knowledge may present the largest benefits to academic achievement. Hence, it is important to investigate development in the brain relative to meta-knowledge training. Evidence on metacognitive training in educational sciences tends to suggest that offline metacognition is more “plastic” and may therefore benefit learning more than online metacognition. Furthermore, it is important to have a good understanding of the developmental trajectory of metacognitive abilities — not only on a behavioural level but also on a neural level — to identify critical periods for successful training. Doing so would also allow researchers to investigate the potential differences in terms of plasticity that we mention above. Currently, the developmental trajectory of metacognition is under-studied in cognitive neuroscience with only one study that found an overlap between the neural correlates of metacognition in adults and children 109 . On a side note, future research could explore the potential role of genetic factors in metacognitive abilities to better understand to what extent and under what constraints they can be trained.

Fourth, domain-specific and domain-general aspects of metacognitive processes should be further investigated. Educational scientists have studied the development of metacognition in learners and have concluded that metacognitive abilities are domain-specific at the beginning (meaning that their quality depends on the type of learning activity, like mathematics vs. writing) and progressively evolve towards domain-general abilities as knowledge and expertise increase. Similarly, neurocognitive evidence points towards a common network for (offline) metacognitive knowledge which engages the different regions at varying degrees depending on the domain of the activity (i.e., perception, memory, etc.). Investigating this network from a developmental perspective and comparing findings with the existing behavioural literature may improve our understanding of the metacognitive brain and link the two bodies of evidence. It may also enable researchers to identify stages of life more suitable for certain types of metacognitive intervention.

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We would like to thank the University of Amsterdam for supporting this research through the Interdisciplinary Doctorate Agreement grant. W.v.d.B. is further supported by the Jacobs Foundation, European Research Council (grant no. ERC-2018-StG-803338), the European Union Horizon 2020 research and innovation programme (grant no. DiGYMATEX-870578), and the Netherlands Organization for Scientific Research (grant no. NWO-VIDI 016.Vidi.185.068).

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Metacognitive strategies improve learning

Metacognition refers to thinking about one's thinking and is a skill students can use as part of a broader collection of skills known as self-regulated learning. Metacognitive strategies for learning include planning and goal setting, monitoring, and reflecting on learning. Students can be instructed in the use of metacognitive strategies. Classroom interventions designed to improve students’ metacognitive approaches are associated with improved learning (Cogliano, 2021; Theobald, 2021).

Strategies to encourage students to use metacognitive techniques

  • Prompt students to develop study plans and to evaluate their approaches to planning for, monitoring, and evaluating their learning. Early in the term, advise and support students in making a study plan. After receiving feedback on the first and subsequent assessments, ask students to reflect on their performance and determine which study strategies worked and which did not. Encourage them to revise their study plans if needed. One way to support this is to ask students to identify their personal learning environment .  This is an activity where students identify the various resources and support available to them.
  • Offer practice tests. Explain to students the benefits of practice testing for improving retention and performance on exams. Create practice tests with an answer key to help students prepare for exams. Use practice questions for in-class formative feedback throughout the term. Consider creating a bank of practice questions from previous exams to share with students (Stanton, 2021).
  • Call attention to strategies students can adopt to space their practice. This can include explaining the benefits of spaced practice and encouraging students to map out weekly study sessions for your course on their calendar. These study sessions should include the most recent material and revisit older material, perhaps in the form of practice tests (Stanton, 2021).
  • Model your metacognitive processes with students. Show students the thinking process behind your approach to solving problems (Ambrose, 2010). This can take the form of a think-aloud where you talk through the steps you would take to plan, monitor, and reflect on your problem-solving approach.
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TEAL Center Fact Sheet No. 4: Metacognitive Processes

Metacognition is one’s ability to use prior knowledge to plan a strategy for approaching a learning task, take necessary steps to problem solve, reflect on and evaluate results, and modify one’s approach as needed. It helps learners choose the right cognitive tool for the task and plays a critical role in successful learning.

What Is Metacognition?

Metacognition refers to awareness of one’s own knowledge—what one does and doesn’t know—and one’s ability to understand, control, and manipulate one’s cognitive processes (Meichenbaum, 1985). It includes knowing when and where to use particular strategies for learning and problem solving as well as how and why to use specific strategies. Metacognition is the ability to use prior knowledge to plan a strategy for approaching a learning task, take necessary steps to problem solve, reflect on and evaluate results, and modify one’s approach as needed. Flavell (1976), who first used the term, offers the following example: I am engaging in Metacognition if I notice that I am having more trouble learning A than B; if it strikes me that I should double check C before accepting it as fact (p. 232).

Cognitive strategies are the basic mental abilities we use to think, study, and learn (e.g., recalling information from memory, analyzing sounds and images, making associations between or comparing/contrasting different pieces of information, and making inferences or interpreting text). They help an individual achieve a particular goal, such as comprehending text or solving a math problem, and they can be individually identified and measured. In contrast, metacognitive strategies are used to ensure that an overarching learning goal is being or has been reached. Examples of metacognitive activities include planning how to approach a learning task, using appropriate skills and strategies to solve a problem, monitoring one’s own comprehension of text, self-assessing and self-correcting in response to the self-assessment, evaluating progress toward the completion of a task, and becoming aware of distracting stimuli.

Elements of Metacognition

Researchers distinguish between metacognitive knowledge and metacognitive regulation (Flavell, 1979, 1987; Schraw & Dennison, 1994). Metacognitive knowledge refers to what individuals know about themselves as cognitive processors, about different approaches that can be used for learning and problem solving, and about the demands of a particular learning task. Metacognitive regulation refers to adjustments individuals make to their processes to help control their learning, such as planning, information management strategies, comprehension monitoring, de-bugging strategies, and evaluation of progress and goals. Flavell (1979) further divides metacognitive knowledge into three categories:

  • Person variables: What one recognizes about his or her strengths and weaknesses in learning and processing information.
  • Task variables: What one knows or can figure out about the nature of a task and the processing demands required to complete the task—for example, knowledge that it will take more time to read, comprehend, and remember a technical article than it will a similar-length passage from a novel.
  • Strategy variables: The strategies a person has “at the ready” to apply in a flexible way to successfully accomplish a task; for example, knowing how to activate prior knowledge before reading a technical article, using a glossary to look up unfamiliar words, or recognizing that sometimes one has to reread a paragraph several times before it makes sense.

Livingston (1997) provides an example of all three variables: “I know that I ( person variable ) have difficulty with word problems ( task variable ), so I will answer the computational problems first and save the word problems for last ( strategy variable ).”

Why Teach Metacognitive Skills?

Research shows that metacognitive skills can be taught to students to improve their learning (Nietfeld & Shraw, 2002; Thiede, Anderson, & Therriault, 2003).

Constructing understanding requires both cognitive and metacognitive elements. Learners “construct knowledge” using cognitive strategies, and they guide, regulate, and evaluate their learning using metacognitive strategies. It is through this “thinking about thinking,” this use of metacognitive strategies, that real learning occurs. As students become more skilled at using metacognitive strategies, they gain confidence and become more independent as learners.

Individuals with well-developed metacognitive skills can think through a problem or approach a learning task, select appropriate strategies, and make decisions about a course of action to resolve the problem or successfully perform the task. They often think about their own thinking processes, taking time to think about and learn from mistakes or inaccuracies (North Central Regional Educational Laboratory, 1995). Some instructional programs encourage students to engage in “metacognitive conversations” with themselves so that they can “talk” with themselves about their learning, the challenges they encounter, and the ways in which they can self-correct and continue learning.

Moreover, individuals who demonstrate a wide variety of metacognitive skills perform better on exams and complete work more efficiently—they use the right tool for the job, and they modify learning strategies as needed, identifying blocks to learning and changing tools or strategies to ensure goal attainment. Because Metacognition plays a critical role in successful learning, it is imperative that instructors help learners develop metacognitively.

What’s the Research?

Metacognitive strategies can be taught (Halpern, 1996), they are associated with successful learning (Borkowski, Carr, & Pressley, 1987). Successful learners have a repertoire of strategies to select from and can transfer them to new settings (Pressley, Borkowski, & Schneider, 1987). Instructors need to set tasks at an appropriate level of difficulty (i.e., challenging enough so that students need to apply metacognitive strategies to monitor success but not so challenging that students become overwhelmed or frustrated), and instructors need to prompt learners to think about what they are doing as they complete these tasks (Biemiller & Meichenbaum, 1992). Instructors should take care not to do the thinking for learners or tell them what to do because this runs the risk of making students experts at seeking help rather than experts at thinking about and directing their own learning. Instead, effective instructors continually prompt learners, asking “What should you do next?”

McKeachie (1988) found that few college instructors explicitly teach strategies for monitoring learning. They assume that students have already learned these strategies in high school. But many have not and are unaware of the metacognitive process and its importance to learning. Rote memorization is the usual—and often the only—learning strategy employed by high school students when they enter college (Nist, 1993). Simpson and Nist (2000), in a review of the literature on strategic learning, emphasize that instructors need to provide explicit instruction on the use of study strategies. The implication for ABE programs is that it is likely that ABE learners need explicit instruction in both cognitive and metacognitive strategies. They need to know that they have choices about the strategies they can employ in different contexts, and they need to monitor their use of and success with these strategies.

Recommended Instructional Strategies

Instructors can encourage ABE learners to become more strategic thinkers by helping them focus on the ways they process information. Self-questioning, reflective journal writing, and discussing their thought processes with other learners are among the ways that teachers can encourage learners to examine and develop their metacognitive processes.

Fogarty (1994) suggests that Metacognition is a process that spans three distinct phases, and that, to be successful thinkers, students must do the following:

  • Develop a plan before approaching a learning task, such as reading for comprehension or solving a math problem.
  • Monitor their understanding; use “fix-up” strategies when meaning breaks down.
  • Evaluate their thinking after completing the task.

Instructors can model the application of questions, and they can prompt learners to ask themselves questions during each phase. They can incorporate into lesson plans opportunities for learners to practice using these questions during learning tasks, as illustratetd in the following examples:

  • During the planning phase, learners can ask, What am I supposed to learn? What prior knowledge will help me with this task? What should I do first? What should I look for in this reading? How much time do I have to complete this? In what direction do I want my thinking to take me?
  • During the monitoring phase, learners can ask, How am I doing? Am I on the right track? How should I proceed? What information is important to remember? Should I move in a different direction? Should I adjust the pace because of the difficulty? What can I do if I do not understand?
  • During the evaluation phase, learners can ask, H ow well did I do? What did I learn? Did I get the results I expected? What could I have done differently? Can I apply this way of thinking to other problems or situations? Is there anything I don’t understand—any gaps in my knowledge? Do I need to go back through the task to fill in any gaps in understanding? How might I apply this line of thinking to other problems?

Rather than viewing reading, writing, science, social studies, and math only as subjects or content to be taught, instructors can see them as opportunities for learners to reflect on their learning processes. Examples follow for each content area:

  • Reading: Teach learners how to ask questions during reading and model “think-alouds.” Ask learners questions during read-alouds and teach them to monitor their reading by constantly asking themselves if they understand what the text is about. Teach them to take notes or highlight important details, asking themselves, “Why is this a key phrase to highlight?” and “Why am I not highlighting this?”
  • Writing: Model prewriting strategies for organizing thoughts, such as brainstorming ideas using a word web, or using a graphic organizer to put ideas into paragraphs, with the main idea at the top and the supporting details below it.
  • Social Studies and Science: Teach learners the importance of using organizers such as KWL charts, Venn diagrams, concept maps , and anticipation/reaction charts to sort information and help them learn and understand content. Learners can use organizers prior to a task to focus their attention on what they already know and identify what they want to learn. They can use a Venn diagram to identify similarities and differences between two related concepts.
  • Math: Teach learners to use mnemonics to recall steps in a process, such as the order of mathematical operations. Model your thought processes in solving problems—for example, “This is a lot of information; where should I start? Now that I know____, is there something else I know?”

The goal of teaching metacognitive strategies is to help learners become comfortable with these strategies so that they employ them automatically to learning tasks, focusing their attention, deriving meaning, and making adjustments if something goes wrong. They do not think about these skills while performing them but, if asked what they are doing, they can usually accurately describe their metacognitive processes.

Biemiller, A., & Meichenbaum, D. (1992). The nature and nurture of the self-directed learner. Educational Leadership, 50, 75–80.

Borkowski, J., Carr, M., & Pressely, M. (1987). “Spontaneous” strategy use: Perspectives from metacognitive theory. Intelligence, 11, 61–75.

Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, 34, 906–911.

Flavell, J. H. (1976). Metacognitive aspects of problem solving. In L. B. Resnick (Ed.), The nature of intelligence (pp. 231–236). Hillsdale, NJ: Lawrence Erlbaum Associates.

Flavell, J. H. (1987). Speculations about the nature and development of metacognition. In F. E. Weinert & R. H. Kluwe (Eds.), Metacognition, motivation, and understanding (pp. 21–29). Hillside, NJ: Lawrence Erlbaum Associates.

Fogarty, R. (1994). How to teach for metacognition. Palatine, IL: IRI/Skylight Publishing.

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Livingston, J. A. (1997). Metacognition: An overview. Retrieved December 27, 2011 from

McKeachie, W. J. (1988). The need for study strategy training. In C. E. Weinstein, E. T. Goetz, & P. A. Alexander (Eds.), Learning and study strategies: Issues in assessment, instruction, and evaluation (pp. 3–9). New York: Academic Press.

Meichenbaum, D. (1985). Teaching thinking: A cognitive-behavioral perspective. In S. F., Chipman, J. W. Segal, & R. Glaser (Eds.), Thinking and learning skills, Vol. 2: Research and open questions. Hillsdale, NJ: Lawrence Erlbaum Associates.

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Nietfeld, J. L., & Shraw, G. (2002). The effect of knowledge and strategy explanation on monitoring accuracy. Journal of Educational Research, 95, 131–142.

Nist, S. (1993). What the literature says about academic literacy. Georgia Journal of Reading, Fall-Winter, 11–18.

Pressley, M., Borkowski, J. G., & Schneider, W. (1987). Cognitive strategies: Good strategy users coordinate metacognition and knowledge. In R. Vasta, & G. Whitehurst (Eds.), Annals of child development, 4, 80–129. Greenwich, CT: JAI Press.

Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19, 460–475.

Simpson, M. L., & Nist, S. L. (2000). An update on strategic learning: It’s more than textbook reading strategies. Journal of Adolescent and Adult Literacy, 43 (6) 528–541.

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Authors: TEAL Center staff

Reviewed by: David Scanlon, Boston College

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The influence of metacognition in mathematical problem solving

L R Izzati 1 and A Mahmudi 2

Published under licence by IOP Publishing Ltd Journal of Physics: Conference Series , Volume 1097 , The 5th International Conference on Research, Implementation, & Education of Mathematics and Sciences 7–8 May 2018, Yogyakarta, Indonesia Citation L R Izzati and A Mahmudi 2018 J. Phys.: Conf. Ser. 1097 012107 DOI 10.1088/1742-6596/1097/1/012107

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1 Graduate School of Mathematics Education Program, Yogyakarta State University

2 Department of Mathematics Education, Yogyakarta State University

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This paper is a review of ten papers about the relation of metacognition and mathematical problem solving. So, the aims of this paper is to analyze the influence of metacognition in mathematical problem solving at low, average, and high students' performance. Metacognition is an important factor of mathematical problem solving. Metacognition is the ability to monitor and control our own thoughts, how we approach the problem, how we choose the strategies to find a solution, or ask ourselves about the problem, in the other word, it can be defined as think about thinking. Solving mathematics problems requires analysis of the given problem, planning the strategy to be used to solve the problem, undertaking the planned strategy and checking whether the steps that have been done are correct. Therefore, metacognition is necessary for the successful solving of mathematical problems. This paper analyzes that the higher metacognition that students have, the better mathematical problem solving that students can do.

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H. Hunaifi , Dadang Juandi; The effect of metacognitive strategies on mathematical problem-solving ability: A systematic literature review. AIP Conf. Proc. 17 October 2023; 2734 (1): 090041.

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This paper is a systematic literature review (SLR) of the effect of metacognitive strategies on mathematical problem-solving ability. The purpose of this study was to analyze the effect of applying metacognitive strategies on students’ mathematical problem-solving abilities. The steps in SLR involve, 1) develop research question; 2) construct selection criteria; 3) develop search strategy; 4) select studies using selection criteria; 5) assess the quality of studies; 6) synthesis result of research question. The search for relevant articles was carried out on online publications such as ProQuest, Google Scholar, dan ScienceDirect published in the 2010-2021 period. The authors obtained 9 articles that were included in the criteria for determining the selection of relevant literature. The results o that metacognitive strategies have an effect on mathematical problem-solving abilities. This is because, the components of the metacognitive strategy can raise awareness in students to carry out efficient problem-solving. This paper also found that students who use metacognitive strategies continuously will improve their mathematical problem-solving abilities. This research will provide information to mathematics teachers, which indicates that teachers need to use the metacognitive strategy in learning to maximize students’ mathematical problem-solving abilities.

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Kristen A. Carter MS

Metacognition’s Role in Decision Making

Metacognition can help us to think outside the box..

Posted February 14, 2024 | Reviewed by Hara Estroff Marano

  • Research tells us that making creative decisions is not necessarily related to intelligence.
  • We can use metacognition to draw from a wide range of problem-solving strategies.
  • Metacognition is a cognitive skill that can be taught and nurtured.

Source: ismagilov | iStock

We make decisions all day long. Some of them are based on careful consideration, some are based on past experiences, and some just seem to come without much thought. Decisions come in all forms. Results can be good, bad, or unclear.

Research tells us that our decision-making ability is not necessarily linked to intelligence but rather to personality , motivation , and willingness to learn. We all have goals and we want to find a way to reach them.

More complex decisions require problem-solving, strategies, re-framing, creative thinking , and possibly seeking advice from others. In addition, there often is the matter of evaluating the difficulty of the task. Is it within or beyond a person’s perceived capabilities?

There is another key player in the mix when it comes to making effective decisions and following up with appropriate actions. It has to do with being able to reflect on one’s thinking and make adjustments that bring about the desired outcome.

The intricacies of how we make decisions are directly related to our facility of metacognition . Metacognition is often referred to as the ability to “think about our thinking.” It includes knowledge about oneself and the ability to select effective strategies, as well as being able to evaluate task performance. Importantly, it includes knowledge about oneself as a learner. Can the person trust their abilities to evaluate all phases of the decision-making process?

An everyday example

Let’s bring this down to what this can look like when making daily decisions that may affect health and well-being,

There is a person whose goal is to eat healthier and lose weight. He/she has decided that there is a specific number on the scale that matters, and a restrictive diet has been chosen. Let’s say that person is then confronted with making choices at a dinner buffet. They can select a small plate of items that are part of the program or a large plate filled with favorites as well as a plan to go back for dessert. It’s decision time!

The person can say to themselves, “Well, just this once, I am going to go for it. I will be better with my eating tomorrow.” Or they may say, “OK, I am going to garner all my willpower and do the right thing here.”

Alternatively, using metacognition would look like this: The person says, “Uh oh, this is a situation that is challenging for me. I can reframe this and come up with a creative solution. I do not have to think about this as my last chance to pig out. I can be more selective and choose what will please me most, using reasonable portion size as a guide. That way, I can enjoy the experience and still reach my goals.” This person is evaluating and shifting their thoughts in order to achieve their goal, rather than trying to following some rules.

This is a fairly simplistic example, but it describes a scenario that is fairly common.

Learning about metacognition

Why don’t people take advantage of metacognition more often, in this context and others?

In spite of that simplistic example, there are some complexities here. Metacognition is a vital part of being able to think creatively, as seen in the example. Research by Akcaoglu, Mor and Kulekci (2023) indicates that, “As a skill, metacognitive awareness is one of the core components of self-regulated learning.” The example above shows how metacognition links to self-regulated learning via creative thinking, curiosity, and willingness to learn.

Notice that the expression is metacognitive awareness. Not everyone has that awareness. This came to the attention of John Flavell in the 1970s when he was first formulating the concept of metacognition. His focus at the time was educational psychology.

Broadly speaking, metacognition is a skill like other cognitive skills in that some people have more of it than others. For many people, developing the skill comes from having been exposed to the concept and learning how to use it.

Flavell envisioned an educational system that describes metacognition, and supports development of it. He indicated that metacognition includes awareness that a person’s beliefs about themselves affect their learning process. Additionally, metacognitions may not be correct. Being able to evaluate the thought process and results is important. According to Flavell, these features can become part of the educational process.

Stimulating metacognition

In line with Flavell’s observations, there are ways to encourage metacognition by asking certain questions. Here are some examples:

  • Are you aware that we all have habits around how we think?
  • What are your beliefs about how difficult this task is going to be?
  • Have you used some creative strategies for this challenge in the past? What were they?
  • Sometimes, we evaluate our mistakes so that we can learn from them. Could you do that with this process?

metacognitive strategies in problem solving

The point here is to stimulate new thoughts that are outside the usual box. Also to see the potential to do so, and the benefits. Ultimately, the goal is nurture metacognition skills when devising solutions to a problem, whether it is healthier eating or something else.

Flavell, J. (1979) Theories of Learning in Educational Psychology. American Psychologist. 34: 906-911.

Akcaoglu, M.O.. Mor, E., Kulekci, E. (2023). The mediating role of metacognitive awareness in the relationship between critical thinking and self-regulation. Thinking Skills and Creativity. 47:101187.

Basu, S & Dixit, S. (2022). Role of metacognition in explaining decision-making styles: A study of knowledge about cognition and regulation of cognition. Personality and Individual Differences. 185:111318.

Kristen A. Carter MS

Kristen Carter, M.S., is an exercise physiologist and the author of The End of Try Try Again: Overcome Your Weight Loss and Exercise Struggles for Good.

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Metacognitive strategies in solving mathematical word problems: a case of Rwandan primary school learners

  • Original Paper
  • Open access
  • Published: 07 September 2022
  • Volume 2 , article number  186 , ( 2022 )

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This study aims to understand the use of metacognitive skills by Rwandan learners while solving mathematical word problems. We interviewed and assessed third-, fourth- and fifth-grade learners from a public primary school. The following three points emerged. First, the metacognitive skills of learners with correct answers were considerably higher than that of those with incorrect answers. Second, although there was no considerable difference in metacognitive skills between learners who answered correctly and those who did not at the stage of ‘understand the problem’, considerable differences were observed in the ‘search for solving methods’ and ‘execute the solving methods’ and ‘examine the answer’ stages. During the ‘search for solving methods’ and ‘execute the solving methods’ stage, learners who answered correctly mainly used three metacognitive skills to control their learning—‘writing the process by sentences’, ‘drawing tables’ and ‘drawing pictograms’. Third, when metacognitive skills were measured and scored, the average scores for fifth and third graders were similar. The interview revealed that the teachers of third graders taught them metacognitive strategies in mathematics lessons. It can be inferred that consequently, the metacognitive skills of third graders were raised to be as high as those of fifth graders. Although this is only a single empirical study in Rwanda, it is a major step towards improving the standard of mathematics education in African countries. In the future, similar research must be conducted in other African countries to accumulate relevant research results.

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Metacognition is a cognitive psychological concept, and several physical and practical studies have been conducted about it since the 1970s. Recently, the concept of metacognition has gained attention and has been actively discussed as an indispensable aspect of learning (Stillman and Mevarech 2010 ; Güner and Erbay 2021 ). For example, in the 2001 revised edition of the internationally established Bloom’s Taxonomy, ‘metacognitive knowledge’ is established as a new dimension of knowledge and is ranked extremely high. In addition, the Centre for Curriculum Redesign (CCR) states that global frameworks, such as twenty-first-century skills and key competencies, have several commonalities, including metalearning strategies, which are posited as the fourth dimension of education and learning. This concept corresponds to the skill called ‘learning to learn’ in the European Union’s ‘Key Competencies for Lifelong Learning’ (European Commission 2007 ) and the ATC21S’s ‘21st Century Skills’ (Griffin et al. 2012 ). Furthermore, the Organisation for Economic Co-operation and Development (OECD) encourages prioritising metacognitive skills. Metacognitive skills are an increasingly critical competency for individuals in a world enduring the effects of globalisation, climate change and technological advances, which will require individuals to acquire new knowledge and skills for jobs fundamentally altered or not yet invented (Horvathova 2019 ; OECD 2019 ). In an increasingly volatile and uncertain world, it can be stated with certainty that metacognitive skills are indispensable.

Metacognition began garnering attention in mathematics education through research on the relationship between problem-solving and metacognition (Schoenfeld 1983 ). When researching problem-solving, the focus has only been on the knowledge and skills that directly influence the process of solving the problem, and little attention has been given to the intellectual functions that regulate them. However, when solving problems, teachers tend to encourage their learners to read and point out known information, predict results, reflect on problem-solving processes and think of other solution methods. This, consciously or unconsciously, enables learners to use metacognition. Regarding the relationship between metacognition and academic abilities, various countries have indicated that children with high mathematical skills also have high cognitive abilities (Okamoto 1992 ; Chytry 2020 ). Additionally, mathematical problem-solving can be facilitated and supported using metacognitive activities (Borkowski et al. 2000 ; Tohir 2019 ). For example, Desoete and Roeyers ( 2002 ) reported that test scores increased significantly in the third grade of elementary school because learners received guidance on metacognitive strategies, such as predicting whether a problem could be solved and self-evaluating answers. Similarly, Dignath and Buettner ( 2008 ) reported that encouraging learners to practise metacognition leads to a significant positive effect. However, such studies are predominant in Western countries. William and Maat ( 2020 ) surveyed 31 articles published between the year 2017 and 2020 from 2 known databases: ERIC and Scopus. All the articles focused on developed countries’ case studies. In developing countries, particularly in Africa, the situation remains largely unclear. As improving the quality of education is an urgent issue among the Sustainable Development Goals and Education 2030, it is important to have a good understanding of metacognitive skills as they are regarded as indispensable in learning in developed and developing countries.

There are two main objectives in this study. One is to analyse the metacognitive skills of Rwandan third, fourth and fifth graders solving mathematical word problems. The second is to analyse the influence of metacognitive abilities on problem-solving abilities.

Literature review

Definition of metacognition.

The term metacognition was first broadly defined by Flavell ( 1979 ) as ‘cognition about cognition’. Brown ( 1987 ) classified metacognition into two categories: ‘knowledge of cognition’ and ‘regulation of cognition’. ‘Knowledge of cognition’ is the activity of consciously reflecting upon a cognitive activity and is similar to Flavell’s idea of metacognition. ‘Regulation of cognition’ comprises three processes: ‘planning’, ‘monitoring’ and ‘regulation’. ‘Planning’ refers to devising a plan for how to solve a problem before attempting to do so, and ‘monitoring’ is about examining and observing the solving method while attempting to solve the problem. Based on the ‘monitoring’ results, the ‘regulation’ aspect evaluates and modifies the methods and plans used.

Developmental stages of metacognitive skill

Regarding the relationship between metacognitive development and age, Mevarech ( 1995 ) demonstrated that kindergarteners use metacognitive knowledge when solving mathematical problems. Further, Shamir et al. ( 2009 ) reported that kindergarteners could recognise the method they used for memorisation tasks and share it with their peers. Whitebread and Coltman ( 2010 ) noted that infants (3 years or older) engage in metacognition when performing non-verbal and unconscious activities. Previous studies have demonstrated that as children grow older, their metacognitive skills develop along with their intellectual abilities (Berk 2003 ; Merchán Garzón et al. 2020 ). Most adults have metacognitive knowledge, can plan according to situations and attempt to resolve the situation (Schraw et al. 2006 ). Furthermore, among metacognitive skills, monitoring, which is used in the process of problem-solving, and evaluation, which occurs after solving a problem, comprise skills that develop later than the skill of planning a solving method. This is because children are not involved in such processes at school. Kramarski et al. ( 2010 ) reported that 8-year-olds are good at planning when solving problems, but they are ineffective at monitoring during their problem-solving process. Regarding the effectiveness of the educational intervention, Hattie et al. ( 1996 ) pointed out that guidance on metacognitive strategies is more effective when given to younger people. Similarly, Dignath and Buettner ( 2008 ) provided instructions on metacognitive strategies for learners in grades 1–12 and measured the effect size. The results showed that primary school learners scored 0.61 times above the standard deviation, and secondary school learners scored 0.54 times above the deviation. Thus, they clarified that the effect size of metacognitive intervention was larger for primary school learners than for secondary school ones.

Metacognitive awareness and visualisation

Visualisation, called the representational view of the mind, integrates the mental processes of visual imagery, memory, processing, relationships, attention and imagination (Makina 2010 ). The use of visualisation supports equity, engagement and learning (Schaffer 2017 ). Learners can not only plan their own education process, evaluate their results and monitor their progress but also transition to higher levels of cognitive skills, mastering the subject content and the competent use of visualisation methods (David and Sulaiman 2021 ). Jacobse and Harskamp ( 2012 ) indicated that pictorial visualisations show that a learner does not yet know how to explore the problem to arrive at a useful solution, thus indicating low metacognitive regulation. However, drawing steps to solve a problem helps learners reflect on, monitor and evaluate their problem-solving abilities and strategies. This has been shown to increase conceptual understanding and help learners evaluate their learning (Martin et al. 2017 ). Drawing and writing your thoughts as pictures, diagrams and sentences are considered a metacognitive strategy.

Metacognition and the stages of solving word problems

Mathematical word problems are one of the most difficult types of problems in mathematics, and many reasons have been identified for their challenging nature (Aaron et al. 2022 ; De Corte et al. 2000 ; Hegarty et al. 1995 ; Lewis 1989 ). One of the greatest difficulties is the process of seeking a solution. A few steps are required. First, the text must be read and understood. Next, a decision must be made regarding which mathematical operations are relevant to formulate an equation. Finally, the learner must solve this mathematical equation to obtain the answer (Boonen et al. 2013 ; Mevarech 1999 ; Pimm 1991 ). How does metacognition function in the process of solving word problems? Pólya ( 1973 ) claimed that problem-solving has four stages: understand the problem, search for solving methods, execute the solving methods and examine the answer. Considering the aforementioned knowledge about metacognition, we will consider how metacognition functions in these four stages. The cognitive activity in the first stage, ‘understand the problem’, is to read and understand the word problem. The metacognition used at this stage is to consider whether one has solved a similar problem before or if one’s understanding of a problem is unclear (e.g. ‘I’m not sure that I understand the question, so I’ll read the text again’). Other metacognitive activities include thinking about what is known and unknown in the problem or whether one has understood the problem. These correspond to the ‘task’ in Brown’s ‘knowledge of cognition’.

In the next stage, ‘search for solving methods’, one needs to approach the problem with a deliberate strategy and plan rather than attempting a solution haphazardly. Establishing a path to the solution, such as considering what to find first and then questioning what to solve, and estimating the solution are also possible metacognitive activities at this stage. Moreover, these correspond to ‘planning’ in Brown’s ‘regulation of cognition’. Subsequently, in ‘execute the solving methods’, metacognitive activities, such as checking whether the solving method’s execution is correct or considering whether other solving methods might exist when the current solving method is not working, are performed. This corresponds to ‘monitoring’ in Brown’s regulation of cognition’. In the final stage, ‘examine the answer’, metacognition is required to verify accurately whether the resultant solution is correct. This corresponds to an evaluation of Brown’s ‘regulation of cognition’.

Hence, various metacognitive activities occur in the process of solving a word problem (see Table 1 ). Thus, metacognitive activity is an especially crucial element at each stage of solving a word problem. Many researchers pointed out that metacognitive abilities influence problem-solving abilities (Chytry 2020 ). People with strong metacognitive skills can solve word problems efficiently. However, people with poor metacognitive skills cannot perform metacognitive activities and are unsure how to begin. They approach the problem randomly, despite the problem’s complexity. When they find themselves unable to solve a problem, they cannot pause to rethink or return to the previous stage and try a new approach. Therefore, they find it difficult to solve problems efficiently.

Theoretical framework

Figure  1 shows the theoretical framework of this study. As mentioned in the literature review, problem-solving can be divided into four stages namely, ‘understand the problem’, ‘search for solving methods’, ‘execute the solving methods’ and ‘examine the answer’. It would be possible to identify the students’ metacognitive skills by analysing their skills used in each stage and their level of skill. Therefore, in this study, the holistic metacognitive skills of the students solving word problems and their relationship to academic performance are determined through an analysis of each stage.

figure 1

Theoretical framework of the study

Research method

Research subjects.

Ten learners from each of the third, fourth and fifth grades of a public school in Kayonza district, Eastern Province, Rwanda, were randomly selected from a register of learners’ names. Thus, 30 learners were included in the study.

Problem under investigation

In modern society, there is worldwide consensus on the notion that teaching routine problem-solving is insufficient. In this study, we used a problem with elements of a complex, unfamiliar, and non-routine (CUN) task. The only knowledge and skills required to solve the problem are adding and subtracting numbers of 10 or less. However, in this problem, rather than being asked to calculate the final number after a transaction has occurred, learners are asked to determine the original quantity before a transaction takes place. Furthermore, it can be said that this problem is a CUN problem because it cannot be solved by only manipulating the given numerical values.

Methods of data collection

We used the Okamoto ( 1992 ) methods in our study. To eliminate differences in the children’s writing skills, the problem comprehension and reflection aspects in this study were conducted through interviews instead of asking learners to write their ideas. Additionally, the presentation of the word problem and interviews were conducted in their local language, Kinyarwanda, to eliminate the influence of various levels of language proficiency as much as possible. To measure metacognition and how it works in each of the aforementioned four stages to solve a word problem, we interviewed each learner using the order of questions/instructions (a) to (g) listed below.

Have learners read the question and state their levels of confidence (0–100) to obtain the correct answer and their reason.

Ask what they were mindful of when reading the problem and what they knew after reading the problem.

Tell learners that they are free to write equations and diagrams and ask them to solve the problem.

Have learners explained their method of problem-solving.

Have learners explained what they were mindful of when solving the problem.

Have them explain the parts that were difficult.

Have learners stated their levels of confidence (0–100) about whether they obtained the correct answer and stated their reasons.

Table 2 shows the relationship between the seven interview questions/instructions and the four stages of problem-solving. Notably, (a) and (b) relate to what happens before actually solving the problem and are classified under ‘understand the problem’. As (c) corresponds to the process of solving the problem and (d) corresponds to explaining the solving method, we classified them under ‘Search for solving methods’ and ‘Execute the solving methods’, respectively. Further, (e), (f), and (g) make the learners reflect on their process of problem-solving and think about what they were mindful of, what was difficult, and whether they obtained the correct answer. We classified these questions under ‘examine the answer’.

Method of analysis

This subsection explains the method of assigning scores to the interviews. The study was conducted using a mixed methods approach, incorporating qualitative research to supplement the results of the quantitative analysis. To quantitatively analyse the data, it is necessary to express the extent to which the metacognitions (a) to (g) occur as numerical scores. In this study, we created a rubric showing the criteria to determine the degree to which metacognition occurs, making it possible to assign scores (Table 3 ). Each item was evaluated using a scale of 0 to 4 (5 levels). Level 0 represents a state in which metacognition has not occurred. Level 1 is the stage where metacognition can be slightly established. Further, Level 2 is when metacognition is established to a certain extent; Level 3 is where metacognition is mostly established. Finally, Level 4 is where metacognition is fully established. For questions/instructions (a) to (g), we developed a criterion corresponding to each level. For example, in the case of question (f), where learners are required to explain the difficult parts, a score of 0 would be given for the learner not providing a response. A score of 1 was given for the learner only to answer which parts were difficult. A score of 2 would be given if, in addition to 1, the learner answered with some reasoning. Further, a score of 3 was given if the learner stated their reason to a certain extent of clarity; a score of 4 was for a learner who aptly and accurately presented their reason. We quantitatively analysed learners’ correct answers and qualitatively analysed how their metacognitive skills appeared in their responses, especially the method search and execution aspects.

Additionally, two qualitative analyses were conducted to thoroughly review the data of the quantitative research. First, the metacognitive skills in the ‘planning’ and ‘monitoring’ stages were analysed qualitatively, focusing on the learners’ drawings and texts. Second, an interview was conducted with the teachers to confirm how they taught the word problems. The author asked the teachers about two points: (1) the instructions given to the students and (2) confirming the answer with the students.

Results and discussion

Table 4 shows an example interview (one learner from the third grade). All learners were posed questions similar to those exemplified in Table 4 . Using this example, we can explain the scoring. Responding to question (a) in ‘recognition of the task’, this student answered 90% and offered the reason ‘Because we learned it’. As this was a minimal reason, it was judged as Level 2, which is ‘Judges whether they can solve the problem, providing minimal reasons’. In addition, responding to question (d) in ‘planning and monitoring’, the student responded ‘Ten plus three is thirteen, thirteen minus six is six’. He offered an explanation, but it was incorrect. Therefore, it was judged as Level 2, which is ‘Explains their solving method, although their solving method is incorrect’. Finally, responding to question (f) in ‘regulation’, the answer provided was ‘To find the number of balls he has after’. As this was a minimal response, it was judged as Level 1, which is ‘Can explain, although only slightly, the sections of which they were mindful’.

Categorisation of the results by grade level

Table 5 presents the results for each grade. The average scores for third, fourth and fifth graders were 13.4, 7.7 and 15.8, respectively.

The rate of correct answers for the third graders was almost equivalent to that of the fifth graders, contradicting a previous study (Berk 2003 ; Merchán Garzón et al. 2020 ), stating that metacognitive skills improve as learners’ grade levels increase. This is illuminated in the results of our interviews with teachers concerning how they teach word problems. In a third-grade teachers’ interview, they stated that, for tasks that involve problem-solving, such as word problems, they instruct their learners daily to be aware of ‘what they know’, ‘what is being asked’ and ‘how a solution can be reached’. The three aspects they mentioned are metacognitive skills that relate to planning and searching for a solving method. Expressly, it was observed that the third-grade teachers consciously trained their learners to use metacognitive strategies when solving problems. However, teachers of other grades did not provide such guidance. They asked for the mathematical expression and how to solve the expression, which requires cognitive skills. Most third graders, as shown in the previous example, could sequentially respond by dividing the response space into three columns of ‘what they know’, ‘what they are being asked’ and ‘how to find the answer’. This suggests that their higher metacognitive scores compared to other grades are due to differences in teaching methods. To date, many studies have demonstrated that instruction improves metacognitive skills (Shilo and Kramarski 2019 ; Zimmerman 2008 ; Mevarech and Kramarski 1997 ). As our study does not focus on metacognition instruction, we cannot determine with certainty whether the third-grade teachers actually taught metacognition. However, our study suggests that comparable results can be obtained throughout Rwanda.

Categorisation based on learners with correct answers and learners with incorrect answers

Table 6 shows the number of learners who received the correct answer and those who did not. We investigated by a chi-square examination whether there is any statistically significant difference for each in the problem-solving process stages, which are ‘understand the problem’, ‘search for a solving method and Execute the plan’ and ‘Check and extend’. We investigated whether there is a statistically significant difference in the mean scores of metacognitive skills for the two groups or not.

The null hypothesis states that ‘to answer correctly or incorrectly does not depend on the metacognitive skills’. Thus, if the p -value is high, the null hypothesis is accepted, and if the p -value is low, the null hypothesis is rejected. The group of the learners who answered correctly ( n  = 8) and the group of the learners who answered incorrectly ( n  = 22) are different students. The table shows the arithmetic mean of his/her marks in each question, though the independent samples, T -test was conducted by category: ‘recognition of the task’ (a and b), ‘planning and monitoring’ (c and d) and ‘regulation’ (e, f and g)’. The p -value was derived from the independent samples T -test.

No statistically significant difference was observed at the ‘problem comprehension’ stage. At this stage, metacognition involved making judgements about what one is mindful of when reading the problem, what one knows, and whether one thinks they can solve the problem.

Both groups of learners could not immediately understand the complex transaction structure in the problem; they judged that the answer could be obtained using only simple addition and subtraction—both groups of learners responded using almost no metacognition. Even among those who answered correctly, their scores on metacognition were the lowest in three of the categories. No statistically significant differences were found between the two groups. Next, for ‘method search and execution’, a statistically significant difference was found at a significance level of 1%. When searching for a method, learners need to suitably monitor their cognitive processes and make adjustments about whether connections can be successfully made from the various information obtained in the previous process of comprehending the problem. Furthermore, even in the execution process, learners must continue to monitor their thinking and make adjustments about whether the solving method is appropriate to arrive at the correct answer.

According to Glasser, learners with high problem-solving skills can simulate the act of monitoring a problem-solving process while referring to their prior knowledge and correctly predicting the result of their problem-solving. The results of this study also support the findings of existing research, as the gap between the two groups of learners is the most significant in this category when compared to the other two groups. A significant difference was also observed for metacognition, one which occurs during the ‘check and extend’ stage. In this stage, the learners were asked to explain what they were mindful of about their problem-solving methods and in their process of solving the problem. Additionally, they were asked to state how confident they were in obtaining the correct answer while providing reasons. These situations require a sophisticated level of processing, which involves monitoring and verbalising one’s cognitive processes. Theoretically, even if a learner’s answer is incorrect, they should still be able to score highly if metacognition occurs during the process. However, a significant difference in metacognition score was observed between those who answered the question correctly and those who did not.

From the above, it can be said that the results we obtained were similar to those from previous studies. Learners who obtained the correct answers generally had high metacognitive skills, and learners with high academic ability also had high metacognitive skills.

Metacognitive skills in ‘method search’ and ‘execution’

The metacognitive skills used in ‘method search’ and ‘execution’ were analysed qualitatively based on the learners’ responses. We found that learners with correct answers mainly used three methods. All the methods showed that learners used their metacognitive skills to control their learning while solving the problem. The first was to express the interaction between two people using words and mathematical expressions (Fig.  2 ).

figure 2

Writing the interaction between two people in words and mathematical expressions

While writing them, the learners organised the movement of the balls between two people to formulate their answers. In other words, they control their learning by writing sentences. The second step was to draw a table about the problem: what they knew, what was being asked, and the solution or answer (Fig.  3 ).

figure 3

Making a table of what they knew, what was being asked, and the solution or answer

The learners organised their ideas by filling their ideas Querysystematically in a table. The third method involves drawing two people in the question and drawing the actual direction of the balls’ movement in the picture (Fig.  4 ). Regarding visualisation, drawing increases conceptual understanding and helps learners evaluate information (Martin et al. 2017 ). In this case, we found that the learners used their metacognitive control in their own pictorial visualisations.

figure 4

Drawing the actual movement of the balls


In this study, we conducted tests and interviews with Rwandan third, fourth, and fifth graders in a public elementary school to better understand Rwandan children’s metacognitive skills when solving mathematical word problems. For additional data, we conducted interviews with teachers educating the relevant grades on how they teach word problems. The results clarified the following three points about the metacognitive skills used when Rwandan learners solved mathematical word problems.

The first point concerns the difference in metacognitive skills between those who correctly solved the problem and those who did not. The metacognitive skill of those who arrived at the correct answer was observed to be considerably higher than that of those who did not.

The second point is that although a considerable difference in metacognitive skills was not observed at the ‘problem comprehension’ stage, a significant difference was observed in the subsequent stages of ‘search for a solving method and execute the plan’ and ‘check and extend’. In particular, the ‘check and extend’ stage requires sophisticated levels of cognitive processing, such as monitoring and verbalising one’s cognitive processes. It was shown that learners who answered correctly could perform sophisticated processes that use metacognitive skills compared to learners who answered incorrectly.

Third, when metacognitive skills were measured and scored, the average scores for fifth and third graders were similar. However, of the four stages of the problem-solving process, the third graders scored much higher than the fifth graders at the ‘problem comprehension’ stage. At this stage, several of them could provide their responses in a logical sequence, dividing their response space into three columns. When the teachers were interviewed about this, they answered that they had instructed their learners to check each of their steps when solving word problems. This suggests that the third-grade teachers instruct their learners in metacognitive strategies; consequently, the learners’ metacognitive skills increased to the same level as the fifth graders. It has been reported that metacognitive skills are enhanced by instruction. These three points are evident in previous studies; however, as noted in the background, they are significant in this study because the students are from an African country in which very little research has been conducted on metacognition.

In addition, there are two main contributions to the research on metacognition from a theoretical and methodological perspective. The theoretical perspective is that we proposed a link between Brown’s approach to metacognition and Pólya’s problem-solving stages. Metacognitive skills were presented in each of the four stages indicated by Pólya. The methodological perspective is that we developed a rubric to assess students’ metacognitive skills quantitatively, although further improvements are needed to deem the indicator more objective.

To the best of our knowledge, this is the only empirical study in Rwanda. As only one school was surveyed and the number of participants was not sufficient, generalisations cannot be made from this survey alone. However, as the participating school is typical of rural public schools, it is possible to acquire some implications about the metacognitive skills of Rwandan students from the results of this study.

One future task will be to conduct empirical studies regarding this in the context of the Rwandan nation. If this can be demonstrated, it can be suggested that equivalent results can be obtained in other African countries with similar sociocultural contexts. This would be a big step towards improving the quality of mathematics education in African countries.

The datasets generated during and/or analysed during the current study are not publicly available due to protection of students’ rights to privacy but are available from the corresponding author on reasonable request.

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Kusaka, S., Ndihokubwayo, K. Metacognitive strategies in solving mathematical word problems: a case of Rwandan primary school learners. SN Soc Sci 2 , 186 (2022).

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Metacognitive awareness and academic motivation and their impact on academic achievement of Ajman University students

Rasha m. abdelrahman.

a Psychology Department Ajman University, United Arab Emirates

b Researcher at the National Center for Examination and Educational Evaluation (NCEEE), Egypt

Metacognition is the ability of learners to take necessary steps to plan suitable strategies for solving the problems they face, to evaluate consequences and outcomes and to modify the approach as needed, based on the use of their prior knowledge. Metacognition helps learners to successfully achieve a personal goal by choosing the right cognitive tool for this purpose. The study, therefore, aims to explain the relationship and impact of metacognitive awareness and academic motivation on student's academic achievement. This descriptive and correlational study design has included 200 students (60 males) studying sociology in the College of Mass Communication and Humanities at Ajman University, UAE. Academic intrinsic motivations scale and the metacognitive awareness inventory were used as instruments. PLS-SEM was used to examine the relationship between metacognitive awareness and academic motivation, and their impact on academic achievement. Females obtained significantly higher levels than males on the two scales of metacognitive awareness, as shown in metacognitive knowledge. Females reported a higher-level academic extrinsic motivation than males. There is a highly significant correlation between the students' academic achievement and academic motivation; academic achievement and academic intrinsic motivation; academic achievement and academic extrinsic motivation. Metacognitive awareness is a major contributor to success in learning and represents an excellent tool for the measurement of academic performance.

Psychology; Academic achievement; Academic motivation; Gender differences; Metacognitive awareness.

1. Introduction

The quality of education has been positively changed by the rapid development of science ( Darling-Hammond et al., 2019 ). This condition (quality of education) further paved the way to transition from teacher-centered education to student-centered education, completing changing the conventional understanding of education ( Kasim and Aini, 2012 ). Furthermore, the crucial components of student-centered education are among the study procedures, where students use their metacognitive awareness, regulating their own study procedures, and possessing motivation. Metacognitive awareness, metacognitive experiences, metacognitive knowledge, metacognitive beliefs, metacognitive skills, high-level skills, and upper memory are some terms associated with metacognition ( Veenman et al., 2006 ; Yeşilyurt, 2013 ). The objective of education in the 21st century is not only to provide students with a huge amount of knowledge and information but also to prepare students to become effective and independent learners, who have self-regulatory skills and can achieve academic success as long with life success. Wolters (2003) identified the self-regulated learners as “the persons who have the cognitive, metacognitive abilities as well as motivational beliefs required to understand, monitor, and direct their own learning”.

Boekaerts and Corno (2005) have argued that students must be actively engaged in the learning process. Students should be able to plan, monitor, regulate, and control their cognitive procedures with respect to their attitudes and behaviors. Therefore, students need to possess high metacognition skills to engage actively in learning and achieve success. Achieving excellence in academic performance is founded on the student's academic intrinsic motivation, which plays a vital role in the learning process and human's life activities. Learners are not only information recipients from psychologists' viewpoint, but they must be active participants in the process of learning, which requires full engagement and deep involvement of students. Modern statistical investigations proved that optimum learning outcomes are achieved when learners possess the intrinsic motivation and true interest in the subject they learn ( Cerasoli et al., 2014 ; DePasque and Tricomi, 2015 ; Ryan and Deci, 2000 ). Learners equipped with intrinsic motivation can face academic challenges and difficulties with the appropriate flexibility and adaptability.

College-aged students can take advantage of using strategies under metacognition strategies. Moreover, metacognitive skills can be understood by students for enhancing their learning ( Fisher et al., 2015 ; Barenberg and Dutke, 2019 ). Pintrich claims that students will more likely to use different types of strategies for learning, problem-solving, and thinking. Furthermore, Pintrich (2002) argues that there is a need to teach metacognitive knowledge comprehensively. Two recent studies have presented particular strategies for enhancing metacognition ( McGuire, 2015 ; Medina et al., 2017 ). The relationship between the metacognitive level of students with their demographic attributes including academic achievement and grade point average (GPA) is also examined ( Özsoy and Ataman, 2017 ; Mokhtari et al., 2018 ). Higher cognition knowledge was observed among undergraduate students ( Erenler and Cetin, 2019 ), whereas Medina et al. (2017) have found higher knowledge of cognition among graduate students as compared to undergraduates.

The commitment of the teachers is considered as the principal indicator to endorse failure or success, in the education system. Due to minimal commitment of the teachers, students tend to lose the level of self-efficacy. In this way, students switch the deeper strategic approach to learning and move in the direction of surface learning approach, in the first year of education ( Güvendir, 2016 ). Most of the teachers do not assist or develop the motivation of the students appropriately, which reduces the motivation of the students. Therefore, behavior of the teachers is important to increase the motivation of the students. Specifically, behavior of autonomy tends to increase the motivation within the students while the control behavior decreases it ( Hallinger et al., 2018 ). Moreover, the learning atmosphere and environment are important for the motivation of the education rather than teachers' behavior and individual students. Similarly, the practices of the institutes and perception of the class mates are likewise important ( Hanus and Fox, 2015 ). Furthermore, it is observed that the major downside of the extrinsic motivation is its tentative nature. The extrinsic motivation disappears when the reward or prize is achieved ( Hofferber et al., 2016 ).

The study, therefore, aims to explain the relationship and impact of metacognitive awareness and academic motivation on student's academic achievement. Following research questions are constructed to achieve the aim comprehensively;

  • 1. Is there any significant difference in (academic achievement, metacognitive awareness, and academic motivation) related to Gender differences?
  • 2. Is there a relationship between metacognitive awareness (metacognitive knowledge and metacognitive regulation) and academic achievement?
  • 3. Is there a relationship between academic motivation (intrinsic motivation-extrinsic motivation) and academic achievement?
  • 4. Is there a relationship between metacognitive awareness (metacognitive knowledge and metacognitive regulation) and academic motivation (intrinsic motivation and extrinsic motivation)?

The importance of this study is to provide the insights about the factors which impacts upon the academic achievement of the students in Ajman University. Firstly, the exploration of the concepts related to the metacognition will help the literature in the settings of educational institutes. Secondly, this study adds value to the literature on motivation as the concept of intrinsic and extrinsic motivation among the students is also the focus of this study. Thirdly, this study develops the concept about the academic achievement of the students, in the context of Ajman University. Hence, this research work should add value to the lives of university students to increase the level of academic achievement among the students of Ajman. Moreover, the outcomes, implication, and suggestions of the study should provide an advantage to the administrators of the university as well, to develop the strategy to improve the teacher's affective support among the teachers of Ajman.

2.1. Metacognition awareness and academic motivation

Several studies have indicated a strong relationship between metacognition skills and intrinsic motivation. These studies linked the success of academic involvement of students to their intrinsic motivation and application of sound and fruitful metacognition strategies, in comparison to their fellow students who have no intrinsic motives ( DePasque and Tricomi, 2015 ; Efklides, 2011 ). Pintrich and DeGroot (1990) believed that metacognition strategies are essential for success in the learning process; however, academic success is not only dependent on these strategies. The type of metacognition strategies and intrinsic motivation play a major role in the students' academic achievement. Furthermore, students with intrinsic motivation are capable of engagement in metacognition strategies for continuous planning, assessment, and evaluation of their progress in academic performance. The positive correlation between motivation and self-appeared to be one of the main pillars of the self-learning process.

According to Ibrahim et al. (2017) , the metacognitive strategy is further considered as one of the basic pillars of academic performance and learning excellence. This shows that metacognition assists a learner in appropriately planning, regulating, organizing, and calibrating his or her cognitive procedures and intellectual capabilities. Negovan et al. (2015) have classified metacognition into metacognitive regulation and metacognitive knowledge. Metacognition regulation indicates the actual activities of a learner to enhance memory and learning such as evaluating monitoring and planning. Metacognitive knowledge refers to a learner who identifies his or her own cognitive knowledge based on conditional knowledge and declarative process ( Young and Fry, 2008 ). These strategies are strongly associated with intrinsic motivations, learning advancement, the adoption of adequate strategies based on the task demands, learning outcomes and reading comprehension, and developing an association between previous and new knowledge.

Metacognition is also categorized as higher-order thinking that engages active control over the cognitive procedures involved in the learning process ( Barnes and Stephens, 2019 ). It is also an essential strategy associated with academic achievement and problem-solving abilities. The development of modeling strategies of students is influenced by metacognition when the effects of self-checking, cognitive strategy, awareness, and planning are considered ( Vettori et al., 2018 ). Students who carry-out better self-check reflect higher development in their modeling abilities as compared to those who are less skillful in self-checking. The development in modeling competencies is mediated by planning skills and cognitive strategy. Students with increased skills carried out modeling better after some experience is achieved. On the contrary, the metacognitive and cognitive activities did not occur sequentially in the procedure through which planning activities are most common, while prediction activities are least common ( Hidiroğlu and Bukova Güzel, 2016 ).

2.2. Academic achievement and metacognitive awareness

Some researchers have reported the influence of metacognitive on academic achievement ( Bogdanović et al., 2017 ; Abdellah, 2015 ), while others view that explicit metacognitive training can enhance students' metacognition skills and believed that metacognition skills promote and correlate significantly with students' academic performance or achievement ( Nbina, 2012 ; Nzewi and Ibeneme, 2011 ). Several studies have illustrated that students demonstrated high metacognitive awareness skills by reaching a high level of academic achievement, while students with poor metacognitive awareness skills have illustrated the lower level of academic success ( Narang and Saini, 2013 ; Kocak and Bayaci, 2011 ). Therefore, metacognition can be used as a strong predictor of academic level. Several studies have shown the positive impact of training on students with poor metacognitive strategies. Those students can benefit from training to improve their metacognitive and academic performance ( Nbina, 2012 ; Nzewi and Ibeneme, 2011 ; Rezvan et al., 2006 ). Other studies have shown a negative or no relationship between metacognitive awareness and academic achievement ( Cubukcu, 2009 ; Sperling et al., 2004 ).

Many studies illustrated the positive relationship between intrinsic motivation and academic achievement. These studies pointed out that, intrinsic motivation plays an essential role in the student's performance and academic achievement. These studies have also found that students with high academic intrinsic motivation had achieved academic success easier than others who have the lower academic intrinsic motivation ( Lepper et al., 2005 ; Deci and Ryan, 1998 ; Gottfried, 1985 , 1990 ).

Metacognition positively influences problem-solving skills, which comes from studies in other domains ( García et al., 2016 ). Differentiations are observed between inaccurate and accurate students in the metacognitive process during solving math problems, even though students spent little time representing or organizing information ( García et al., 2016 ). Accurate students pay substantial attention to time planning so they do not evaluate their results and progress. Astonishingly, metacognitive training is majorly beneficial for low achievers as it enables them to advance and solve a similar number of tasks ( Karaali, 2015 ). Students usually get help with self-reflective and metacognitive activities emphasized learning comprehensively and motivated and engaged within the study ( Karaali, 2015 ). On the contrary, the contribution of metacognition in the problem varies for students with and without learning complexities. Metacognition does not work well with learning complexities even when associated with the mathematics problem ( Al Shabibi and Alkharusi, 2018 ). For instance, students with learning complexities show a much lower mean score to identify the sequence of steps for solving the activities as compared to those regardless of learning complexities ( Al Shabibi and Alkharusi, 2018 ).

2.3. Metacognition awareness, academic achievement, and gender differences

Previous studies on gender differences in self-regulation and metacognition have been generally inconsistent. Jenkins (2018) has reported that male students use more superficial learning tactics as compared to female students, whereas Nunaki et al. (2019) have indicated that female students utilize self-monitoring goal setting and planning as compared to male students. Jenkins (2018) has studied gender differences to evaluate academic metacognition and motivation. The study has used strategies that are used by students to actively change their learning capabilities. Male students show higher scoring in their use of rote-learning strategies as compared to female students and indicate no gender differences in any of the other superficial learning strategies.

Alghamdi et al. (2020) have examined gender differences in self-regulated learning by identifying metacognition of students to several other self-regulated learning strategies, which include time management, elaboration and effort, rehearsal, and organization. In general, female students report higher scores as compared to male students in different strategies of self-regulated learning, which include metacognition. Arum (2017) has claimed that awareness must be owned by students at every step of his thinking for improving metacognition skills. The student will be aware of his thinking procedure and assess him or herself to the outcomes of his thought process so that it will reduce the mistake of a student to solve the problem. Purnomo and Nusantara (2017) have indicated that the concept of metacognition is an estimation of an individual's thinking, including metacognitive skills and metacognitive knowledge. In addition, Trisna et al. (2018) have indicated that metacognition allows a student to be aware of the thinking process by regulating and rechecking the thinking process. Sometimes, there is a concept error on the information acquired by the student in the learning process. The information provided by the lecturer is not like the information that is thought by students. In this instance, metacognition shows the thinking stage of students for reflecting on the way of thinking and the outcomes of thinking. There is an important role of metacognition in the procedure of academic learning, specifically in understanding the concept. A conceptual framework has been constructed to present the relationship between variables discussed aforementioned ( Figure 1 ).

Figure 1

Conceptual framework.

3. Material and methods

Ethical approval.

IRB # D-H-F-2020-May-28, Ajman University, United Arab Emirates.

3.1. Study design

The descriptive and correlational study design has been employed to determine the impact of metacognitive awareness, intrinsic motivation, and extrinsic motivation on the student's academic achievement.

3.2. Participants

A purposive sample consisted of 200 students (140 females and 60 males) studying sociology in the College of Mass Communication and Humanities at Ajman University, UAE during the academic year 2015–2016. The range of the age varies between 20 and 29 years, with an average age of 23 years. The survey was conducted between December 2016 and February 2017, covering students studying courses of social psychology and social problems (second, third and fourth years), who responded to the two questionnaires on a voluntary basis. Administration time ranged from 25-40 min. Student's names were not included to ensure confidentiality.

3.3. Instruments

3.3.1. academic motivation scale.

Regina (1998) has proposed this scale based on the results reached in several previous studies. This scale has been translated into Arabic by the researcher to be used in this study and facilitate the students. The scale consists of 56 items graded on a five-point rating scale. It covers six factors: four extrinsic motivation factors including authority expectations, peer acceptance, fear of failure and power motivation, and two intrinsic motivation factors including mastery goals and need for achievement. External motivation drives the intrinsic motivation as compared to undermine it and it has positive influence specifically when students possess low levels of intrinsic motivation in spite of the negative notions on extrinsic motivation.

The scale validation was made by sending the scale to six different arbitrators, who were educational experts specializing in psychology, language, and measurement. Based on the experts' suggestion, eight items were deleted from the original scale. Therefore, the final form of the scale consisted of 48 items, eight items for every factor. Consistency validity was tested by the correlation coefficients ranging from 0.31 to 0.68, which were all statistically significant. The scale reliability was found by using Cronbach alpha, which was: mastery goals (0.73), need for achievement (0.77), authority expectations (0.75), peer acceptance (0.71), fear of failure (0.73) and power motivation (0.72).

3.3.2. The metacognitive awareness inventory (MAI)

Schraw and Dennison (1994) have designed the MAI to determine the adults' metacognition. The MAI consists of 52 statements rated based on the Likert five-point scale, covering two factors of metacognitive: metacognitive knowledge (17 items) and metacognitive regulation (35 items).

3.3.3. The MAI validation and reliability

The MAI validation and reliability were tested and verified by educational experts in Psychology, language, and measurement. A few modifications were made in response to their suggestions. The reliability of the inventory has been found by using Cronbach alpha: The MAI knowledge was (0.78), MAI regulation was (0.8) and MAI total was (0.79).

3.4. Data analysis

The study has used PLS-SEM to analyze the data collected. Structural equation modeling was applied to identify the relationship between metacognitive awareness and academic motivation. Furthermore, this technique was used to examine the impact of metacognitive awareness and academic motivation on academic achievement.

4. Results and discussion

4.1. gender differences, metacognition, and academic achievement.

Table 1 presents the mean and standard deviation of each of the academic achievement as reflected on the students' cumulative grade point average (CGPA), metacognitive awareness (metacognitive knowledge and metacognitive regulation) and academic motivation (academic intrinsic motivation and extrinsic motivation), based on the data of 200 students. The significance levels of t-tests comparing males and females are also provided.

Table 1

Gender differences in academic achievement, metacognitive skills and academic motivation.

∗p > 0.05, ∗∗p > 0.01.

Results showed no significant differences between female and male students in academic achievements, where the academic achievement for female students was 77.1, while the academic achievement for male students was 80.1. Females obtained significantly higher levels than males on the two scales of metacognitive awareness, as shown in metacognitive knowledge (Female m = 79.1, Male m = 65.5, t (98) = 3.1708, p > 0.01). Also, in metacognitive regulation, females reported a higher score than males (Female M = 121.3, Male M = 111.2, t (98) = 3.7052, p > 0.01). These results are supported by Roeschl-Heils et al. (2003) and contradicted by Misu and Masi (2017) who attributed the differences in metacognitive awareness to gender differences. The activities related to metacognition allow students to develop an awareness of themselves, care about, and also give instructions ( Smith et al., 2017 ). In a classroom, teachers must be aware of the individual differences in the metacognitive awareness level and must provide the teaching by accounting their individual differences so that their metacognitive ability might improve well in the classrooms ( Jaleel, 2016 ). The importance of metacognitive knowledge is that it encompasses information regarding tactics that work effectively for most students and information of strategies that work for diverse learners. Therefore, at the beginning of the semester, students who receive metacognitive training learn early in the semester how to study for a specific subject, which may include activity or tasks strategies.

There were no significant differences in academic intrinsic motivation between female and male students. This result is consistent with the findings of Cerezo et al. (2004) . Interestingly, females also reported a higher academic extrinsic motivation than males (Female M = 156.29, Male M = 163.28, t (98) = 3.6399, p > 0.01), which differ than the result of Cerezo et al. (2004) , who found no difference between males and females in their intrinsic motivation. It should be noted that intrinsic motivation improves innovation, creativity, performance and intellectual ability, resilience and enjoyment, and deep learning process ( Fidan and Ozturk, 2015 ). It has been asserted that academic intrinsic motivation accounted for 19% of the total variance of the study variables. The extent of intrinsic motivation in the academic setting was even better as compared to the extrinsic motivation. However, both intrinsic and extrinsic motivation played a substantial role between academic achievement, metacognitive knowledge, and metacognitive regulation.

With respect to academic intrinsic motivation, no large difference was noticed between male and female students, but females reported a higher-level of academic extrinsic motivation than males. Findings also showed a significant correlation between metacognitive awareness and metacognitive regulation, which is confirmed with the results of Narang and Saini (2013) ; Kocak and Bayaci (2010) ; Young and Fry (2008) ; Coutinho (2007) ; Nietfeld et al. (2005) ; and Sperling et al. (2004) . These studies confirmed that students with high metacognitive awareness demonstrate perfect academic performance compared to students with poor metacognitive awareness. It was also found that students' learning strategies have more contribution to academic success than their awareness of metacognitive knowledge.

In all stages of the educational process, the implementation of metacognitive strategies will improve the cognitive performance and efforts of all students. Teaching should be rapid, understandable, and focused on all metacognition parameters based on the special and developmental learning children needs ( Mastrothanasis et al., 2016 ). To be precise, a greater amount of variance was explained by metacognition of the recognized regulatory learning style as compared to the other styles, which complement the importance of metacognition in order to achieve autonomy learning behavior and regulatory learning behavior ( Rosman et al., 2018 ).

Tables  2 , ​ ,3, 3 , ​ ,4, 4 , and ​ and5 5 present reflective higher-order construct of metacognitive knowledge, metacognition regulation, academic intrinsic motivation, and academic extrinsic motivation. From the findings, it is observed that declarative knowledge (0.72, p < 0.10), procedural knowledge (0.88, p < 0.10), and conditional knowledge (0.87, p < 0.10) are positively and significantly reflected from metacognitive knowledge ( Table 2 ). Similarly, planning (0.77, p < 0.10), information management (0.81, p < 0.10), and comprehension monetary (0.18, p < 0.10) are reflected from metacognition regulation ( Table 3 ). Needs for achievement (0.87, p < 0.10) and mastery (0.41, p < 0.10) are reflected from intrinsic motivation ( Table 4 ). Authority expectation (0.79, p < 0.10), peer acceptance (0.83, p < 0.10), fear of failure (0.73, p < 0.10), and power motivation (0.39, p < 0.10) are significantly and positively reflected from extrinsic motivation ( Table 5 ).

Table 2

Reflective Higher-Order Construct (Metacognitive knowledge).

Table 3

Reflective Higher-Order Construct (Metacognitive regulation).

Table 4

Reflective Higher-Order Construct (Intrinsic motivation).

Table 5

Reflective higher-order construct (Extrinsic motivation).

High metacognitive regulation students considered autonomy strategies as more influential and considered to manage their motivation. Autonomous regulatory learning and autonomous style positively affected performance anticipations and performance across the students' achievement ( Ibrahim et al., 2017 ). However, metacognitive knowledge was not an influential indicator of regulatory learning style and; therefore, it reported in school achievement directly. At this specific educational level, it is observed that students perceived the controlling behavior of parents as influential for their objectives to a significant extent.

It has been provided in the above table that metacognitive knowledge (0.13, p < 0.10) and metacognitive regulation (0.35, p < 0.10) have significant relationship with metacognitive awareness. Metacognitive awareness has a significant and positive relationship with academic motivation (0.29, p < 0.10) and academic achievement (0.41, p < 0.10). Academic intrinsic motivation (-0.20, p < 0.10) and academic extrinsic motivation (0.15, p < 0.10) have statistically significant relationship with academic motivation. Furthermore, academic motivation (0.19, p < 0.10) has statistically significant and positive impact on academic achievement. It is essential to develop influential strategies for facilitating the cognitive procedures as learning is a multifaceted process. Furthermore, a learner is represented by his or her accuracy experience, better judgment, significant ways for improving accuracy, and their metacognition and cognitive process (see Table 6 ).

Table 6

Path analysis.

There is a strong correlation between academic achievement and academic intrinsic motivation ( Pintrich, 2002 ; Ryan and Deci, 2000 ; Wu, 2003 ), and a significant correlation between academic achievement and academic extrinsic motivation. Furthermore, findings showed a high correlation between metacognitive knowledge awareness and academic intrinsic motivation, and a high correlation between metacognitive regulation awareness and academic intrinsic motivation, which agree with the studies of ( DePasque and Tricomi, 2015 ; Efklides, 2011 ; Pintrich and DeGroot, 1990 ). There is a weak correlation between academic extrinsic motivation and either metacognitive knowledge awareness and metacognitive regulation awareness.

4.2. Practical implications

The study has determined the relationship and impact of metacognitive awareness and academic motivation on student's academic achievement. The findings of the present study showed no significant differences between female and male students in academic achievement. However, there is a significant difference in metacognitive awareness. Female students showed a higher level of metacognitive knowledge and metacognitive regulation. Findings found that intrinsic and extrinsic motivations are essentially independent. However, extrinsic motivation does not suppress intrinsic motivation and both showed little compatibility in male students. In contrast, both motivations are compatible or even collaborative in female students. This result is consistent with the nature of females in Arab culture, which is patriarchal societies, in which men hold primary power and authority. In such a society, the female motivation is strongly influenced by many extrinsic factors including, family and professor expectation, peer acceptance, fear of failure and power motivation, which affect their motivation.

Both intrinsic and extrinsic reasons underlie the students' achievement behavior. In this instance, professors must adopt effective methods of teaching which include; interactive teaching and curiosity-based learning, using interesting materials and enjoyable tasks that promote academic intrinsic and extrinsic motivation. The present study incorporates independent assessments of both intrinsic and extrinsic motivations, based on the reasons why students engage in-class learning and provide a valuable complement to traditional assessment of motivation, such as how much students enjoy certain activities or content domains. To overcome poor academic performance, university professors can enhance students' intrinsic motivation and metacognition skills by helping them to set endurable goals, which facilitate learning acquisition and enhance constructive and meaningful involvement in academic activities.

Students' academic performance and achievement depend on the applied metacognitive strategies with respect to their intrinsic motivation. Therefore, these aspects with respect to students' intrinsic motivation in universities must be developed and promoted. Teaching strategies and techniques adopted by university professors should not be limited to deliver information but must encourage more interaction between professors and students and activate the use of metacognition skills as an effective tool of positive impacts on academic achievement. Supporting and improving students' intrinsic motivation by using different and enjoyable non-academic activities supports students' personalities and motivates them to participate and raise their self-concept. These improvements would raise their intrinsic motivations and give them the energy to face complex and multidimensional learning challenges and reach achievement.

Lastly, for better understanding of the effects of metacognitive awareness, academic intrinsic motivation, and academic extrinsic motivation on the university academic achievement, future studies should focus on their effect on the outcomes of the learning process, such as students' qualifications, achieved knowledge and skills, and development of social responsibility. Academic motivation is an important factor in college success. The motivations behind academic constancy vary through many intrinsic and extrinsic factors. Many university students lack the motivation needed to excel in their academic performance and to achieve their goals. Most of the students are studying majors they have not chosen, but because of their parent's desires, which make them lose motivation to learn and achieve.

The traditional teaching methods used by professors are not appropriate with a cognitive revolution that can influence the students' academic motivation. Therefore, professors have a great responsibility to support students to learn and achieve their academic degrees. Also, they must adopt successful methods of teaching to motivate them to learn as much as they can. Professors should use their experiences to design the context and tasks in an attractive way. This study has concluded that metacognitive awareness is a major contributor to success in learning and represents an excellent tool for the measurement of academic performance. This study has found a correlation between metacognitive awareness and intrinsic academic motivation. The findings have provided important implications with regards to the findings of mediation analysis. Firstly, self-extrinsic motivation, and intrinsic motivation are identified as determinants of academic motivation and related with metacognition in students in Ajman University. In addition, it should be realized that the likelihood of motivation and metacognition of students are possible approaches related with student's academic achievement.

4.3. Limitation and future studies

One of the limitations of this study was the sampled participants which belong to one academic program at Ajman University, UAE. Therefore, the findings of this study cannot be generalized to other locations or populations. This limitation, however, shines some light on how different locations and populations may influence the relationships between metacognition, intrinsic and extrinsic motivation and academic achievement. Future studies should adopt other measurement approaches such as the experimental approach. In addition, other sources of self-reported data may include parents, instructors, and peers. This will provide future research with different perspectives and holistically assesses students' learning activities. Future studies may also identify other key features such as causal relationships among the complex constructs that were not evident in the findings of this study. Therefore, it is strongly recommended that an experimental design or a mixed-method approach shall be used to gain more knowledge on how optimal learning occurs.


Author contribution statement.

R. M Abdelrahman: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Competing interest statement

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.


The author is thankful to all the associated personnel, who contributed for this study.

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Metacognition in musical practices: two studies with beginner and expert brazilian musicians.

Rosane Cardoso de Araújo,

  • 1 Department of Arts, Federal University of Parana, Curitiba, Brazil
  • 2 The Brazilian National Council for Scientific and Technological Development (CNPq), Brasília, Brazil
  • 3 School of Fine Arts, Pontifical Catholic University of Parana, Curitiba, Brazil

Metacognition is essential in the musical learning process as it involves understanding the purpose of each task, its planning, execution and evaluation. Considering the relevance of metacognitive processes, our objective in this study was to investigate how expert and beginner musicians manifest and verbalize their metacognitive processes in the context of preparing repertoire for a performance. The method used was a multi-case study carried out in two different contexts: with the five members of a brass quintet made up of professional musicians and with three beginner university violin students. The results obtained indicated that even at different levels of expertise, metacognitive processes were present in the musical practices of participants in the two contexts investigated. It was found that in both cases time management is a component of the preparation process in metacognitive regulation, however, for the beginner violinists in our sample it was a significantly more complex task than for the professional brass players. Regarding the learning monitoring and evaluation processes, it was possible to verify that beginner instrumentalists as well as professional musicians used declarative, conditional and procedural knowledge to carry out and reflect on their musical practices. These results have implications for both the individual and collective study process and for teaching processes. It is also observed that reflective thinking must accompany the processes of individual and collective interpretative-musical practices, considering that the musical results desired by musicians are related to the quality of cognitive, behavioral, affective and motivational undertakings pertinent to control and regulation of metacognitive processes.

1 Introduction

Metacognition occupies a prominent position among the factors involved in learning processes from the perspective of cognitive and developmental psychology ( Flavell et al., 2002 ). According to Flavell (1979) to master metacognitive skills is to understand the purpose of each task, plan how to complete it, consciously apply and change study strategies and evaluate the results of performance and of the learning outcome. Ribeiro (2003) explains that metacognition is higher-order cognition, which guides thinking about thinking, knowledge about one’s own knowledge and the self-regulation of cognitive processes.

Anderson et al. (2001) defined metacognitive knowledge as awareness and knowledge of one’s own cognition, which encompasses strategic knowledge, knowledge about cognitive tasks, including contextual and conditional knowledge, as well as self-knowledge. Thinking about thinking, learning how to learn, cognition about cognition, goal management, coordination and monitoring of mental activities are components of metacognition explored by various authors and indicate that this construct, due to its complexity, is understood as a set of processes ( Jacobs and Paris, 1987 ; Paris and Winograd, 1990 ; Schraw and Moshman, 1995 ; Kuhn, 2000 ; Ribeiro, 2003 ; Joly, 2007 ; Noushad, 2008 ; Chick et al., 2009 ; Benton, 2014 ; Bustos et al., 2014 ; Jordan, 2014 ; Bártolo-Ribeiro et al., 2016 ; Ozturk, 2017 ; Varga, 2017 ). Metacognitive processes allow musicians to engage in the planning and organization of instrumental practice tasks, the monitoring and evaluation of performances and the promotion of self-directed changes.

According to Flavell (1999) , metacognitive regulation operates through three central processes: planning , monitoring and evaluation . These processes are manifested in a dynamic and non-linear way, allowing that, due to cognitive flexibility, elements of the three processes can be explored and combined at different times during a musical learning activity. Planning includes, through anticipatory thinking, outlining tasks, setting goals, mobilizing strategies and resources, and estimating the amount of time and effort invested. According to Portilho (2011) , people generally develop a plan to execute a task when faced with a problem or a new situation. This organization will guide cognitive activity. Monitoring takes place through constant self-observation and allows the objectives and strategies to be revised so that the individual can achieve the stipulated goals. During monitoring, metacognitive knowledge reveals learners’ levels of awareness of their own functioning, manifesting itself in three forms: (i) “declarative knowledge,” which is the students’ understanding of what they know in terms of information, skills, strategies and resources; (ii) “conditional knowledge,” which is the discernment of when to mobilize a certain practice strategy; and (iii) “procedural knowledge,” which is the understanding of how to perform a task using specific procedures ( Portilho, 2011 ). Finally, the evaluation involves judging their own behavior, verifying the quantity and quality of progress made, as well as the relevance of the resources and strategies used.

There are also recent theoretical models of metacognition, for example, the studies developed by; Drigas and Mitsea (2020a , b) . The authors present a holistic and muti-scientific approach to identify the basic pillars of metacognition. They refer to eight fundamental pillars: (1) Deep theorical knowledge on our cognition; (2) Operation knowledge about the functionality of cognitive abilities; (3) self-monitoring; (4) self-regulation; (5) Adaptation; (6) Recognition; (7) Discrimination; (8)Mnemosyne. The authors argue that all the pillars are interdependent and function with some degree of autonomy, since that any improvement or malfunction in each pillar can affect the metacognitive mechanism as a whole. They also propose the use of a mindfulness model, which includes strategies to develop metacognitive skills, which increase the level of self-organization and awareness of individuals.

Beginner and expert musicians explore and improve metacognitive skills by reflecting on their study processes from the moment before direct practice, during the study and when verifying the results obtained. This process influences the quality of learning and motivation in individual and collective musical achievements. These findings reflect those of previous studies examining the relationship between musical learning and metacognition ( Hallam, 2001 ; Jørgensen and Hallam, 2016 ; Ordóñez, 2016 ; Power and Powell, 2018 ; Concina, 2019 ; Veloso and Araujo, 2019 ). Hallam (2001) carried out a comparative study on the development of metacognitive skills between 22 professional musicians and 55 novices (aged 6–18), during the preparation of a repertoire. She found that professional musicians had well-developed metacognitive skills to identify their strengths and weaknesses, evaluate tasks and identify strategies to optimize performance, while novice students had less developed strategies that did not always optimize performance. The focus of our research, as well as in the investigation by Hallam (2001) , was the study of the metacognitive processes of professional and beginner musicians, in musical practice and learning. Our sample, however, although limited by its small size in relation to Halam’s study, was original in carrying out the research in the Brazilian context, with a group of professional musicians, members of a brass quintet ( n = 5) and a group of university students who were beginning to study the violin ( n = 3). We do not seek to replicate the relevant study carried out by Hallam (2001) , but we seek to follow our own methodology, to later contribute to the results already achieved by this author. The general objective of our study was to investigate how profissional musicians and beginner college musicians manifest and verbalize their metacognitive processes in the context of studying and preparing of a musical repertoire. Here, we present the methodological procedures applied to each case study, the results of the two studies (separately), and the discussion of the results cross-sectionally.

The method used was a multi-case study, carried out in two different contexts: (i) with the five members of a brass quintet made up of professional musicians and (ii) with three beginner college violin students. The data was collected in Brazil, in the city of Curitiba (southern Brazil).

2.1 Participants

The first case study (Case 1) we present was carried out in 2022, with a brass quintet (two trumpet players, a horn player, a euphonium/trombonist and a tuba player - all men). All were professional and experienced instrumentalists, as they all had undergraduate and postgraduate degrees in music and worked as teachers and performers. They had been playing together regularly since 2017. The musicians’ ages ranged from 30 to 50 years old.

The second case study (Case 2) was carried out between 2018 and 2019 with three beginner university violin students (one woman and two men), aged between 19 and 22 years old. The three participants in this second case study were university students. They had never studied violin. One student was a music student and played electric guitar. The other two studied computer science and had no formal musical knowledge. All participants had small paid jobs outside the university.

2.2 Procedures

Data from case 1 come from the systematic observation of two trials and two semi-structured interviews in focus groups (conducted in Portuguese). The interview questions were developed based on the theoretical model of metacognitive regulation. 1 This model includes the stages of planning (involving task selection, time organization, effort forecasting and outlining practice goals and strategies), monitoring (with emphasis on metacognitive experiences and knowledge), evaluation and self-reaction (involving domain parameters, performance standards, causal attributions and changes in the course of actions) ( Flavell, 1979 ; Schraw and Moshman, 1995 ).

The data for Case 2 were collected through observation of the beginner college violin students in the classroom context, because we understood that the relationship with the teacher could have a significant influence on the students’ learning and metacognition, since they were all beginners on the violin. The study included class observations and semi-structured interviews with students. The interview guide was based on the Motivated Strategies for Learning Questionnaire (MSLQ), developed by Pintrich (1991) .2 Pintrich et al. (2000) highlights in his MSLQ model: (a) students’ knowledge of general strategies for learning to think, (b) how, when and why to use different strategies, and (c) what is the relationship between cognitive components and motivational of each individual. Thus, the interviews used in case study 2 had two main sections: (1) the first section, which included questions about students’ motivation and expectations, their beliefs about their abilities to succeed in studying the violin, (2) and the second section included questions about the metacognitive process - planning, monitoring and evaluation - to verify learning strategies, cognitive and metacognitive skills. In this section, the questions were divided into 3 parts: about the participants’ study process, about their knowledge of the content covered in violin classes and about seeking help to assist their study with the teacher and classmates.

The interviews in both case studies were audio recorded and transcribed in full. Case observations were carried out: (1) through video recording of professional musicians’ rehearsals; and through notes in an “observation notebook” of beginner college violin students. The analysis of the material includes the content analysis steps identified by Bardin (2011) : pre-analysis; categorization; treatment of results, inferences and interpretation. Analysis categories were defined a priori - Planning, Monitoring and Evaluation – and were selected from the data through semantic proximity ( Bardin, 2011 ).

3.1 First study: professional musicians from a brass quintet

During the data collection period, the ensemble was preparing for a recital. The program included the following musical pieces: “Go!,” by Anthony DiLorenzo; “Largo,” from Symphony n. 9, by A. Dvořák, “Bachianas Brasileiras n. 5,” by H. Villa-Lobos, “Spiritual Waltz,” by Enrique Crespo, “bRUMBA!,” by James M. Stephenson, “Fire Dance,” by Anthony DiLorenzo and “The Knight of the Hill” (1st mov.), by Giancarlo C. D’addona. The quintet performed a weekly rehearsal of approximately 2 hours. The dynamics of the rehearsals involved an initial collective warm-up session, a repertoire practice session – in which different practice strategies were mobilized, with an emphasis on part-whole strategies ( Jørgensen and Hallam, 2016 ) and recital simulation – and a session closing, when individual reflections, perceptions and evaluations were shared in the group. In data collection, systematic observations of two rehearsals were carried out respecting the ensemble’s regular schedule; the two semi-structured focus group interviews were carried out in person immediately after the rehearsals.

In the interviews, when asked about the dynamics of rehearsals, the musicians highlighted the adoption of three main strategies: organizing the practice in line with the group’s artistic programming, the management of practice time considering the distribution of warm-up tasks and repertoire practice throughout the rehearsal, and the complexity of the musical material they were working on (giving more rehearsal time to challenging repertoires for the group). In this regard, the three properties of the goals cited by Schunk (2001 , 2014) were explored: specificity, proximity and difficulty (see Table 1 ).

Table 1 . Property of goals.

Planning musical practice requires setting goals, which is understood as what you want to achieve in a conscious and intentional way. The data regarding the selection of tasks and the delineation of goals for the quintet’s rehearsals revealed the group’s consolidated experience, both in carrying out the warm-up exercises and in practicing the repertoire.

In the observations of the group’s rehearsals, it was possible to collect data on the instrumentalists’ initial contact with the rehearsal space, the first dialogs between the musicians, the warm-up session (the initial moment of the rehearsal) and the repertoire practice session (the central moment of the rehearsal). According to Portilho (2011) , when a new situation or problem arises, people organize a plan to regulate the execution of the task. Thus, planning activities requires flexibility, considering the challenges that may require redirecting objectives and tasks during the rehearsal. Specifically regarding the planning of the repertoire, the following factors could be verified:

a. Consideration of interpretative-musical and technical-instrumental challenges as criteria for selecting the repertoire: “(...) when we chose to be part of this group, we also set ourselves challenges, we did not want to go out and do more of the same” (Trumpet player 1, interview 1).

b. The need for development and improvement of skills: “We really wanted pieces that would make us grow as musicians, and that’s what happened” (Trumpet player 1, interview 1). “ We have played Bachianas Brasileiras n.5 since 2016 until today in all rehearsals... and we still manage to find some issues to improve ” (Trumpet player 1, interview 1).

Monitoring consists of deliberate observation of the performance of musical practice activities. In this study, the monitoring of the brass quintet musicians was verified based on the three main types of knowledge that integrate metacognitive processes: declarative knowledge, procedural knowledge and conditional knowledge. Monitoring strategies based on metacognitive knowledge were verified by observing dialogs and verbalizations carried out during rehearsals and reports offered by musicians during collective interviews (see Table 2 ).

Table 2 . Examples of types of metacognitive knowledge.

In addition to metacognitive knowledge, another phenomenon deserves to be highlighted: metacognitive experiences, that is, conscious perceptions associated with performing a task ( Flavell, 1979 ; Schraw and Moshman, 1995 ; Ribeiro, 2003 ; Veloso and Araujo, 2019 ). Data collected from observing the rehearsals suggested that the metacognitive strategies used by the musicians during collective practice included self-questioning (thinking out loud) “I think my [G] is low” (Trumpet player 1, rehearsal 1) and with evaluative inferences, “I was really making a mistake!,” “(...) yeah, now it’s [good] [expressions of agreement in the group] ” (Trumpet player 1, rehearsal 1), “He was! Now it was. Now it’s fitting!” (Trumpet player 2, rehearsal 2).

Data from the interviews made it possible to relate aspects of individual study and collective study in overcoming challenges based on the verbalization of the strategies employed and personal perceptions of performance ( Ribeiro, 2003 ) - “So we practice at home, and how many times have we said that, this phrase here, right: ‘gee, but I practice at home and at home everything works out’ [expressions of agreement in the group]” (Trumpet player 1, interview 1). The sense of collective achievement was another aspect related to metacognitive experiences, particularly in situations where the musicians realized that they had overcome a challenge: “That was an achievement! We were overjoyed! [Expressions of agreement in the group]” (“Trumpet player 1,” interview 1).

In the evaluation , the expression of opinions about individual and collective performance was frequently observed throughout the rehearsals. Assessments were based, among other factors, on comparative parameters, considering data from the group’s performance history: “It was the best time we have done so far [expressions of agreement in the group]” (Trumpet player 2. rehearsal 1).

Data from observations and recordings of rehearsals also indicated that there were collective evaluation processes by participants regarding the group’s performance:

• Regarding rhythmic precision: “(...) it was a bit off, right? [Expressions of agreement in the group]” (Trumpet player 1, rehearsal 1).

• About technical-instrumental variables: “[It’s difficult] to measure this breathing...” (Trumpet player 2, rehearsal2).

• About nuances of dynamics: “Is it possible for you [trombonist] to reduce it a little less?” (Horn player; rehearsal 2).

• About agogic inflections: “(...) here it’s just a ritenuto and continues. We’re doing a fermata...” (Trumpet player 1; rehearsal 2).

As a result of the evaluation processes, self-reactive initiatives were observed. In this sense, it is possible to highlight: the conscious and deliberate repetition of specific passages, the study in parts guided by the analysis of structuring aspects of the music and the collective resolution of interpretative and technical-instrumental problems, based on dialog and experimentation with strategies.

Summarizing, the data presented here highlights the occurrence of cognitive processes, affective and motivational undertakings pertinent to the processes of metacognitive regulation, resulting from planning (outlining goals and activities), monitoring (metacognitive experiences and knowledge), evaluation and self-reaction (evaluative inferences and self-reactive initiatives) in musical practice and learning of the investigated chamber ensemble.

3.2 Second study: beginner violin students

During the lesson observations, it was found that the teacher acted in a way that encouraged the students to learn reflectively and autonomously, leading them to develop their metacognitive processes. The interviews served to confirm how the three students developed their metacognitive processes while studying the violin in their individual musical practices. The students’ study practice was based on the instructions given by the teacher weekly. Participant 1 (P1) spent an average of 6 hours a week studying the violin. P1 was in the eighth term of his computer science course. P2 spent an average of 1 hour a week studying the violin, and was in the sixth term of his bachelor’s degree in music production; P3 was in the seventh term of his computer science degree and spent an average of 8 hours a week studying. It was possible to observe, therefore, that the beginning students dedicated fewer hours to studying than the professional musicians in case 1 (who had more than 8 h of weekly practice). In the first part of the interview, about students’ motivation for learning the violin, it was possible to identify that they chose to learn the instrument mainly through intrinsic motivation. P1 commented: “ I like playing the violin ,” “ I want to learn new skills ,” “ It’s fun .” P2 and P3 also stated that they did not feel pressured to study the violin, as they were learning because they liked playing the instrument.

To analyze the metacognitive processes, the first group of questions focused on the planning stage. Participants were asked about planning their studies. Initially, they mainly highlighted planning regarding the place and time of study. P1 and P3 reported that they studied at home, with the concern of studying “inside the room, so as not to disturb the neighbors. No later than 8 or 9 pm.” Both also said that the ideal would be to study inside the classroom where the lessons take place, because “it’s a large place, with good acoustics and quiet, and it does not disturb people with the noise,” said P3. P2, who attended the place where the violin lessons were held, said that he studied in the same place as the lessons, but that “any place that is quiet helps; it does not matter where it is, because if you study in a concentrated way, what happens around you does not get in the way.” When asked if the place where they studied favored their development or if they would like to be able to study elsewhere, P2 and P3 replied that “it would be better to study in the room where the classes take place, but for convenience I study at home.” P1 said he did not know, because for him the only ideal place to study would be “where there was someone else to correct my mistakes!.” At this stage of planning, in addition to identifying where they planned to practice, participants also indicated that they knew what they planned to practice, highlighting technical exercises and repertoire pieces worked on that week by the teacher.

Regarding the management of time to practice, still in the planning stage, P2 said: “I cannot organize my study time in this course. In other classes, I even try to be more organized, but without much success. I have serious organizational problems.” P1 and P3 said that they were able to plan their time to practice. P1 said that “ when I manage to plan my day, I fit in 20 or 30 min a day to practice the violin ” and P3 explained that during the week, she was able to practice in the morning, as the degree course she was taking had afternoon and evening classes. According to Benton (2014) , among the metacognitive skills advocated by several authors, task planning is an important factor. Thus, from the data collected, it was possible to observe that building a favorable environment for studying the violin and organizing practice time, even with difficulties, were processes sought by students.

The monitoring stage was addressed in the interview, especially focusing on the content covered in violin classes. When asked if they were able to understand and perform the content covered in class, P2 and P3 said that the repertoire covered was easy, as they already knew some of the songs. However, they argued that they found it difficult the way the teacher asked them to play the pieces, with the technical specifications he demanded and the sound he required (see Table 3 ).

Table 3 . Study monitoring.

For Flavell (1979) monitoring includes a variety of actions and interactions from four classes of phenomena: (a) metacognitive knowledge, (b) metacognitive experiences, (c) objectives (or tasks) and (d) actions (or strategies). In addition, according to Flavell (1979) and Ribeiro (2003) metacognitive experiences occur in situations that stimulate attention and include conscious thoughts. In fact, these reflective processes were observed in the participants’ responses, directly or, at times, implicitly. In this sense, Flavell (1979) indicates that these situations that allow reflection on thoughts and feelings and the quality control of metacognitive experiences, can cause elements of cognitive knowledge to be added, deleted or revised.

With regard to evaluating their performance in the practical sections, participants indicated that they should focus more of their efforts on improving the technical aspects presented by the teacher (see Table 4 ).

Table 4 . Performance evaluation.

Benton (2014) reports that many beginner music students of school age simply practice by playing entire pieces repetitively, without detecting errors and stopping to correct them, drawing attention to an erroneous way of practicing, as it generates a counterproductive effect since students reinforce errors. In our study we also found this situation in P2’s interview. In this sense, deliberate practice should be reinforced in learning, because repetition without reflection can generate errors and inappropriate techniques, instead of correcting them. This only highlights the importance of conscious deliberate practice, using all the metacognitive factors related to monitoring and evaluating cognition, bringing correction strategies to verify the best way to change strategies that have not had an effect, which are advocated by Schraw and Dennison (1994) . According to Benton (2014) , when an individual uses metacognition, their object of thought is the personal act of knowing or the intellectual process of obtaining knowledge.

The final questions in the interview were about seeking help to practice and to improve the performance. The participants were asked if they sought help from their teacher and peers (peer review) when they had any doubts. Everyone answered positively highlighting that they felt free to ask their teacher any questions they had. This was confirmed during class observations. According to them, the freedom to talk to the teacher had important consequences for learning. P3 said: “I feel that here I’m learning with much more quality. The teacher wants us to learn properly. The teacher does not come here just to keep to the timetable and get paid at the end of the month. He wants us to learn!.” The participants also confirmed that they used to exchange information with colleagues. P1 commented that he talked to other colleagues, often by text message on his cell phone: “Sometimes I even send them a photo of the score so they can help me read the notes.” This way of practicing, exchanging information with colleagues, using technological resources, such as cell phone photos, exchanging messages, therefore, is a specificity found in this study on the way in which beginning university students practiced. These specificities were not found in the data from other studies that we reviewed on the metacognitive processes of young instrumentalists.

Finally, when evaluating their own performance in comparison with each other’s, all participants stated that they noticed individual differences, highlighting that some had faster development in one skill (e.g., tuning), while others developed faster in other skills. (e.g., reading, interpretation, etc.). Concina (2019) states that the relationship between metacognition and expertise is characterized by a gradual process of transfer of learning, where students learn metacognitive strategies within a particular cognitive domain, and then go on to transfer their metacognitive skills to other areas of learning. According to the author, educators can play a key role in promoting this process, encouraging students to apply what they have learned to other areas of learning.

4 Discussion

By analyzing the two case studies, carried out with professional musicians and beginner college musicians, it was possible to verify that, even at different levels of expertise, metacognitive processes were present in the participants’ musical practices and were declared through the musicians’ reflective processes.

About metacognitive processes, in both cases (beginners and experts), it was possible to verify that study organization was a component present of the planning process in metacognitive regulation. However, it was noticeable that, for beginner musicians, organizing activities and managing study time was a significantly more difficult compared to the group of professional musicians. The reported difficulty was often related to external factors, which participants often could not control.

Regarding the monitoring and evaluation processes, it was possible to verify that both the beginning and the professional musicians used declarative knowledge (in the form of self-knowledge), conditional knowledge (awareness of the demands of the tasks) and procedural knowledge (problem-solving strategies) to carry out and reflect on their musical activity. This result follows the direction of the study by Power and Powell (2018) , who, when carrying out their study with children and teenagers, found that increasing awareness of metacognitive processes impacts on improving the quality of learning processes for beginner instrumentalists. In our study, when working with adults, we could question whether beginning adults, when developing metacognitive skills in other domains, could transfer these skills to instrumental learning. This issue, however, could be a topic for further study.

Overall, this research is in line with Hallam (2001) ’s study, which highlighted the difference in the metacognitive processes of beginner students and professional musicians. According to the author, professionals demonstrate advanced metacognitive skills in relation to the construction of performance, covering technical and interpretative elements, as well as issues related to learning, such as concentration, planning, monitoring and evaluation. The author also indicated in her study that, for beginner students, “there was a complex relationship between the development of knowledge and the use of planning strategies” (p. 27).

The results of the two studies we carried out also demonstrated that there is a difference between the metacognitive skills of (adult) beginners and expert musicians. Expert musicians have greater self-reflection about their performance and how to improve the quality of their performances. This result follows the direction of the study by Concina (2019) , indicating that the student’s level of musical experience has an impact on metacognitive skills. According to the author, experienced musicians exhibit more developed metacognitive attitudes and behaviors, highlighting that advanced or professional students can select the most appropriate strategies, understand the level of difficulty and possible challenges of a task, monitor their performance and allocate the amount of time needed to solve each challenge, optimizing their efforts in the learning activity.

Data from the two case studies led us to understand that the development of metacognitive skills allows the instrumentalist to follow their own musical path autonomously. This finding follows the direction of the study by Hallam (2001) . The author indicated that the musician must develop considerable metacognitive skills to be able to recognize the nature and requirements of a task in order to identify particular difficulties and have knowledge of a series of strategies to deal with these challenges. Furthermore, the author points out that it is necessary to know which strategy is appropriate to deal with each task, as well as monitor progress and, if there is unsatisfactory progress, use alternative strategies to finally evaluate learning results and take necessary measures to improve performance. Our results, therefore, add to the results already achieved by Hallam, bringing as a genuine contribution the look at a different geographic and sociocultural context and a different population, which included adult beginner violin students and professional musicians from a chamber group, offering a new perspective to studies on metacognition and musical practice.

5 Conclusion

We observed that in both case studies time management was a component of the planning process in metacognitive regulation, however for the beginner violinists this was a more complex task compared to the group of professional musicians. In relation to learning monitoring processes, it was possible to verify that both adult beginner musicians and professional musicians used declarative, conditional and procedural knowledge to carry out and reflect on their musical practices, however, professional musicians demonstrated that they had greater reflective capacity, a situation resulting from their musician/professional experiences. Regarding the evaluation process, it was possible to verify that professional musicians were able to judge their own behavior in relation to musical progress and the relevance of the resources and strategies used in study practices. Beginner musicians judged their behavior, but did not always know how to evaluate the effectiveness of the strategies used. These results have implications for both the teaching process and the musical study process. In this sense, it is observed that reflective thinking and the use of strategies must accompany individual and collective musical practices for improvements in music studies to occur, since as Mitsea and Drigas (2019) indicate there is a significant co-occurrence between high-level cognitive functions (such as reasoning, critical thinking and problem solving) and the use of metacognitive strategies. We therefore reinforce the idea that metacognition is a relevant process to guide the practice of instrumentalists (in individual and collective contexts), as it involves conscious, reflective and autonomous musical development.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Ethics statement

The studies involving humans were approved by Ethics Committee of the Health Sciences Sector of the Federal University of Paraná - SCS/UFPR. Number: 41065320.6.0000.0102. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author contributions

RCA: Writing – original draft, Writing – review & editing. RSF: Writing – original draft, Writing – review & editing. FDDV: Writing – original draft, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was funded by the Brazilian National Council for Scientific and Technological Development (CNPq - Brazil), award number 309423/2019-8 - Research productivity grant from RA.


The Brazilian National Council for Scientific and Technological Development (CNPq - Brazil) and Universidade Federal do Paraná (Brazil).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1. ^ See the interview questions in: Flávio Denis Dias Veloso. “A prática e a aprendizagem da performance musical em grupos de câmara: uma investigação sob a perspectiva sociocognitiva.” (PhD diss., Veloso, 2022 ).

2. ^ See the interview questions in: Rafael Stefanichen Ferronato. “Um estudo longitudinal sobre autodeterminação e processos metacognitivos na aprendizagem do violino.” (PhD diss., Veloso, 2022 ).

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Keywords: metacognition, metacognitive process, musical practices, beginners musicians, expert musicians

Citation: Araújo RC, Ferronato RS and Veloso FDD (2024) Metacognition in musical practices: two studies with beginner and expert Brazilian musicians. Front. Psychol . 15:1331988. doi: 10.3389/fpsyg.2024.1331988

Received: 02 November 2023; Accepted: 08 February 2024; Published: 22 February 2024.

Reviewed by:

Copyright © 2024 Araújo, Ferronato and Veloso. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Rosane Cardoso de Araújo, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.


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    04 September 2020 Accepted: 29 March 2021 Keywords Problem-solving Metacognitive skills Middle school students Non-routine problem Abstract The purpose of this study is to investigate the metacognitive strategies that middle school students used in the process of solving problems individually.

  4. Strategies for teaching metacognition in classrooms

    Metacognition is thinking about thinking. It is an increasingly useful mechanism to enhance student learning, both for immediate outcomes and for helping students to understand their own learning...

  5. Metacognition: ideas and insights from neuro- and educational ...

    We argue that to improve our understanding of metacognition, future research needs to (i) investigate the degree to which different protocols relate to the similar or different metacognitive...

  6. The Effect of Metacognitive Instruction on Problem Solving Skills in

    Since metacognitive instruction has positive effects on students' problem solving skills and is required to enhance academic achievement, metacognitive strategies are recommended to be taught to the students. Keywords: metacognition, problem solving skills, metacognitive instruction, students Go to: 1. Introduction

  7. Assessing Metacognitive Regulation during Problem Solving: A Comparison

    Metacognition is hypothesized to play a central role in problem solving and self-regulated learning. Various measures have been developed to assess metacognitive regulation, including survey items in questionnaires, verbal protocols, and metacognitive judgments.

  8. A path model for metacognition and its relation to problem-solving

    Metacognition is a powerful predictor for learning performance, and for problem-solving. But how metacognition works for cognitive strategies and learning performance is not clear. The present study was designed to explore how metacognition affected the cognition (learning strategies and problem solving strategies) and different kinds of learning performance involving the development of ...

  9. Metacognition and Metacognitive Learning

    Metacognitive knowledge contributes to successful problem solving over and above the contribution of IQ in task-relevant strategies (Schraw, 1998). Studies have shown that metacognitive knowledge is not strongly correlated with ability, although there does appear to be a modest, positive relation between the two.

  10. Cognitive, Metacognitive, and Motivational Aspects of Problem Solving

    Abstract. This chapter examines the role of cognitive, metacognitive, and motivational skills in problem solving. Cognitive skills include instructional objectives, components in a learning hierarchy, and components in information processing. Metacognitive skills include strategies for reading comprehension, writing, and mathematics.

  11. The Use of Metacognitive Strategies in Solving ...

    Data obtained through problem solving by the subject and then analyzed qualitatively. Through the application of metacognitive strategies, a systematic and well-structured problem solving is implemented based on the logical thinking steps, and allows to the right solution. For students, metacognitive strategies are very important to build a ...

  12. Metacognitive strategies improve learning

    Metacognitive strategies for learning include planning and goal setting, monitoring, and reflecting on learning. Students can be instructed in the use of metacognitive strategies. Classroom interventions designed to improve students' metacognitive approaches are associated with improved learning (Cogliano, 2021; Theobald, 2021).

  13. (PDF) Metacognitive Strategies

    Metacognition Metacognitive Strategies DOI: Authors: Garry Hornby University of Plymouth Deborah Greaves Learning Across Boundaries Consulting Abstract This chapter promotes teachers' use of...

  14. What Is Metacognition? How Does It Help Us Think?

    1. Planning Strategies As students learn to plan, they learn to anticipate the strengths and weaknesses of their ideas. Planning strategies used to strengthen metacognition help students...

  15. TEAL Center Fact Sheet No. 4: Metacognitive Processes

    Examples of metacognitive activities include planning how to approach a learning task, using appropriate skills and strategies to solve a problem, monitoring one's own comprehension of text, self-assessing and self-correcting in response to the self-assessment, evaluating progress toward the completion of a task, and becoming aware of distractin...

  16. PDF The effect of metacognitive strategy training on mathematical problem

    Montague (1992), three most commonly used metacognitive skills during problem solving are self-instruction, self-questioning and self-monitoring. Self-instruction helps children to determine and manage previously used problem solving strategies while working on a problem. Through the introduction of internal dialogues, self-questioning

  17. The influence of metacognition in mathematical problem solving

    Metacognition is an important factor of mathematical problem solving. Metacognition is the ability to monitor and control our own thoughts, how we approach the problem, how we choose the strategies to find a solution, or ask ourselves about the problem, in the other word, it can be defined as think about thinking.

  18. The effect of metacognitive strategies on mathematical problem-solving

    The purpose of this study was to analyze the effect of applying metacognitive strategies on students' mathematical problem-solving abilities. The steps in SLR involve, 1) develop research question; 2) construct selection criteria; 3) develop search strategy; 4) select studies using selection criteria; 5) assess the quality of studies; 6 ...

  19. Metacognition's Role in Decision Making

    Metacognition can help us to think outside the box. More complex decisions require problem-solving, strategies, re-framing, creative thinking, and possibly seeking advice from others.In addition ...

  20. Full article: Effect of use of metacognitive instructional strategies

    The study found that metacognitive instructional strategies are contributing to the mathematical problem solving competence of undergraduate students in facing competitive examinations, and also we have observed that there is a meaningful relation exists in the students in terms of the problem solving achievement level.

  21. PDF Reading comprehension: The mediating role of metacognitive strategies

    Self-management denotes metacognition in action. This is related to mental procedures that take part in "coordinating facets of problem solving" (Paris and Winograd, 1990, p.17). This comprises planning before the task, modifying during the task, and revising after the task. Reading Comprehension.

  22. Metacognitive strategies in solving mathematical word ...

    This study aims to understand the use of metacognitive skills by Rwandan learners while solving mathematical word problems. We interviewed and assessed third-, fourth- and fifth-grade learners from a public primary school. The following three points emerged. First, the metacognitive skills of learners with correct answers were considerably higher than that of those with incorrect answers ...

  23. Metacognitive Mastery in Mathematics Education: Modern Strategies for

    During different phases of problem solving the participant engaged in different metacognitive behaviors whereas the dynamic geometry software supported strategies that are available and/or not ...

  24. Strategies for Improving Learner Metacognition in Health Professional

    Metacognitive strategies are an important variable during thinking processes. 33 These skills need to be made explicit and public to develop critical thinking skills. 33. ... This scaffolding or progressive problem-solving approach is a critical part of developing expertise. It is the gaining of experience for both content but process that is ...

  25. Metacognition in 60 seconds

    Educators define 'metacognition' as the process we all use to better understand our own thinking processes and problem-solving strategies. In the simplest terms it's 'thinking about thinking' or being aware of how we learn best. Metacognitive skills are the techniques we use to learn and to understand ourselves as learners.

  26. Exploring Vandergrift 's Metacognitive Strategies Use and Its Impact on

    The reviewed results were discussed in detail based on various findings of metacognitive strategies components (problem-solving, person knowledge, mental translation, planning and evaluation and directed attention) that highly used by language learners and also its impact during learning listening. In conclusion, metacognitive strategies use on ...

  27. Metacognitive awareness and academic motivation and their impact on

    Metacognition is the ability of learners to take necessary steps to plan suitable strategies for solving the problems they face, to evaluate consequences and outcomes and to modify the approach as needed, based on the use of their prior knowledge. ... Alkharusi H. Mathematical problem-solving and metacognitive skills of 5th grade students as a ...

  28. Frontiers

    Data collected from observing the rehearsals suggested that the metacognitive strategies used by the musicians during collective practice included self-questioning ... (such as reasoning, critical thinking and problem solving) and the use of metacognitive strategies. We therefore reinforce the idea that metacognition is a relevant process to ...