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• Published: 08 June 2021

Metacognition: ideas and insights from neuro- and educational sciences

• Damien S. Fleur   ORCID: orcid.org/0000-0003-4836-5255 1 , 2 ,
• Bert Bredeweg   ORCID: orcid.org/0000-0002-5281-2786 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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• 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.

Introduction

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.

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 .

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.

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 .

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.

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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Acknowledgements

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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Fleur, D.S., Bredeweg, B. & van den Bos, W. Metacognition: ideas and insights from neuro- and educational sciences. npj Sci. Learn. 6 , 13 (2021). https://doi.org/10.1038/s41539-021-00089-5

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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 ).

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.

Declarations

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.

Acknowledgements

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

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Comprehension

Instruction of Metacognitive Strategies Enhances Reading Comprehension and Vocabulary Achievement of Third-Grade Students

The use of metacognitive strategies helps students to “think about their thinking” before, during, and after they read.

On this page:

What we know about teaching reading, the importance of metacognitive strategies, classroom instruction using metacognitive strategies, vocabulary and comprehension, instrumentation, the instruction in the intervention school, the instruction in the comparison school, evidence of effectiveness of the study, final thoughts, about the authors.

Comprehension is the reason for reading, and vocabulary plays a significant role in comprehension (National Institute of Child Health and Human Development, 2000). The question is, What kind of instruction best promotes the development of comprehension and vocabulary? Consider the following reading comprehension lesson (the teacher’s name is a pseudonym):

“What time is it when an elephant sits on a fence?” Mrs. Thornton asks as she begins a reading lesson with her third-grade class. The class laughs and responds in unison, “Time to fix the fence.”

“What makes that riddle funny?” Mrs. Thornton asks. The students discuss the image of a big elephant climbing up onto a fence and the multiple meanings of the word time as the reasons that the riddle is funny.

“Today you will read an expository passage and learn more about elephants. Let’s see what you know about elephants.” Mrs. Thornton asks a series of questions: How tall are elephants? How much do they weigh? Are there different kinds of elephants? What do elephants eat? How do they use their trunks? The students jot down their answers to these questions.

Mrs. Thornton writes a word on the board and says, “This is the word versatile. It comes from two Latin word parts — vert (vertere, to turn) and ile (having to do with) — and means can be turned or used in different ways.” She writes the word parts and the definition on the board under the word versatile and circles the information. “What words do you know that mean the same or almost the same as versatile?” The students respond with useful, flexible, and handy. “What words mean the opposite?” The students respond with inflexible, limited, and restricted. Mrs. Thornton webs the students’ responses on the board. The synonyms are webbed to the left of the definition, and the antonyms are webbed to the right.

“In the passage you will read, the word versatile is used as an adjective. An adjective describes a noun. What are nouns that could be described as versatile?” Mrs. Thornton asks. The students respond with a tool, a person, and a jacket. “What might be described as versatile in a passage about elephants?” The students respond with the elephant’s trunk. “Let’s use the word versatile in a sentence.” The students respond, “An elephant has a versatile trunk.” All the students’ responses are added to the web.

“Now it is time to read the passage. As you read, think about the answers to the questions I asked you earlier. I want to hear you thinking as you read. If you were right about something, let me hear you softly say ‘yes.’ If you need to correct information, let me hear you softly say ‘oops.’ If you learn something new, let me hear you softly say ‘wow’ or ‘aha,’” says Mrs. Thornton. (Carreker, 2004).

The students begin reading the passage silently. Their interaction with the passage becomes instantly apparent as the students’ subvocalized connections, corrections, and collections fill the room.

When the students finish reading the passage, Mrs. Thornton asks them to identify the important elements of the expository passage they have just read: the main idea , the supporting ideas, and the details. She writes the elements on a graphic organizer on the overhead projector.

The students then construct a summary paragraph, with Mrs. Thornton serving as the scribe and writing the paragraph on the overhead. Mrs. Thornton reminds the students that the summary paragraph must have one quarter the number of words of the passage, so they must decide what information is the most important. When the paragraph is complete, the students read the paragraph and answer a few questions, both orally and in written form.

The reading lesson in Mrs. Thornton’s class illustrates lessons that were part of a five-week intervention study. The study was designed to determine the effectiveness of systematic direct instruction of metacognitive strategies on comprehension and vocabulary development, which will be discussed in this article.

Reading comprehension is a multifaceted process (Adams, 1990). For students to adequately comprehend text, they will need an awareness of print, which can be obtained through multiple channels to facilitate word recognition. Carlisle and Rice (2002) found that the lack of phonological sensitivity did impede reading, but other factors came into play as students progressed through the different levels of reading comprehension. These factors are evident because children who receive phonological awareness training do not necessarily become fluent readers (Scarborough, 2001). In addition to decoding skills, students need vocabulary knowledge and metacognitive skills so they can monitor their understanding and reflect on what has been read. Competent readers learn these components simultaneously and fluently. In addition, if either component is inadequate, comprehension can be impeded.

Some teachers may assume that reading comprehension will develop naturally without any direct teaching of comprehension (Denton & Fletcher, 2003). This line of reasoning places reading in the same developmental progression as oral language development. Children are able to acquire speech without formal instruction if given enough exposure to it. This led many researchers to believe that given enough exposure to print the child would experience the same developmental pattern. Nevertheless, research has proven this line of reasoning to be faulty (Gough & Hillinger, 1980; Wren, 2002). Humans have been communicating through speech for thousands of years. We have used written communication for the masses for only several hundred years. This skill must be taught through formal education. Research evidence gathered over the last 20 years has shown that children need to learn phonological awareness, phonemic awareness , awareness of print, phonics , and fluency . Instruction of these components enables the child to decode unknown words. These components are the basics or prerequisites needed for reading. Learning to decode is a means to an end, and that end is to read and understand written communication created by others and to be able to write in order to communicate. In other words, reading instruction does not end when students can decode the words. They continue to need instruction that will support their understanding of what they are reading.

Although metacognition has become a buzz word in education, it seems that the meaning is often assumed. For clarification purposes this study adopted the definition offered by Kuhn (2000). Kuhn defined metacognition as, “Enhancing (a) metacognitive awareness of what one believes and how one knows and (b) metastrategic control in application of the strategies that process new information” (p. 178). This awareness is developmental and lies on a continuum. Proficient readers use one or more metacognitive strategies to comprehend text. The use of such strategies has developed over time as the reader learns which ones are best suited to aid in comprehension (Pressley, Wharton-McDonald, Mistretta-Hampston, & Echevarria, 1998).

Pressley et al. (1998) found that students’ comprehension was not enhanced by merely reading more text. If the students used even one of the strategies, for example summarizing , comprehension was improved. If students were given a host of strategies that they could apply at their discretion, comprehension was greatly improved.

Research evidence will do education little good if findings are not applied in classroom settings. Even though metacognitive strategies are considered to be of value for adequate text comprehension , classroom teachers often fail to teach this process. Pressley et al. (1998) conducted a qualitative research study on 10 fourth- and fifth-grade classrooms to investigate instructional practice regarding reading, writing, motivation, classroom management, use of materials, and instructional goals. Teachers were interviewed twice during a yearlong period and monthly observations were carried out. They found that direct teaching of comprehension strategies was minimal. At the same time, the teachers professed to teach reading comprehension strategies. Some of the teachers did mention strategy use but did so in a passive manner without actively and directly teaching the strategies. Some teachers felt like they taught the use of the strategies by using summarizing , predicting, and imagery as an assessment tool. This, however, does not validate that students used the strategies during the act of reading text. While some teachers used these more often, most of the teachers did not believe it necessary to see that the students were aware of the use of such strategies.

Palincsar and Brown (1984) identified four activities they believe aid in comprehension-fostering and comprehension-monitoring activities. These activities are self-questioning, summarizing, clarifying, and predicting. The technique used by Palincsar and Brown was termed reciprocal teaching (RT). While research has shown that the strategies employed in RT are effective (Rosenshine & Meister, 1994), the strategy tends to be too time consuming for teachers to implement, and modifications are often necessary for implementation (Marks et al., 1993).

Vocabulary acquisition has also been found to be a high predictor of reading comprehension . Biemiller and Slonim (2001) reported that students who were behind in vocabulary knowledge in third grade would remain behind throughout the duration of their schooling. Biemiller and Slonim found that students in grade 2 in the highest quartile of vocabulary knowledge acquired an average of 7,100 root words and students in the lowest quartile an average of 3,000 root words. The authors noted that the lower quartile children could be brought up to grade level, but to do so would take extensive vocabulary instruction and most schools do not promote such programs.

The National Reading Panel (National Institute of Child Health and Human Development, 2000), in summarizing much of the evidence cited above, concluded that there are eight effective or promising strategies: comprehension monitoring , cooperative learning, graphic and semantic organizers , story structure , question answering, question generation, summarization, and multiple-strategy use. The final strategy is the basis for the five-week study.

The purpose of the study was to determine the effectiveness of systematic direct instruction of multiple metacognitive strategies designed to assist students in comprehending text. Specifically, the reading comprehension and vocabulary achievement of 119 third-grade students was investigated to determine whether instruction that incorporated metacognitive strategies led to an increase in the reading comprehension of expository text . In addition, the investigation was also designed to determine the impact of the metacognitive strategies on vocabulary.

This research project took place in six third-grade classrooms in two urban elementary schools in the southwest United States that were deemed demographically and academically equal by the school district’s research department. One school was selected as the intervention school and the other school was the comparison school. The students in both schools were pretested before the five-week study and posttested at the end of the study.

The pre- and posttesting battery involved multiple instruments that were intended to measure academic skill levels of the students both before and after the intervention . Students were pretested using the Word Attack, Letter-Word Identification, and Spelling subtests of the 2001 Woodcock Johnson III (WJIII) Test of Achievement. These three subtests were used to ensure that the groups were of comparable decoding ability.

The two tests that were used to measure the students’ progress in reading comprehension and vocabulary were the 2000 Gray Silent Reading Test, form A and B, and a criterion vocabulary test. Information on all the testing is listed.

Word Attack (Forms A and B). Each part of the WJIII Word Attack test begins with four letters, and the tester asks the student to find the letter that matches a sound. For example, the tester says the sound /p/ and the student matches the letter with the sound. Second, the tester shows the participant two letters and the participant gives the most common sound for the letters presented. Next the participant is shown two pseudowords such as ib, and the student is to pronounce the word as if it were real. Correct pronunciations for difficult words are provided for the tester. Instructions for the test are given only once. Each Word Attack test has 32 items.

Letter-Word Identification (Forms A and B). This WJIII subtest consists of participants identifying letters and moves to identifying real words. Words begin with monosyllabic regular words and proceeds to multisyllabic irregular words . Each subtest has 76 items.

Spelling (Forms A and B). The Spelling subtest of the WJIII begins with participants copying lines and squiggles and later copying letters. Next participants are asked to write uppercase and lowercase letters as given by the tester. Finally, participants are asked to spell real words. The list of words moves from monosyllabic regular words to multisyllabic irregular words. There are 59 items in this subtest.

Gray Silent Reading Test (Forms A and B). This comprehension test uses a multiple-choice format to assess reading comprehension. Children read a set number of passages according to grade level and answer questions at the end of the passage. Grade 3 was required to read 7 passages that moved from shorter to longer. There was no time constraint for this test.

Criterion Vocabulary Test. The vocabulary test was a criterion-referenced test and was constructed using words presented in the 25 lessons (Carreker, 2004). The test was a multiple-choice format where a target word was given, and students had to pick a synonym from four options. There were 40 items on both the pretest and posttest. The words were the same for both tests, but were not presented in the same order.

The students in both schools received 30 minutes of reading comprehension instruction a day for 25 days. The passages for the lessons were from a commercially available reading comprehension curriculum, Six-Way Paragraphs, Middle Level (Pauk, 2000). The series, Six-Way Paragraphs, contains three leveled books-Introductory, Middle, and Advanced-that range in readability from 1st grade to 12th grade. The study chose the Middle Level for the richer content and the readability that was most appropriate for the students. All the passages were expository passages and contained 300 to 400 words. The lessons in the intervention school were supplemented with the direct instruction of metacognitive strategies (Carreker, 2004).

Mrs. Thornton’s lesson exemplifies the instruction that was conducted in the intervention school. There were five parts to the lesson:

• Introduction. The teacher “hooked” or captured the students’ attention by asking a question, showing a picture, stating facts about a topic, or by telling a riddle or joke. The teacher stated the purpose of the lesson and activated the students’ background knowledge of the lesson’s topic by asking a series of questions. The students jotted down their answers in a few words.
• Vocabulary . The teacher introduced one or two vocabulary words. Most words were added to semantic webs as demonstrated in Mrs. Thornton’s lesson. The semantic web connects the part of speech, synonyms , antonyms, and other related words to a new vocabulary word (see Figure 1). Any words with multiple meanings were webbed on a multiple meaning web that delineated at least six different meanings of a word. Mrs. Thornton’s class could have also webbed the word trunk (see Figure 2).
• Reading the story. The students read the story. Before reading, the students reviewed their answers to the questions that the teacher had asked at the beginning of the lesson. The students were reminded to think out loud while they read, as demonstrated in Mrs. Thornton’s lesson. During the study, to control for individual differences in decoding , the teacher read the passages to the students the first week. The students read the passages chorally the second and third weeks. Then the students read the passages silently during the fourth and fifth weeks.
• Summary. The teacher asked students to identify the main idea , the supporting ideas, and the details in the passage. They were written on the board or on an overhead in the form of a “card pyramid” (see Figure 3). The main idea was the top card. The supporting ideas were placed under the main idea. The details were placed under the supporting ideas. After the pyramid was completed, the teacher numbered the cards. The main idea was numbered 1. The first supporting detail was numbered 2, and the details for that supporting idea were numbered 3. The second supporting idea was number 4, with the details numbered 5. The next supporting idea was numbered 6, with the details numbered as 7. The final supporting idea was numbered 8, with the details numbered 9. The students took turns orally summarizing the passage, using the numbered elements in order. The students then constructed a written summary that contained one quarter of the number of words in the passage. Initially, the teacher was the scribe. Eventually, students wrote their own summaries.
• Questions. The teacher asked questions and the students answered orally. Questions were both simple questions, where the answers were found in the passage (e.g., How much can an elephant weigh?), and complex, where students had to tap into their background knowledge (e.g., Could an elephant live in the desert?). The students then answered the six questions from the Six-Way Paragraphs, Middle Level curriculum, which included finding the main idea, drawing conclusions, finding supporting details, clarifying details, and defining vocabulary in context.

Figure 1. The Semantic Web Connects Related Words to the New Vocabulary Word

Figure 2. The Multiple Meaning Web Displays the Many Meanings of a Word

Figure 3. The Card Pyramid Summarized the Main Idea, the Supporting Ideas, and the Details

The lesson plan for the comparison group began with the same introduction activities. The same vocabulary words were introduced; however, vocabulary webs were not used. The students copied the definitions off the board and wrote sentences that illustrated the meaning of the words. The students were not encouraged to think out loud as they read. They did not identify the elements of the expository passage they read or write a summary. They responded to several questions orally from the teacher. Then they copied two or three questions from the board and wrote the answers to those questions. The students also answered the six questions from the Six-Way Paragraphs curriculum.

In order to show efficacy of the intervention , students’ pre- and posttest scores on a criterion-referenced vocabulary test and a standardized reading comprehension test were analyzed to see if there was a statistically significant difference between the two groups. Means and standard deviations for pre- and posttest academic scores can be found in Table 1.

Table 1. Means and (Standard Deviations) for Vocabulary and Reading Comprehension

The intervention group improved significantly over the comparison group in vocabulary, F (1, 117) = 22.521, p < .001, with an effect size of .161, and in reading comprehension, F (1,117) = 4.28, p < .041, with an effect size of .041. While the scores were statistically significant, to better understand the academic gain of the experimental group, a Binomial Effect Size Display (BESD; Rosenthal, Rosnow, & Rubin, 2000) was computed. This index reports the effect size in terms of change attributed to the intervention. The BESD showed the intervention group with a 40% difference in gains in vocabulary between the two groups and a 20% difference in gains in reading comprehension. Given the duration of the study (five weeks) gains attributed to the intervention are noteworthy.

This study provides further evidence to support metacognitive instruction. Students in the intervention school whose vocabulary instruction required generating synonyms , antonyms, and other related words saw greater increase on the vocabulary measure (40%) than students who wrote the vocabulary word and used it in a sentence. It appears that constructing synonyms, antonyms, and other related words creates a deeper understanding of a word, which in turn heightens the ability to recall meaning. The use of vocabulary webs created a more visual representation of the word’s meaning and conceptual understanding (Beck & McKeown, 1991) over the traditional use of memorizing a definition and using the word in a sentence.

Comprehension gains were also found to be greater in the intervention school (20%) compared to the comparison school. This appears to be the consequences of the vocabulary instruction and of the use of metacognitive comprehension strategies that were used in the intervention school. Both groups read the same expository text , answered many of the same questions, and were engaged in the same introductory activities, which included metacognitive strategies such as understanding the purpose for reading and activating background knowledge . However, the intervention school students incorporated more metacognitive strategies during and after their reading. These students were encouraged to think out loud while they read, softly exclaiming “yes,” “oops,” or “wow.” This strategy actively engaged the students in the reading process. After reading the passage, they were instructed to identify the main idea , the supporting ideas, and the details. These elements were written on the overhead projector in the form of a card pyramid. The construction of the pyramid gave them a visual representation of the structure of expository text and helped to reinforce the key elements of this kind of text so that the more the students read expository text, the more aware they became of what to think about while they were reading.

The writing of the summary paragraph was another important metacognitive strategy. The students had to capture the essence of what they had read. They had to review all the information in the passage. Because they could use only a limited number of words in their paragraphs, they had to carefully evaluate the information: What was important and what was superfluous? They had to organize their thoughts in writing in as few words as possible. The instructor served as the scribe until the students were able to write a summary on their own.

Mrs. Thornton described the strategy in the following way:

What amazes me about this kind of comprehension instruction is that it is easy to implement and is great for all my students. My good readers love the challenge of writing the summary paragraphs in as few words as possible. Just yesterday one of my students who consistently uses simple sentences or run-on sentences in his writing proudly produced a summary paragraph with one quarter the number of words in the passage. What was even better than the succinct summary was that he was aware of and verbalized how he had to combine sentences and eliminate unnecessary words in order to get a low word count with all the important information intact.

All the students, especially those who have comprehension problems, now have tools that can help them understand what they read. I love the way students are asked to make connections to what they know. One day, I was working with a group of students with comprehension and language problems. We were reading a passage, and we webbed the word vain using a semantic web. The students were having difficulty generating synonyms and antonyms, but I offered words and scenarios to try to help the students make the connections to words and knowledge in their oral language and background knowledge. Imagine my surprise when one of the students finally said, “Oh, this is like Narcissus when he stared at himself in the river.” She connected this new word to something she already knew. Now, that student and all the other students in the group have the word narcissistic as a synonym for vain.

The challenge is always what to do with poor decoders. I do like the students to work in small groups or pairs. When the students are working in pairs, I group a strong reader with a poorer reader. The stronger reader can read the passage, but the rest of the lesson they can do together.

We spend about 30 minutes a day on the lessons. The students can’t wait to see what I use for my hook each day. The lessons take me some time to prepare, but it is worth the effort when I see the students actively engaged in the lesson and using the strategies we have learned.

It was found that the metacognitive reading comprehension instruction significantly improved the academic achievement of third-grade students in the domains of reading comprehension and vocabulary over the other instruction that was offered to the students in the comparison school. The intensity of the study and the systematic instruction of metacognitive strategies led to positive effects for understanding written text, which is the reason for reading.

Boulware-Gooden is the director of research at the Neuhaus Education Center (4433 Bissonet, Houston, TX 77401, USA). E-mail [email protected].

Carreker is the director of programs at Neuhaus Education Center. Thornhill is a certified academic language therapist, and Joshi teaches reading at Texas A&M University in College Station.

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Adams. M.J. (1990). Beginning to read: Thinking and learning about print. Cambridge, MA: MIT Press.

Beck, I.L., & McKeown, M.G. (1991). Conditions of vocabulary acquisition. In R. Barr, M.L. Kamil, P. Mosenthal, & P.D. Pearson (Eds.), Handbook of reading research (Vol. 2, pp. 789-814). White Plains, NY: Longman.

Biemiller, A., & Slonim, N. (2001). Estimating root word vocabulary growth in normative and advantaged populations: Evidence for a common sequence of vocabulary acquisition. Journal of Educational Psychology, 93, 498-520. doi:10.1037/0022-0663.93.3.498

Carlisle, J.F., & Rice, M.S. (2002). Improving reading comprehension: Research-based principles and practice. Timonium, MD: York Press.

Carreker, S. (2004). Developing metacognitive skills: Vocabulary and comprehension. Bellaire, TX: Neuhaus Education Center.

Denton, C.A., & Fletcher, J.M. (2003). Scaling reading interventions. In B.R. Foorman (Ed.), Preventing and remediating reading difficulties: Bringing science to scale (pp. 445-464). Timonium, MD: York Press.

Gough, P.B., & Hillinger, M.L. (1980). Learning to read: An unnatural act. Bulletin of the Orton Society, 30, 179-196. doi:10.1007/BF02653717

Kuhn, D. (2000). Metacognitive development. Current Directions in Psychological Science, 9, 178-181. doi:10.1111/1467-8721.00088

Marks, M., Pressley, M., Coley, J.D., Craig, S., Gardner, R., DePinto, T., et al. (1993). Three teachers’ adaptations of reciprocal teaching in comparison to traditional reciprocal teaching. The Elementary School Journal, 94, 267-283. doi:10.1086/461766

National Institute of Child Health and Human Development. (2000). Report of the National Reading Panel. Teaching children to read: An evidence-based assessment of the scientific research literature on reading and its implications for reading instruction (NIH Publication No. 00-4769). Washington, DC: U.S. Government Printing Office.

Palincsar, A.S., & Brown, A.L. (1984). Reciprocal teaching of comprehension-fostering and comprehension-monitoring activities. Cognition and Instruction, 2, 117-175.

Pauk, W. (2000). Six-way paragraphs, middle level. Providence, RI: Jamestown Publishers.

Pressley, M., Wharton-McDonald, R., Mistretta-Hampston, J., & Echevarria, M. (1998). The nature of literacy instruction in ten grade-4/5 classrooms in upstate New York. Scientific Studies of Reading, 2, 159-194. doi:10.1207/s1532799xssr0202_4

Rosenshine, B., & Meister, C. (1994). Reciprocal teaching: A review of the research. Review of Educational Research, 64, 479-530. doi:10.2307/1170585

Rosenthal, R., Rosnow, R.L., & Rubin, D.B. (2000). Contrasts and effect sizes in behavioral research: A correlational approach. Cambridge, England: Cambridge University Press.

Scarborough, H.S. (2001). Connecting early language and literacy to later reading (dis)abilities: Evidence, theory, and practice. In S.B. Neuman & D.K. Dickinson (Eds.), Handbook of early literacy research (pp. 97-110). New York: Guilford.

Wren, S. (2002, December). Ten myths of reading instruction. SEDL Letter, 14(3), 3-8.

Boulware-Gooden, R., Carreker, S., Thornhill, A., Joshi, R.M. (2007). Instruction of Metacognitive Strategies Enhances Reading Comprehension and Vocabulary Achievement of Third-Grade Students. The Reading Teacher, 61(1), pp. 70-77.

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Teaching Students to Read Metacognitively

A mini-lesson and anchor chart for showing early elementary students how to monitor their comprehension as they read.

Comprehension is, of course, the whole point of reading. As proficient readers read, they make meaning, learn new information, connect with characters, and enjoy the author’s craft. But as students begin to transition in their skills from cracking the sound-symbol code to becoming active meaning makers, they do not always monitor their understanding of the text as they read or notice when they make errors.

There are several categories of errors that students tend to make as they read. They may insert words where they don’t belong, substitute words as they read (this tends to happen with smaller sight words—reading the  as a ), make phonetic errors, or omit words completely. They may also make fluency-related errors, such as not attending to punctuation, which can lead to confusion about which character is speaking, for example.

Sometimes a student’s error will change the meaning of the text, and other times it won’t. But it remains true that the fewer the errors, the greater the child’s comprehension will be.

When students actively monitor their comprehension, they catch themselves when they make an error and apply a strategy to get their understanding back on track. Monitoring comprehension is a critical skill for both students who are still learning to decode and those who have become proficient decoders but are not yet actively making meaning while they read.

Using Metacognition to Teach Monitoring

When students use metacognition, they think about their thinking as they read. This ability to think about their thinking is critical for monitoring comprehension and fixing it when it breaks down.

When I introduce the concept of metacognition to young children, we talk about the voice in our head that talks back to us while we think and dream. We talk about how this voice also talks back to the story while we read. As we read, thoughts bubble up for us, and it’s important to pay attention to these thoughts. When we’re reading and understanding a story, we talk about how our minds feel good. When we don’t understand a story, our minds have another feeling entirely.

Mini-Lesson on Monitoring

I teach a mini-lesson that has proved effective in helping my third-grade students understand what monitoring comprehension feels like. I use the poem “Safety Pin” by Valerie Worth, which describes this common object, without naming it, by comparing it with a fish and a shrimp—and I don’t reveal the title to the students at first. (The Emily Dickinson poem “I like to see it lap the Miles” can be used with middle and high school students.)

After we read the poem, I ask, “What do you think this is about? What words in the poem make you think that? What do you picture as you read it?” The students generally say they think it’s about a fish or other aquatic animal, and I try to steer them away from these ideas by pointing out other lines in the poem that contradict that image.

After gathering their ideas, I delve a little deeper in my questions, and we discuss how their minds felt when they heard the poem. Most of them say that it felt uncomfortable not to fully understand the poem. I explain to them that something similar happens when we read and make mistakes, or read something that’s too difficult so that we don’t fully understand: Our minds simply do not feel good.

I then reveal the poem’s title and pass out some safety pins, and we reread the poem together. Many of the students find the reveal to be terribly funny. We discuss how our brains feel after learning what the subject of the poem is. I emphasize that as readers, it’s important for us to pay attention to how our brains feel so that we can make sure we truly understand what we’re reading.

Charting It

After this mini-lesson, I share with my students an anchor chart I made based on ideas in the book Growing Readers by Kathy Collins. It has the following questions for students to ask themselves as they read: Does it look right and sound right? Can I picture the story? Can I retell the story? Does my mind feel good?

The bottom of the chart outlines what students can do if the answer to any of these questions is no: Slow down, re-read, sound it out, and read on.

I have students practice monitoring with their independent reading books and a pile of sticky notes. If something doesn’t make sense, and they’ve tried re-reading, they jot a note on a sticky and later discuss what was confusing with their partners or me. I’ve found that through conferring with students about their independent reading, and giving them support and feedback during small group sessions, I’m able to guide them to develop their monitoring skills more fully.

Monitoring comprehension can be a complicated skill for some students—it requires a lot of practice, and teacher modeling is critical. But the effort does pay off.

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• Advancing Your Skills

Engaging with the Thesis Statement: Developing Metacognitive Skills

Tags: critical thinking , feedback , group work , metacognition , participation , peer teaching , reading & composition courses , writing

Categories: GSI Online Library • Teaching Effectiveness Award Essays

by Jennifer Johnson, Linguistics (Home Department: Education)

Teaching effectiveness award essay, 2013.

The course “Endangered Languages: What We Lose when a Language Dies” offers an array of interesting topics related to language, thought, and culture that keeps the discussion engaging and the ideas flowing. However, as a Reading and Composition course, the main objective is to improve students’ academic writing skills. As an instructor, I spend hours each week offering written feedback on short writing assignments and argumentative papers; however, early in the semester, I discovered that feedback often went unread or unincorporated. Or students would come to office hours, paper with unread feedback in hand, requesting to dialogue around my margin comments. This made me realize I needed to develop in-class peer review and self review activities that assist students in exploring, understanding, and contesting feedback. How do I transfer students’ passion during the in-class discussions to engaging verbally with the written dialogue I have created in the margins of their papers? How do I help students develop metacognitive skills — in other words, reflect on their reflections?

One approach to developing metacognitive skills among students was to create multi-step “follow-up” activities for every writing assignment that would engage them in peer- and self-reflections. For the initial diagnostic essay, students were asked to write a paper with the prompt, “To what extent is language endangerment comparable to species endangerment?” The first aim of the follow-up assignment I created was to help students identify and write solid thesis statements that effectively address the question at hand. The second aim was to have students revisit the initial question and revise their own writing in order to explicitly understand the strengths and weaknesses in their own statements. I created a multi-step in-class activity/handout to revisit student thesis statements. First, it is important to return to the initial assignment prompt and fully unpack the question. In the first part of the handout activity, I highlighted key words and measures in the prompt so that students are aware of what their thesis statements must address: “To what extent is language endangerment comparable to species endangerment ?” Next, students were offered three good examples of thesis statements from their classmates (with the student writers’ permission). They were asked to give feedback, just as an instructor would, to each statement. The handout asks, “For each thesis statement example, how do you envision the rest of the paper mapping out? How does each thesis statement address the ‘so what’ question? What revisions would you make to the following statements?” I let students discuss their answers with classmates in groups in order to understand various perspectives on each statement. Finally, I asked students to reread my feedback and rewrite their initial thesis statements. Students were given the opportunity to mark up their initial statements and rewrite them.

By collecting the handout for review, I was able to assess any changes or progress made from each student’s initial thesis statement to the revised statement. Peer review, peer sharing, and revisiting feedback are vital components that offer students ways to reflect on their own writing — hopefully enhancing their academic writing by engaging in dialogue-driven revision.

Metacognitive Reflection and Insight Therapy for Early Psychosis: A preliminary study of a novel integrative psychotherapy

Affiliations.

• 1 Indiana University School of Medicine, Department of Psychiatry, Indiana University Psychotic Disorders Program, 355 W. 16th St., Suite 4800, Indianapolis, IN 46202, USA; Eskenzai Health, Midtown Community Mental Health Centers, Prevention and Recovery Center for Early Psychosis, 720 Eskenazi Avenue, Indianapolis, IN 46202, USA. Electronic address: [email protected].
• 2 Indiana University School of Medicine, Department of Psychiatry, Indiana University Psychotic Disorders Program, 355 W. 16th St., Suite 4800, Indianapolis, IN 46202, USA; Eskenzai Health, Midtown Community Mental Health Centers, Prevention and Recovery Center for Early Psychosis, 720 Eskenazi Avenue, Indianapolis, IN 46202, USA.
• 3 San Francisco VA Health Care System, 4150 Clement Street, San Francisco, CA 94121, USA.
• 4 Indiana University School of Medicine, Department of Psychiatry, Indiana University Psychotic Disorders Program, 355 W. 16th St., Suite 4800, Indianapolis, IN 46202, USA; Richard L Roudebush VA Medical Center, 1481 W 10th St, Indianapolis, IN 46202, USA.
• PMID: 29108671
• DOI: 10.1016/j.schres.2017.10.041

Poor insight impedes treatment in early phase psychosis (EPP). This manuscript outlines preliminary findings of an investigation of the novel metacognitively oriented integrative psychotherapy, Metacognitive Reflection and Insight Therapy, for individuals with early phase psychosis (MERIT-EP). Twenty adults with EPP and poor insight were randomized to either six months of MERIT-EP or treatment as usual (TAU). Therapists were trained and therapy was successfully delivered under routine, outpatient conditions. Insight, assessed before and after treatment, revealed significant improvement for the MERIT-EP, but not TAU, group. These results suggest MERIT-EP is feasible to deliver, accepted by patients, and leads to clinically significant improvements in insight.

Keywords: Early phase psychosis; Insight; MERIT; Psychotherapy; Schizophrenia.

Copyright © 2017 Elsevier B.V. All rights reserved.

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• metacognitive

having to do with metacognition , high-level thinking that enables understanding: The students are disengaged from the curriculum, and they have not gained a metacognitive understanding of the material.

Origin of metacognitive

Words nearby metacognitive.

• metachronous
• metachrosis
• metacinnabar
• metacinnabarite
• metacognition
• metacomputer
• meta-dichlorobenzene

Dictionary.com Unabridged Based on the Random House Unabridged Dictionary, © Random House, Inc. 2023

How to use metacognitive in a sentence

As a coach, I’ve found that different metacognitive strategies are useful to different athletes.

“It’s a powerful thing to point on your resume, so you’re not just saying you have Microsoft Excel skills, but that you’ve also taken the time to understand and improve your metacognition,” says Akhila Satish, CEO of Meseekna.