metacognitive essay

What Is Metacognition? How Does It Help Us Think?

Metacognitive strategies like self-reflection empower students for a lifetime..

Posted October 9, 2020 | Reviewed by Abigail Fagan

Metacognition is a high order thinking skill that is emerging from the shadows of academia to take its rightful place in classrooms around the world. As online classrooms extend into homes, this is an important time for parents and teachers to understand metacognition and how metacognitive strategies affect learning. These skills enable children to become better thinkers and decision-makers.

Metacognition: The Neglected Skill Set for Empowering Students is a new research-based book by educational consultants Dr. Robin Fogarty and Brian Pete that not only gets to the heart of why metacognition is important but gives teachers and parents insightful strategies for teaching metacognition to children from kindergarten through high school. This article summarizes several concepts from their book and shares three of their thirty strategies to strengthen metacognition.

What Is Metacognition?

Metacognition is the practice of being aware of one’s own thinking. Some scholars refer to it as “thinking about thinking.” Fogarty and Pete give a great everyday example of metacognition:

Think about the last time you reached the bottom of a page and thought to yourself, “I’m not sure what I just read.” Your brain just became aware of something you did not know, so instinctively you might reread the last sentence or rescan the paragraphs of the page. Maybe you will read the page again. In whatever ways you decide to capture the missing information, this momentary awareness of knowing what you know or do not know is called metacognition.

When we notice ourselves having an inner dialogue about our thinking and it prompts us to evaluate our learning or problem-solving processes, we are experiencing metacognition at work. This skill helps us think better, make sound decisions, and solve problems more effectively. In fact, research suggests that as a young person’s metacognitive abilities increase, they achieve at higher levels.

Fogarty and Pete outline three aspects of metacognition that are vital for children to learn: planning, monitoring, and evaluation. They convincingly argue that metacognition is best when it is infused in teaching strategies rather than taught directly. The key is to encourage students to explore and question their own metacognitive strategies in ways that become spontaneous and seemingly unconscious .

Metacognitive skills provide a basis for broader, psychological self-awareness , including how children gain a deeper understanding of themselves and the world around them.

Metacognitive Strategies to Use at Home or School

Fogarty and Pete successfully demystify metacognition and provide simple ways teachers and parents can strengthen children’s abilities to use these higher-order thinking skills. Below is a summary of metacognitive strategies from the three areas of planning, monitoring, and evaluation.

1. Planning Strategies

As students learn to plan, they learn to anticipate the strengths and weaknesses of their ideas. Planning strategies used to strengthen metacognition help students scrutinize plans at a time when they can most easily be changed.

One of ten metacognitive strategies outlined in the book is called “Inking Your Thinking.” It is a simple writing log that requires students to reflect on a lesson they are about to begin. Sample starters may include: “I predict…” “A question I have is…” or “A picture I have of this is…”

Writing logs are also helpful in the middle or end of assignments. For example, “The homework problem that puzzles me is…” “The way I will solve this problem is to…” or “I’m choosing this strategy because…”

2. Monitoring Strategies

Monitoring strategies used to strengthen metacognition help students check their progress and review their thinking at various stages. Different from scrutinizing, this strategy is reflective in nature. It also allows for adjustments while the plan, activity, or assignment is in motion. Monitoring strategies encourage recovery of learning, as in the example cited above when we are reading a book and notice that we forgot what we just read. We can recover our memory by scanning or re-reading.

One of many metacognitive strategies shared by Fogarty and Pete, called the “Alarm Clock,” is used to recover or rethink an idea once the student realizes something is amiss. The idea is to develop internal signals that sound an alarm. This signal prompts the student to recover a thought, rework a math problem, or capture an idea in a chart or picture. Metacognitive reflection involves thinking about “What I did,” then reviewing the pluses and minuses of one’s action. Finally, it means asking, “What other thoughts do I have” moving forward?

Teachers can easily build monitoring strategies into student assignments. Parents can reinforce these strategies too. Remember, the idea is not to tell children what they did correctly or incorrectly. Rather, help children monitor and think about their own learning. These are formative skills that last a lifetime.

3. Evaluation Strategies

According to Fogarty and Pete, the evaluation strategies of metacognition “are much like the mirror in a powder compact. Both serve to magnify the image, allow for careful scrutiny, and provide an up-close and personal view. When one opens the compact and looks in the mirror, only a small portion of the face is reflected back, but that particular part is magnified so that every nuance, every flaw, and every bump is blatantly in view.” Having this enlarged view makes inspection much easier.

When students inspect parts of their work, they learn about the nuances of their thinking processes. They learn to refine their work. They grow in their ability to apply their learning to new situations. “Connecting Elephants” is one of many metacognitive strategies to help students self-evaluate and apply their learning.

In this exercise, the metaphor of three imaginary elephants is used. The elephants are walking together in a circle, connected by the trunk and tail of another elephant. The three elephants represent three vital questions: 1) What is the big idea? 2) How does this connect to other big ideas? 3) How can I use this big idea? Using the image of a “big idea” helps students magnify and synthesize their learning. It encourages them to think about big ways their learning can be applied to new situations.

Metacognition and Self-Reflection

Reflective thinking is at the heart of metacognition. In today’s world of constant chatter, technology and reflective thinking can be at odds. In fact, mobile devices can prevent young people from seeing what is right before their eyes.

John Dewey, a renowned psychologist and education reformer, claimed that experiences alone were not enough. What is critical is an ability to perceive and then weave meaning from the threads of our experiences.

The function of metacognition and self-reflection is to make meaning. The creation of meaning is at the heart of what it means to be human.

Everyone can help foster self-reflection in young people.

Marilyn Price-Mitchell, Ph.D., is an Institute for Social Innovation Fellow at Fielding Graduate University and author of Tomorrow’s Change Makers.

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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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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.

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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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Fostering Metacognition to Support Student Learning and Performance

• Julie Dangremond Stanton
• Amanda J. Sebesta
• John Dunlosky

*Address correspondence to: Julie Dangremond Stanton ( E-mail Address: [email protected] ).

Department of Cellular Biology, University of Georgia, Athens, GA 30602

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Department of Biology, Saint Louis University, St. Louis, MO 63103

Department of Psychological Sciences, Kent State University, Kent, OH 44240

Metacognition is awareness and control of thinking for learning. Strong metacognitive skills have the power to impact student learning and performance. While metacognition can develop over time with practice, many students struggle to meaningfully engage in metacognitive processes. In an evidence-based teaching guide associated with this paper ( https://lse.ascb.org/evidence-based-teaching-guides/student-metacognition ), we outline the reasons metacognition is critical for learning and summarize relevant research on this topic. We focus on three main areas in which faculty can foster students’ metacognition: supporting student learning strategies (i.e., study skills), encouraging monitoring and control of learning, and promoting social metacognition during group work. We distill insights from key papers into general recommendations for instruction, as well as a special list of four recommendations that instructors can implement in any course. We encourage both instructors and researchers to target metacognition to help students improve their learning and performance.

INTRODUCTION

Supporting the development of metacognition is a powerful way to promote student success in college. Students with strong metacognitive skills are positioned to learn more and perform better than peers who are still developing their metacognition (e.g., Wang et al. , 1990 ). Students with well-developed metacognition can identify concepts they do not understand and select appropriate strategies for learning those concepts. They know how to implement strategies they have selected and carry out their overall study plans. They can evaluate their strategies and adjust their plans based on outcomes. Metacognition allows students to be more expert-like in their thinking and more effective and efficient in their learning. While collaborating in small groups, students can also stimulate metacognition in one another, leading to improved outcomes. Ever since metacognition was first described ( Flavell, 1979 ), enthusiasm for its potential impact on student learning has remained high. In fact, as of today, the most highly cited paper in CBE—Life Sciences Education is an essay on “Promoting Student Metacognition” ( Tanner, 2012 ).

Despite this enthusiasm, instructors face several challenges when attempting to harness metacognition to improve their students’ learning and performance. First, metacognition is a term that has been used so broadly that its meaning may not be clear ( Veenman et al. , 2006 ). We define metacognition as awareness and control of thinking for learning ( Cross and Paris, 1988 ). Metacognition includes metacognitive knowledge , which is your awareness of your own thinking and approaches for learning. Metacognition also includes metacognitive regulation , which is how you control your thinking for learning ( Figure 1 ). Second, metacognition includes multiple processes and skills that are named and emphasized differently in the literature from various disciplines. Yet upon examination, the metacognitive processes and skills from different fields are closely related, and they often overlap (see Supplemental Figure 1). Third, metacognition consists of a person’s thoughts, which may be challenging for that person to describe. The tacit nature of metacognitive processes makes it difficult for instructors to observe metacognition in their students, and it also makes metacognition difficult for researchers to measure. As a result, classroom intervention studies of metacognition—those that are necessary for making the most confident recommendations for promoting student metacognition—have lagged behind foundational and laboratory research on metacognitive processes and skills.

FIGURE 1. Metacognition framework commonly used in biology education research (modified from Schraw and Moshman, 1995 ). This theoretical framework divides metacognition into two components: metacognitive knowledge and metacognitive regulation. Metacognitive knowledge includes what you know about your own thinking and what you know about strategies for learning. Declarative knowledge involves knowing about yourself as a learner, the demands of the task, and what learning strategies exist. Procedural knowledge involves knowing how to use learning strategies. Conditional knowledge involves knowing when and why to use particular learning strategies. Metacognitive regulation involves the actions you take in order to learn. Planning involves deciding what strategies to use for a future learning task and when you will use them. Monitoring involves assessing your understanding of concepts and the effectiveness of your strategies while learning. Evaluating involves appraising your prior plan and adjusting it for future learning.

How do undergraduate students develop metacognitive skills?

To what extent do active learning and generative work 1 promote metacognition?

To what extent do increases in metacognition correspond to increases in achievement in science courses?

FIGURE 2. (A) Landing page for the Student Metacognition guide. The landing page provides a map with sections an instructor can click on to learn more about how to support students’ metacognition. (B) Example paper summary showing instructor recommendations. At the end of each summary in our guide, we used italicized text to point out what instructors should know based on the paper’s results.

The organization of this essay reflects the organization of our evidence-based teaching guide. In the guide, we first define terms and provide important background from papers that highlight the underpinnings and benefits of metacognition ( https://lse.ascb.org/evidence-based-teaching-guides/student-metacognition/benefits-definitions-underpinnings ). We then explore metacognition research by summarizing both classic and recent papers in the field and providing links for readers who want to examine the original studies. We consider three main areas related to metacognition: 1) student strategies for learning, 2) monitoring and control of learning, and 3) social metacognition during group work.

SUPPORTING STUDENTS TO USE EFFECTIVE LEARNING STRATEGIES

What strategies do students use for learning.

First our teaching guide examines metacognition in the context of independent study ( https://lse.ascb.org/evidence-based-teaching-guides/student-metacognition/supporting-student
-learning-strategies ). When students transition to college, they have increased responsibility for directing their learning, which includes making important decisions about how and when to study. Students rely on their metacognition to make those decisions, and they also use metacognitive processes and skills while studying on their own. Empirical work has confirmed what instructors observe about their own students’ studying—many students rely on passive strategies for learning. Students focus on reviewing material as it is written or presented, as opposed to connecting concepts and synthesizing information to make meaning. Some students use approaches that engage their metacognition, but they often do so without a full understanding of the benefits of these approaches ( Karpicke et al. , 2009 ). Students also tend to study based on exam dates and deadlines, rather than planning out when to study ( Hartwig and Dunlosky, 2012 ). As a result, they tend to cram, which is also known in the literature as massing their study. Students continue to cram because this approach is often effective for boosting short-term performance, although it does not promote long-term retention of information.

Which Strategies Should Students Use for Learning?

Here, we make recommendations about what students should do to learn, as opposed to what they typically do. In our teaching guide, we highlight three of the most effective strategies for learning: 1) self-testing, 2) spacing, and 3) interleaving ( https://lse.ascb.org/evidence-based-teaching-guides/student
-metacognition/supporting-student-learning-strategies/
#whatstudentsshould ). These strategies are not yet part of many students’ metacognitive knowledge, but they should know about them and be encouraged to use them while metacognitively regulating their learning. Students self-test when they use flash cards and answer practice questions in an attempt to recall information. Self-testing provides students with opportunities to monitor their understanding of material and identify gaps in their understanding. Self-testing also allows students to activate relevant knowledge and encode prompted information so it can be more easily accessed from their memory in the future ( Dunlosky et al. , 2013 ).

Students space their studying when they spread their learning of the same material over multiple sessions. This approach requires students to intentionally plan their learning instead of focusing only on what is “due” next. Spacing can be combined with retrieval practice , which involves recalling information from memory. For example, self-testing is a form of retrieval practice. Retrieval practice with spacing encourages students to actively recall the same content across several study sessions, which is essential for consolidating information from prior study periods ( Dunlosky et al. , 2013 ). Importantly, when students spread their learning over multiple sessions, they are less susceptible to superficial familiarity with concepts, which can mislead them into thinking they have learned concepts based on recognition alone ( Kornell and Bjork, 2008 ).

Students interleave when they alternate studying of information from one category with studying of information from another category. For example, when students learn categories of amino acid side groups, they should alternate studying nonpolar amino acids with polar amino acids. This allows students to discriminate across categories, which is often critical for correctly solving problems ( Rohrer et al. , 2020 ). Interleaving between categories also supports student learning because it usually results in spacing of study.

How are students enacting specific learning strategies, and do different students enact them in different ways?

To what extent do self-testing, spacing, and interleaving support achievement in the context of undergraduate science courses?

What can instructors do to increase students’ use of effective learning strategies?

What Factors Affect the Strategies Students Should Use to Learn?

Next, we examined the factors that affect what students should do to learn. Although we recommend three well-established strategies for learning, other appropriate strategies can vary based on the learning context. For example, the nature of the material, the type of assessment, the learning objectives, and the instructional methods can render some strategies more effective than others ( Scouller, 1998 ; Sebesta and Bray Speth, 2017 ). Strategies for learning can be characterized as deep if they involve extending and connecting ideas or applying knowledge and skills in new ways ( Baeten et al. , 2010 ). Strategies can be characterized as surface if they involve recalling and reproducing content. While surface strategies are often viewed negatively, there are times when these approaches can be effective for learning ( Hattie and Donoghue, 2016 ). For example, when students have not yet gained background knowledge in an area, they can use surface strategies to acquire the necessary background knowledge. They can then incorporate deep strategies to extend, connect, and apply this knowledge. Importantly, surface and deep strategies can be used simultaneously for effective learning. The use of surface and deep strategies ultimately depends on what students are expected to know and be able to do, and these expectations are set by instructors. Openly discussing these expectations with students can enable them to more readily select effective strategies for learning.

What Challenges Do Students Face in Using Their Metacognition to Enact Effective Strategies?

How can students address challenges they will face when using effective—but effortful—strategies for learning?

What approaches can instructors take to help students overcome these challenges?

ENCOURAGING STUDENTS TO MONITOR AND CONTROL THEIR LEARNING FOR EXAMS

Metacognition can be investigated in the context of any learning task, but in the sciences, metacognitive processes and skills are most often investigated in the context of high-stakes exams. Because exams are a form of assessment common to nearly every science course, in the next part of our teaching guide, we summarized some of the vast research focused on monitoring and control before, during, and after an exam ( https://lse.ascb.org/evidence-based-teaching-guides/student-metacognition/encouraging-students-monitor-control-learning ). In the following section, we demonstrate the kinds of monitoring and control decisions learners make by using an example of introductory biology students studying for an exam on cell division. The students’ instructor has explained that the exam will focus on the stages of mitosis and cytokinesis, and the exam will include both multiple-choice and short-answer questions.

How Should Students Use Metacognition while Preparing for and Taking an Exam?

As students prepare for an exam, they can use metacognition to inform their learning. Students can consider how they will be tested, set goals for their learning, and make a plan to meet their goals. It is expected that students who set specific goals while planning for an exam will be more effective in their studying than students who do not make specific goals. For example, a student who sets a specific goal to identify areas of confusion each week by answering end-of-chapter questions each weekend is expected to do better than a student who sets a more general goal of staying up-to-date on the material. Although some studies include goal setting and planning as one of many metacognitive strategies introduced to students, the influence of task-specific goal setting on academic achievement has not been well studied on its own in the context of science courses.

As students study, it is critical that they monitor both their use of learning strategies and their understanding of concepts. Yet many students struggle to accurately monitor their own understanding ( de Carvalho Filho, 2009 ). In the example we are considering, students may believe they have already learned mitosis because they recognize the terms “prophase,” “metaphase,” “anaphase,” and “telophase” from high school biology. When students read about mitosis in the textbook, processes involving the mitotic spindle may seem familiar because of their exposure to these concepts in class. As a result, students may inaccurately predict that they will perform well on exam questions focused on the mitotic spindle, and their overconfidence may cause them to stop studying the mitotic spindle and related processes ( Thiede et al. , 2003 ). Students often rate their confidence in their learning based on their ability to recognize, rather than recall, concepts.

Instead of focusing on familiarity, students should rate their confidence based on how well they can retrieve relevant information to correctly answer questions. Opportunities for practicing retrieval, such as self-testing, can improve monitoring accuracy. Instructors can help students monitor their understanding more accurately by encouraging students to complete practice exams and giving students feedback on their answers, perhaps in the form of a key or a class discussion ( Rawson and Dunlosky, 2007 ). Returning to the example, if students find they can easily recall the information needed to correctly answer questions about cytokinesis, they may wisely decide to spend their study time on other concepts. In contrast, if students struggle to remember information needed to answer questions about the mitotic spindle, and they answer these questions incorrectly, then they can use this feedback to direct their efforts toward mastering the structure and function of the mitotic spindle.

While taking a high-stakes exam, students can again monitor their performance on a single question, a set of questions, or an entire exam. Their monitoring informs whether they change an answer, with students tending to change answers they judge as incorrect. Accordingly, the accuracy of their monitoring will influence whether their changes result in increased performance ( Koriat and Goldsmith, 1996 ). In some studies, changing answers on an exam has been shown to increase student performance, in contrast to the common belief that a student’s first answer is usually right ( Stylianou-Georgiou and Papanastasiou, 2017 ). Changing answers on an exam can be beneficial if students return to questions they had low confidence in answering and make a judgment on their answers based on the ability to retrieve the information from memory, rather than a sense of familiarity with the concepts. Two important open questions are:

What techniques can students use to improve the accuracy of their monitoring, while preparing for an exam and while taking an exam?

How often do students monitor their understanding when studying on their own?

How Should Students Use Metacognition after Taking an Exam?

How do students develop metacognitive regulation skills such as evaluation?

To what extent does the ability to evaluate affect student learning and performance?

When students evaluate the outcome of their studying and believe their preparation was lacking, to what degree do they adopt more effective strategies for the next exam?

PROMOTING SOCIAL METACOGNITION DURING GROUP WORK

Next, our teaching guide covers a relatively new area of inquiry in the field of metacognition called social metacognition , which is also known as socially shared metacognition ( https://lse.ascb.org/evidence-based-teaching-guides/student
-metacognition/promoting-social-metacognition
-group-work ). Science students are expected to learn not only on their own, but also in the context of small groups. Understanding social metacognition is important because it can support effective student learning during collaborations both inside and outside the classroom. While individual metacognition involves awareness and control of one’s own thinking, social metacognition involves awareness and control of others’ thinking. For example, social metacognition happens when students share ideas with peers, invite peers to evaluate their ideas, and evaluate ideas shared by peers ( Goos et al. , 2002 ). Students also use social metacognition when they assess, modify, and enact one another’s strategies for solving problems ( Van De Bogart et al. , 2017 ). While enacting problem-solving strategies, students can evaluate their peers’ hypotheses, predictions, explanations, and interpretations. Importantly, metacognition and social metacognition are expected to positively affect one another ( Chiu and Kuo, 2009 ).

How do social metacognition and individual metacognition affect one another?

How can science instructors help students to effectively use social metacognition during group work?

CONCLUSIONS

We encourage instructors to support students’ success by helping them develop their metacognition. Our teaching guide ends with an Instructor Checklist of actions instructors can take to include opportunities for metacognitive practice in their courses ( https://lse.ascb.org/wp-content/uploads/sites/10/2020/12/Student-Metacognition-Instructor-Checklist.pdf ). We also provide a list of the most promising approaches instructors can take, called Four Strategies to Implement in Any Course ( https://lse.ascb.org/wp-content/uploads/sites/10/2020/12/Four
-Strategies-to-Foster-Student-Metacognition.pdf ). We not only encourage instructors to consider using these strategies, but given that more evidence for their efficacy is needed from classroom investigations, we also encourage instructors to evaluate and report how well these strategies are improving their students’ achievement. By exploring and supporting students’ metacognitive development, we can help them learn more and perform better in our courses, which will enable them to develop into lifelong learners.

1 Generative work “involves students working individually or collaboratively to generate ideas and products that go beyond what has been presented to them” ( Andrews et al. , 2019 , p2). Generative work is often stimulated by active-learning approaches.

ACKNOWLEDGMENTS

We are grateful to Cynthia Brame, Kristy Wilson, and Adele Wolfson for their insightful feedback on this paper and the guide. This material is based upon work supported in part by the National Science Foundation under grant number 1942318 (to J.D.S.). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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Metacognition: Nurturing Self-Awareness in the Classroom

When students practice metacognition, the act of thinking about their thinking helps them make greater sense of their life experiences and start achieving at higher levels.

How do children gain a deeper understanding of how they think, feel, and act so that they can improve their learning and develop meaningful relationships? Since antiquity, philosophers have been intrigued with how human beings develop self-awareness -- the ability to examine and understand who we are relative to the world around us. Today, research not only shows that self-awareness evolves during childhood, but also that its development is linked to metacognitive processes of the brain.

Making Sense of Life Experiences

Most teachers know that if students reflect on how they learn, they become better learners. For example, some students may think and process information best in a quiet library, while others may focus better surrounded by familiar noise or music. Learning strategies that work for math may be different from those applied in the study of a foreign language. For some, it takes more time to understand biology than chemistry. With greater awareness of how they acquire knowledge, students learn to regulate their behavior to optimize learning. They begin to see how their strengths and weaknesses affect how they perform. The ability to think about one's thinking is what neuroscientists call metacognition. As students' metacognitive abilities increase, research suggests they also achieve at higher levels .

Metacognition plays an important role in all learning and life experiences. Beyond academic learning, when students gain awareness of their own mental states, they begin to answer important questions:

• How do I live a happy life?
• How do I become a respected human being?
• How do I feel good about myself?

Through these reflections, they also begin to understand other people's perspectives.

At a recent international workshop , philosophers and neuroscientists gathered to discuss self-awareness and how it is linked to metacognition. Scientists believe that self-awareness, associated with the paralimbic network of the brain, serves as a "tool for monitoring and controlling our behavior and adjusting our beliefs of the world, not only within ourselves, but, importantly, between individuals." This higher-order thinking strategy actually changes the structure of the brain, making it more flexible and open to even greater learning.

Self-awareness is part of The Compass Advantage ™ (a model designed for engaging families, schools, and communities in the principles of positive youth development) because it plays a critical role in how students make sense of life experiences. Linked by research to each of the other Compass abilities, particularly empathy, curiosity, and sociability, self-awareness is one of the 8 Pathways to Every Student's Success .

Self-awareness plays a critical role in improved learning because it helps students become more efficient at focusing on what they still need to learn. The ability to think about one's thinking increases with age. Research shows that most growth of metacognitive ability happens between ages 12 and 15 (PDF, 199KB). When teachers cultivate students' abilities to reflect on, monitor, and evaluate their learning strategies, young people become more self-reliant, flexible, and productive. Students improve their capacity to weigh choices and evaluate options, particularly when answers are not obvious. When students have difficulty understanding, they rely on reflective strategies to recognize their difficulties and attempt to rectify them. Improving metacognitive strategies related to students' schoolwork also provides young people with tools to reflect and grow in their emotional and social lives.

7 Strategies That Improve Metacognition

1. teach students how their brains are wired for growth..

The beliefs that students adopt about learning and their own brains will affect their performance. Research shows that when students develop a growth mindset vs. a fixed mindset, they are more likely to engage in reflective thinking about how they learn and grow. Teaching kids about the science of metacognition can be an empowering tool, helping students to understand how they can literally grow their own brains.

2. Give students practice recognizing what they don't understand.

The act of being confused and identifying one's lack of understanding is an important part of developing self-awareness. Take time at the end of a challenging class to ask, "What was most confusing about the material we explored today?" This not only jumpstarts metacognitive processing, but also creates a classroom culture that acknowledges confusion as an integral part of learning.

3. Provide opportunities to reflect on coursework.

Higher-order thinking skills are fostered as students learn to recognize their own cognitive growth. Questions that help this process might include:

• Before this course, I thought earthquakes were caused by _______. Now I understand them to be the result of _______.
• How has my thinking about greenhouse gases changed since taking this course?

4. Have students keep learning journals.

One way to help students monitor their own thinking is through the use of personal learning journals. Assign weekly questions that help students reflect on how rather than what they learned. Questions might include:

• What was easiest for me to learn this week? Why?
• What was most challenging for me to learn? Why?
• What study strategies worked well as I prepared for my exam?
• What strategies for exam preparation didn't work well? What will I do differently next time?
• What study habits worked best for me? How?
• What study habit will I try or improve upon next week?

Encourage creative expression through whatever journal formats work best for learners, including mind maps, blogs, wikis, diaries, lists, e-tools, etc.

5. Use a "wrapper" to increase students' monitoring skills.

A "wrapper" is a short intervention that surrounds an existing activity and integrates a metacognitive practice. Before a lecture, for example, give a few tips about active listening. Following the lecture, ask students to write down three key ideas from the lecture. Afterward, share what you believe to be the three key ideas and ask students to self-check how closely theirs matched your intended goals. When used often, this activity not only increases learning, but also improves metacognitive monitoring skills.

6. Consider essay vs. multiple-choice exams.

Research shows that students use lower-level thinking skills to prepare for multiple-choice exams , and higher-level metacognitive skills to prepare for essay exams. While it is less time consuming to grade multiple-choice questions, even the addition of several short essay questions can improve the way students reflect on their learning to prepare for test taking.

7. Facilitate reflexive thinking.

Reflexivity is the metacognitive process of becoming aware of our biases -- prejudices that get in the way of healthy development. Teachers can create a classroom culture for deeper learning and reflexivity by encouraging dialogue that challenges human and societal biases. When students engage in conversations or write essays on biases and moral dilemmas related to politics, wealth, racism, poverty, justice, liberty, etc., they learn to "think about their own thinking." They begin to challenge their own biases and become more flexible and adaptive thinkers.

What other ways do you help students reflect on their thinking in your classroom?

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What are metacognitive skills? Examples in everyday life

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What are metacognitive skills?

Examples of metacognitive skills, how to improve metacognitive skills, take charge of your mind.

When facing a career change or deciding to switch jobs, you might update the hard and soft skills on your resume. You could even take courses to upskill and expand your portfolio.

But some growth happens off the page. Your metacognitive skills contribute to your learning process and help you look inward to self-reflect and monitor your growth. They’re like a golden ticket toward excellence in both academia and your career path , always pushing you further.

A deeper understanding of metacognition, along with effective strategies for developing related skills, opens the door to heightened personal and professional development . Metacognitive thinking might just be the tool you need to reach your academic and career goals

Metacognitive skills are the soft skills you use to monitor and control your learning and problem-solving processes , or your thinking about thinking. This self-understanding is known as metacognition theory, a term that the American developmental psychologist John H. Flavell coined in the 1970s .

It might sound abstract, but these skills are mostly about self-awareness , learning, and organizing your thoughts. Metacognitive strategies include thinking out loud and answering reflective questions. They’re often relevant for students who need to memorize concepts fast or absorb lots of information at once.

But metacognition is important for everyone because it helps you retain information more efficiently and feel more confident about what you know. One meta-analysis of many studies showed that being aware of metacognitive strategies has a strong positive impact on teaching and learning , and that knowing how to plan ahead was a key indicator of future success.

Understanding your cognition and how you learn is a fundamental step in optimizing your educational process. To make the concept more tangible, here are a few cognitive skills examples:

Goal setting

One of the foremost metacognitive skills is knowing how to set goals — recognizing what your ambitions are and fine-tuning them into manageable and attainable objectives. The SMART goal framework is a good place to start because it dives deeper into what you know you can realistically achieve.

Whether it's a personal goal of grasping a complex concept, a professional goal of developing a new skill set, or a financial goal of achieving a budgeting milestone , setting a concrete goal helps you know what you’re working toward. It’s the first step to self-directed learning and achievement, giving you a destination for your path.

Planning and organization

Planning is an essential metacognition example because it sketches out the route you'll take to reach your goal, as well as identifying and collecting the specific strategies, resources, and support mechanisms you'll need along the way. It’s an in-demand skill for many jobs, but it also helps you learn new things.

Creating and organizing a plan is where you contemplate the best methods for learning, evaluate the materials and resources at your disposal, and determine the most efficient time management strategies. Even though it’s a concrete skill, it falls under the umbrella of metacognition because it involves self-awareness about your learning style and abilities.

Problem-solving

Central to metacognition is problem-solving, a higher-order cognitive process requiring both creative and critical thinking skills . Solving problems both at work and during learning begins with recognizing the issue at hand, analyzing the details, and considering potential solutions. The next step is selecting the most promising solution from the pool of possibilities and evaluating the results after implementation. 

The problem-solving process gives you the opportunity to grow from your mistakes and practice trial and error. It also helps you reflect and refine your approach for future endeavors. These qualities make it central to metacognition’s inward-facing yet action-oriented processes.

Concentration

Concentration allows you to fully engage with the information you’re processing and retain new knowledge. It involves a high degree of mental fitness , which you can develop with metacognition. Most tasks require the ability to ignore distractions , resist procrastination , and maintain a steady focus on the task at hand. 

This skill is paramount when it comes to work-from-home settings or jobs with lots of moving parts where countless distractions are constantly vying for your attention. And training your mind to focus better in general can also increase your learning efficacy and overall productivity.

Self-reflection

The practice of self-reflection involves continually assessing your performance, cognitive strategies, and experiences to foster self-improvement . It's a type of mental debriefing where you look back on your actions and outcomes, examining them critically to gain insight and experience valuable lessons. 

Reflective practice can help you identify what worked well, what didn't, and why, giving you the opportunity to make necessary adjustments for future actions. This continuous process enhances your learning and helps you adapt to new changes and strategies. 

Metacognition turns you into a self-aware problem solver, empowering you to take control of your education and become a more efficient thinker. Although it’s helpful for students, you can also apply it in the workplace while brainstorming and discovering new ways to fulfill your roles and responsibilities .

Here are some examples of metacognitive strategies and how to cultivate your abilities:

1. Determine your learning style

Are you a visual learner who thrives on images, diagrams, and color-coded notes? Are you an auditory learner who benefits more from verbal instructions, podcasts , or group discussions? Or are you a kinesthetic learner who enjoys hands-on experiences, experiments, or physical activities?

Metacognition in education is critical because it teaches you to recognize the way you intake information — the first step to effective strategies that help you truly retain information. By identifying your learning style, you can tailor your goals and study strategies to suit your strengths, maximizing your cognitive potential and improving your understanding of new material.

2. Find deeper meaning in what you read

Merely skimming the surface of the text you read won't lead to profound understanding or long-term retention. Instead, dive deep into the material. Employ reading strategies like note-taking, highlighting, and summarizing to help information enter your brain. 

If that process doesn’t work for you, try using brainstorming techniques like mind mapping to tease out the underlying themes and messages. This depth of processing enhances comprehension and allows you to connect new information to prior knowledge, promoting meaningful learning.

3. Write organized plans

Deconstruct your tasks into manageable units and create a comprehensive, step-by-step plan. Having a detailed guide breaks down large, intimidating tasks into bite-sized, achievable parts, reduces the risk of procrastination, and helps manage cognitive load. This process frees up your mental energy for higher-order thinking.

4. Ask yourself open-ended questions

Metacognitive questioning is a powerful tool for fostering self-awareness. Asking good questions like “What am I trying to achieve?” and “Why did this approach work or not work?” facilitates a deeper understanding of your education style, promotes critical thinking, and enables self-directed learning. Your answers will pave the way for improved processes.

5. Ask for feedback

External perspectives offer valuable insights into your thinking patterns and strategies. Seek feedback from teachers, peers, or mentors and earn the metacognitive knowledge you need to identify strengths to harness and weaknesses to address. Remember, the objective isn’t to nitpick or micromanage. It’s constructive criticism to help refine your learning process.

6. Self-evaluate

Cultivate a habit of self-assessment and self-monitoring, whether you’re experiencing something new or working on an innovative project. Check in on progress regularly, and compare current performance with your goals. This continuous self-evaluation helps you maintain focus on your objectives and identify when you're going off track, allowing for timely adjustments when necessary. 

Introspection is a powerful tool, and you can’t overstate the importance of knowing yourself . After all, building your metacognitive skills begins with a strong foundation of self-awareness and accountability .

7. Focus on solutions

It's easy to let problems and obstacles discourage you during the learning process. But metacognitive skills encourage a solutions-oriented mindset. Instead of fixating on the challenges, shift your focus to identifying, analyzing, and implementing creative solutions . 

This proactive approach fosters resilience and adaptability skills in the face of adversity, helping you overcome whatever comes your way. Cultivating this mindset — sometimes known as a growth mindset — also boosts your problem-solving prowess and transforms challenges into opportunities for growth.

The simple act of writing about your learning experiences can heighten your metacognitive awareness. Journaling provides a space to reflect on your thought processes, emotions, and struggles, which can reveal patterns and trends in your behavior. It’s a springboard for improvement that helps you recognize and solve problems as they come.

In the journey of learning and career advancement, metacognitive skills are your compass toward improvement. They empower you to understand your cognitive processes, enhance your strategies, and become a more effective thinker. They’re useful whether you’re just starting a master’s degree or upskilling to earn a promotion.

Remember, the journey to gain metacognitive skills isn’t a race. It’s a personal voyage of self-discovery and growth. Each stride you take toward honing your metacognitive skills is a step toward a more successful, fulfilling, and self-aware life.

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What’s convergent thinking? How to be a better problem-solver

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Metacognitive Study Strategies

Do you spend a lot of time studying but feel like your hard work doesn’t help your performance on exams? You may not realize that your study techniques, which may have worked in high school, don’t necessarily translate to how you’re expected to learn in college. But don’t worry—we’ll show you how to analyze your current strategies, see what’s working and what isn’t, and come up with new, more effective study techniques. To do this, we’ll introduce you to the idea of “metacognition,” tell you why metacognition helps you learn better, and introduce some strategies for incorporating metacognition into your studying.

What is metacognition and why should I care?

Metacognition is thinking about how you think and learn. The key to metacognition is asking yourself self-reflective questions, which are powerful because they allow us to take inventory of where we currently are (thinking about what we already know), how we learn (what is working and what is not), and where we want to be (accurately gauging if we’ve mastered the material). Metacognition helps you to be a self-aware problem solver and take control of your learning.

By using metacognition when you study, you can be strategic about your approach. You will be able to take stock of what you already know, what you need to work on, and how best to approach learning new material.

Strategies for Using Metacognition When You Study

Below are some ideas for how to engage in metacognition when you are studying. Think about which of these resonate with you and plan to incorporate them into your study routine on a regular basis.

Use Your Syllabus as a Roadmap

Look at your syllabus. Your professor probably included a course schedule, reading list, learning objectives or something similar to give you a sense of how the course is structured. Use this as your roadmap for the course. For example, for a reading-based course, think about why your professor might have assigned the readings in this particular order. How do they connect? What are the key themes that you notice? What prior knowledge do you have that could inform your reading of this new material? You can do this at multiple points throughout the semester, as you gain additional knowledge that you can piece together.

Summon Your Prior Knowledge

Before you read your textbook or attend a lecture, look at the topic that is covered and ask yourself what you know about it already. What questions do you have? What do you hope to learn? Answering these questions will give context to what you are learning and help you start building a framework for new knowledge. It may also help you engage more deeply with the material.

Think Aloud

Talk through your material. You can talk to your classmates, your friends, a tutor, or even a pet. Just verbalizing your thoughts can help you make more sense of the material and internalize it more deeply. Talking aloud is a great way to test yourself on how well you really know the material. In courses that require problem solving, explaining the steps aloud will ensure you really understand them and expose any gaps in knowledge that you might have. Ask yourself questions about what you are doing and why.

Ask Yourself Questions

Asking self-reflective questions is key to metacognition. Take the time to be introspective and honest with yourself about your comprehension. Below are some suggestions for metacognitive questions you can ask yourself.

• Does this answer make sense given the information provided?
• What strategy did I use to solve this problem that was helpful?
• How does this information conflict with my prior understanding?
• How does this information relate to what we learned last week?
• What questions will I ask myself next time I’m working these types of problems?
• What is confusing about this topic?
• What are the relationships between these two concepts?
• What conclusions can I make?

Try brainstorming some of your own questions as well.

Use Writing

Writing can help you organize your thoughts and assess what you know. Just like thinking aloud, writing can help you identify what you do and don’t know, and how you are thinking about the concepts that you’re learning. Write out what you know and what questions you have about the learning objectives for each topic you are learning.

Organize Your Thoughts

Using concept maps or graphic organizers is another great way to visualize material and see the connections between the various concepts you are learning. Creating your concept map from memory is also a great study strategy because it is a form of self-testing.

Take Notes from Memory

Many students take notes as they are reading. Often this can turn notetaking into a passive activity, since it can be easy to fall into just copying directly from the book without thinking about the material and putting your notes in your own words. Instead, try reading short sections at a time and pausing periodically to summarize what you read from memory. This technique ensures that you are actively engaging with the material as you are reading and taking notes, and it helps you better gauge how much you’re actually remembering from what you read; it also engages your recall, which makes it more likely you’ll be able to remember and understand the material when you’re done.

Review Your Exams

Reviewing an exam that you’ve recently taken is a great time to use metacognition. Look at what you knew and what you missed. Try using this guide to  analyze your preparation for the exam and track the items you missed , along with the reasons that you missed them. Then take the time to fill in the areas you still have gaps and make a plan for how you might change your preparation next time.

Take a Timeout

When you’re learning, it’s important to periodically take a time out to make sure you’re engaging in metacognitive strategies. We often can get so absorbed in “doing” that we don’t always think about the why behind what we are doing. For example, if you are working through a math problem, it’s helpful to pause as you go and think about why you are doing each step, and how you knew that it followed from the previous step. Throughout the semester, you should continue to take timeouts before, during or after assignments to see how what you’re doing relates to the course as a whole and to the learning objectives that your professor has set.

Test Yourself

You don’t want your exam to be the first time you accurately assess how well you know the material. Self-testing should be an integral part of your study sessions so that have a clear understanding of what you do and don’t know. Many of the methods described are about self-testing (e.g., thinking aloud, using writing, taking notes from memory) because they help you discern what you do and don’t actually know. Other common methods include practice tests and flash cards—anything that asks you to summon your knowledge and check if it’s correct.

Figure Out How You Learn

It is important to figure out what learning strategies work best for you. It will probably vary depending on what type of material you are trying to learn (e.g. chemistry vs. history), but it will be helpful to be open to trying new things and paying attention to what is effective for you. If flash cards never help you, stop using them and try something else instead.

Works Consulted

McGuire, S.Y. & McGuire, S. (2016). Teach Students How to Learn: Strategies You Can Incorporate in Any Course to Improve Student Metacognition, Study Skills, and Motivation. Stylus Publishing, LLC.

Ten Metacognitive Teaching Strategies. Vancouver Island University. Centre for Innovation and Excellence in Learning.

Anderson, J. (2017, May 09). A Stanford researcher’s 15-minute study hack lifts B+ students into the As. Quartz.

Developed and shared by The Learning Center , University of North Carolina at Chapel Hill.

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Reflection is an act of looking back in order to process experiences. Metacognition, a type of reflection, is a way of thinking about one’s thinking in order to grow. Metacognition and reflection are terms often used interchangeably, but it is most helpful to distinguish metacognition as a particular form of reflection. Often instructors and students think about reflection as one specific genre that never changes—a letter or a note to an authority figure about what was done effectively and what could be improved. But this doesn’t have to be the case. At its best, reflection is not a static form. It can work in many dynamic ways: talking, blogging/vlogging, writing letters, formal essays, etc. Teaching your students to practice reflection in a variety of ways can facilitate more effective and fulfilling metacognition.

General Considerations

Coursework can train students to think not only about the subject matter of the field, but also about how they acquire knowledge in relation to society or a specific social context [i.e. a particular scholarly or practical community]. Students who learn to think about how their academic environments affect their learning strategies are more likely to develop the ability to transfer knowledge among varying contexts. Here are some rules of thumb that make for effective reflection:

1. Students need explicit training to practice reflection and metacognition. Experience shows that the best way to develop students’ metacognitive abilities is to teach metacognitive strategies hand-in-hand with the course content. The most effective metacognitive training happens when you talk explicitly with your students about why metacognitive practice is useful. You should also provide specific, guided prompts that consistently direct students’ thinking throughout the course. For example:

• Have students work in groups to parse out the prompt for a new paper, asking each group to complete a portion of the “Pre-Write” exercise (see “Planning Exercises: Strategy 2” below) and then report back to the class. This allows the class to work collectively to imagine strategies that students can then tailor to their individual needs. They can learn from their peers’ ideas as well as from your feedback in the class discussion.
• Contribute to a class blog as they work. For instance, ask students to post calls for help when they hit an obstacle or become frustrated in some way. Likewise, you might ask them to post when they experience a moment of triumph in their work—perhaps when they found the perfect source after a long search, or when they had an epiphany about a great way to start an essay. These kinds of in-the-moment posts (which can be very brief) help students not only to observe their own process but also to make use of their classmates’ potential advice and encouragement. Such contributions build a record of reflection that students can return to later for self-evaluation.

2. The best reflective assignments respond to an authentic problem or a disagreement that needs to be resolved. Otherwise, reflective assignments can be counterproductive by allowing students not to think but to get by operating on autopilot. For example, many students have come to expect being asked to write self-assessment letters to professors making a case for what grade they think a particular project should receive and why. Thus, many students automatically write about the effort they spent (often in terms of hours or days, rather than anything concrete to actually illustrate effort). They also may acknowledge that they have “some trouble with grammar and punctuation” and then suggest an ‘A’ or a ‘B’ and call it a day. In a response like that, virtually no self-assessment is actually happening, because students assume they already know the genre and its markers, and they fill in what they perceive to be the expected answers. Often instructors assign grades that disregard the self-assessments or creatively “average” their own assessments with the students’, thereby proving the exercise fairly useless for both parties. A small adjustment, such as asking students to create a Post-Write (see “Evaluation Exercises: Strategy 3” below) after they receive a grade from you, will help students understand how each element of their process affected the product. You also might consider allowing the Post-Write to serve as an argument for a separate grade or as a blueprint for a revision that could lead to a re-grade.

3. The best reflection is often social/collaborative. Social activity is an essential part of reflective practice; by reflecting together occasionally, students can begin to understand their own learning in relationship to other people’s learning styles and experiences.

4. Reflection in the midst of a process can be as helpful as reflection after the fact. Reflection can be powerful in a moment of problem solving (reflection in-action) or after problem solving (reflection-on-action). Reflection-in-action, however, allows learners to disrupt bad habits and shift gears as they recognize unproductive strategies. It’s most useful to establish a reflective practice of setting goals beforehand, monitoring progress as students work, and evaluating the outcome compared to original goals after the fact.

5. Reflection should be consistent and responsive. It’s most effective when it happens often (in a variety of ways), and when you respond promptly (whether in writing, in class, or in some other way). Encouraging a variety of formal and informal reflections allows students to receive different kinds of feedback at multiple stages without overburdening your own preparation time. For instance, across the course of a semester, you might assign:

• Semi-regular informal in-class reflections that you read and respond to in the next class (perhaps condensed versions of the Pre-Write and Post-Write activities mentioned below)
• Self-reflective comments (on various project drafts) to which you respond individually in writing
• Collaborative troubleshooting exercises that you have students report back on and respond to in the moment.

In Practice

Reflection & the metacognitive cycle.

In this section, we focus on activities and exercises you can use in and out of the classroom to provide the explicit training that will develop students’ reflective and metacognitive skills. People who study metacognition think about it in terms of metacognitive cycle, which at its most basic includes:

In the next section you’ll find several strategies for incorporating reflection at each stage of the metacognitive cycle.

Strategies for the Classroom

“planning” exercises.

In the planning stage, students think ahead to upcoming assignments and identify what tools, skills, knowledge, and resources they already have and what they will still need to acquire in order to get the work done. They also set goals for the tasks and develop strategies for achieving those goals.

Strategy 1: Role Playing. In terms of helping students understand who they are as learners in various communities, you can provide roles for students to play as they work on mastering elements of course content. This leads to a particular environment that supports the range of roles created, and challenges students to respond to the material— and the conversations in the course—in those roles. This can be a useful strategy for in-class discussions, peer review sessions, and written homework, such as blogging, essays, or reports. For example, before students begin an essay or project, talk to them about their role in the assignment (i.e. their role as the author). That is, they would benefit from thinking not only about who their audience is, but who they are as they address that audience.

You can certainly let them determine the role they want to play, however, in early iterations of this process, it helps if you provide a specific role, particularly one that they wouldn’t automatically take. For example, if the assignment is to make a case for lowering the legal drinking age, you might ask students to write their essay/create their presentation/build their site (whatever the task may be) as a concerned parent, or a conservative legislator, or a university president. This way, students must reflect on the differences between how they themselves might argue the case and how someone in another position might argue differently. They would reflect on their own identities, leanings, and perspectives, and then they would actively investigate or brainstorm those of the role they’ve assumed.

Strategy 2: Pre-writing. As you begin a new project (or exam, unit, essay, etc.), ask students to examine the prompt and write a reflection that does some or all of the following:

• Paraphrases what the project is calling for them to do in terms of the “big picture”
• Identifies (in their own words) the individual pieces, or tasks, or processes that will need to happen for them to successfully complete the project
• Identifies areas of the prompt that require clarification
• Identifies and articulates their role as the author/architect of this project (Who are they in the big picture? To whom are they speaking? For what purpose?)
• Considers the purpose of the assignment—what is its role in the course, but also how might it help them in the future?
• Lists or sketches out what they will need to know and/or know how to do in order to complete the project
• Lists or sketches out what they already know/know how to do in relation to the assignment
• Lists or sketches out what they will need to learn (a research question to pursue, a skill they need to develop, a tool they need to figure out how to use, etc.)
• Lays out a plan of action (the more specific it can be, the better … including self-imposed deadlines for “deliverables”)

Because research has shown that reflection is often effective when it’s social, it would be nice to give students time in class to break into pairs or small groups so they can share the results of their pre-writing exercise and discuss their reactions to the prompt. Furthermore, if you read these pre-writes quickly and respond to their questions for clarification as soon as possible, you can help students identify strategies they might need to reconsider before they begin, or course-correct misunderstandings about the project. You might want to look for common questions that you can clarify in the classroom, and then respond to any that need individual attention either in writing or via conference.

“Monitoring” Exercises

In the monitoring stage, students check in with you (and/or themselves) during the course of their work and report how things are going and where they might need to adjust or adopt new strategies. (This kind of reflection during a task is often referred to as “reflection-in-action”.) Students have indicated that these kinds of exercises often lead to the most productive reflection and learning opportunities.

Strategy 1: Collaborative Troubleshooting. Have students help each other out, either in a lab or with groups or partners, by reflecting on problems whenever they arise. For example, if students are working in class on drafting introductions, encourage them to turn to their neighbors to talk about what they’re struggling with as they write .

Strategy 2: Post Peer Review Follow-Up. After giving students time to read and consider the feedback they receive in a peer review, have them actively engage with that feedback as they plan their revisions. For example, ask them to choose something a peer disliked or disagreed with and respond to it in writing or in direct conversation with their peers. Alternatively, have them explain why they plan to follow a peer’s particular piece of advice, and/or explain why they have considered and dismissed a peer’s particular piece of advice.

Strategy 3: Self-Reflective Comments On Drafts. As students draft papers or other projects, ask them to insert a few comments that do the following: identify areas of struggle; ask for a specific piece of advice; and explain why they believe something specific aspect is already working well. Then, when you respond to their work, you can engage in direct conversation with them via those comments. For a thorough demonstration of how this practice can work, along with sample handouts you can provide to your students, refer to Supplement 1: “Self-Reflective Comments Handout Example.”

Strategy 4: Troubleshooting Journal. In this journal, students make note of any time they have a question or hit a “roadblock” in their work. Once they’ve noted the issue, they can seek help by talking to peers or to you, or by consulting other resources. They should keep an active record of their troubleshooting process, noting what strategies seem successful, and what strategies seem less so (and why). You can make this journal more productive by:

• Establishing a minimum number of entries, depending on your anticipation of or experience with the kinds of trouble students run into with the project you’re assigning.
• Discussing with the class what kinds of problems might arise (and when) and explaining how they can become aware that they’re in need of troubleshooting in the first place. For example, many students deal with “writer’s block” of some kind when writing essays. Maybe they struggle with developing a thesis, or organization, or writing a conclusion, or integrating evidence. However, many students don’t necessarily automatically think: “This is a roadblock. How can I get around it?” Therefore, you’ll need to prime them to be more intentional about identifying and dealing with the challenges of writing.

“Evaluating” Exercises

Finally, in the evaluating stage, students look back on the work they’ve done and reflect on the strategies, tools, resources, and/or processes that served them well. They should also think about what didn’t work as effectively, what they learned in the process, what they achieved, and how they might translate—or “transfer”—the experiences, skills, and knowledge gained to another context. (This kind of reflection is often referred to as “reflection-on-action.”)

Strategy 1: Dialogue About Feedback. Once students have had time to review your feedback on a project, ask them to write a letter back to you that addresses the following questions: What was most clear and helpful to you? What was your biggest take-away? What suggestions about revision do you agree with? Why? How will you put those into practice? What suggestions about revision do you disagree with? Why?

Strategy 2: Articulate Transferable Skills. Have students write a reflection in which they discuss a skill that the project helped them develop and ask them to imagine how the next writing experience could be made easier, more effective, or more efficient based on this writing experience. You might also ask them to imagine how they would solve problems in other writing scenarios or classes, such as other fields or professional work. For example, if they struggled with creating a cohesive narrative in a personal essay and used storyboarding to visualize what they were trying to do, how might they use that same skill in, say, a marketing course or in a job as a geologist?

Strategy 3: Project Post-Write. A post-write can be an effective way of getting students to think carefully about their process, the product, and the assessment of that product. After you’ve handed back a graded project with your feedback, invite students to consider how well their planning and/or monitoring strategies worked, and why they earned the particular grade (or other form of assessment). The post-write may take many forms, from a simple worksheet providing questions to answer, to an informal letter of advice to future students taking on similar projects, to a formally written reflective essay. Some questions you may want to have students consider are:

• How much time did you spend on this project?
• Creating a plan of action
• Reviewing course notes
• Talking with your peers
• Talking with your instructor
• Visiting the Writing Center
• Learning a new tool
• Pre-writing
• Given the time you spent on various aspects of the project, and the feedback you received, what would you do differently if you were to do it over? Why? What would you do the same? Why?
• What kind of plans and strategies did you make for completing this project? To what extent did you follow those plans? How and to what extent did they change as your work progressed?
• Looking at the feedback you received on your project, what strategies proved most effective for you, and how? What strategies didn’t work, and why? Based on your answers to these questions, what could you do differently next time to increase your chances of success?

Final Thoughts

The exercises suggested for each stage of the metacognitive cycle can be productively mixed and matched in a variety of ways. For instance, one effective combination is to use Planning Strategy 2 (Pre-Write), Monitoring Strategy 3 (Self-Reflective Comments), and Evaluation Strategy 3 (Project Post-Write) for one project or unit. We do suggest that you try different combinations—not only to find what seems most relevant and appropriate to your course, but also to keep students from falling back on rote responses when they get too used to a particular pattern.

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Metacognitive monitoring skills of reading comprehension and writing between proficient and poor readers

• Published: 31 August 2022
• Volume 18 , pages 113–134, ( 2023 )

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• Christian Soto 1 ,
• Antonio P. Gutierrez de Blume   ORCID: orcid.org/0000-0001-6809-1728 2 ,
• Verónica Rebolledo 1 ,
• Fernanda Rodríguez 1 ,
• Diego Palma 1 &
• Fernando Gutiérrez 1  

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Reading comprehension and writing are essential skills for success in modern societies. Additionally, reading and writing have been described as highly reflective activities that necessitate metacognitive monitoring and control. However, reading comprehension and writing are skills moderated by many factors, proficiency among them. Thus, in the present study we examined the influence of reading comprehension proficiency (proficient, poor) on elementary school students’ ( N  = 120) metacognitive monitoring accuracy in reading and writing tasks. Further, we investigated the predictive patterns of linguistic indices between proficient and poor readers on their metacognitive monitoring accuracy in a writing task. Findings revealed that proficient readers exhibited significantly better monitoring accuracy in both reading and writing tasks, and that unique predictive patterns of linguistic indices on writing skill monitoring accuracy emerged between proficient and poor readers. We discuss the implications of these findings for research, theory, and practice and propose recommendations for future research.

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This research was supported by a FONDEF ID grant, number 20I10290, titled “Literador: Un tutor inteligente que potencia las competencias en lectura y escritura” [Literador: An intelligent tutor that improves reading and writing skills], from ANID, Chile.

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Soto, C., Gutierrez de Blume, A.P., Rebolledo, V. et al. Metacognitive monitoring skills of reading comprehension and writing between proficient and poor readers. Metacognition Learning 18 , 113–134 (2023). https://doi.org/10.1007/s11409-022-09317-8

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Home — Essay Samples — Philosophy — Existence — Metacognition as a Powerful Phenomenon

Metacognition as a Powerful Phenomenon

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A questionnaire-based validation of metacognitive strategies in writing and their predictive effects on the writing performance of English as foreign language student writers

Ruru Zhang, https://orcid.org/0000-0002-5654-2402

Yanling Xiao, https://orcid.org/0000-0003-0025-2024

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

Introduction

This study—drawing upon data from a questionnaire—examined 503 Chinese university students’ metacognitive strategies in writing (MSW). The focus was on Chinese student writers who are learning English as a foreign language (EFL).

The examination was conducted through a survey on MSW and a writing test administered at the end of the semester. We employed exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) for data analysis. Multiple regression analysis was also adopted for understanding the predictive effects of strategies on writing performance.

The findings provided validity to MSW, including person, task, strategies, planning, monitoring, and evaluating. The different components of MSW were reported to significantly affect the participants’ writing performance. The findings highlight that EFL student writers were aware of metacognitive writing strategies. The MSW survey could be used to assess EFL students’ metacognitive writing strategies and develop curricula in writing strategy training.

Writing instruction can direct learners’ ability to acquire metacognitive writing strategies, particularly those of planning, monitoring, and evaluating, to build their awareness as agents in EFL writing. Relevant pedagogical implications are discussed.

Metacognitive strategies are essential to the process of learning to write when learning English as a foreign language (EFL; Nguyen and Gu, 2013 ; Teng, 2016 , 2019 ; Teng and Yue,2022 ). However, in the Chinese EFL context, for which English writing instruction typically emphasizes grammatical correctness rather than idea development, learners may find it difficult to build an awareness of using metacognitive writing strategies ( Ruan, 2014 ). Through a mixed-methods study, Amani (2014) found that explicit metacognitive strategy instruction had a positive impact on the writing competence of L2 writing students. However, in terms of EFL writing, university EFL students may find it challenging because of their lack of awareness of metacognitive writing strategies ( Teng, 2019 ). In addition, EFL learners in the Chinese context receive limited English language input, making it more challenging to learn to write. Student writers are expected to have repertoires of strategies when learning to write ( Raimes, 1987 ). In particular, they need to build an advanced level of “self-initiated thoughts, feelings, and actions” for them to “attain various literary goals” ( Zimmerman and Risemberg, 1997 , p.76). Hence, metacognitive writing strategies are essential to possible improvements in EFL writing.

Nevertheless, even though students are taught how to plan, monitor, and evaluate their own writing, students may know little about themselves as writers ( Leung and Hicks, 2014 ). They may also not recognize their own writing strengths or weaknesses, tending to overemphasize the latter and overlook any progress they have made or can make in their writing ( Teng, 2016 ). Wenden (1998) argued that metacognitive knowledge is a prerequisite for self-regulation, and metacognitive knowledge is essential to learner autonomy because it “informs planning decisions taken at the outset of learning and the monitoring processes that regulate the completion of a learning task and decisions to remediate; it also provides the criteria for evaluation made once a learning task is completed” (p. 528). Teng and Zhang (2021) argued that there is a dynamic and longitudinal relationship between metacognitive knowledge and reading and writing in a foreign language context. However, teachers may not recognize the importance of metacognitive knowledge in Chinese EFL writing contexts, wherein teaching academic writing is product oriented ( Teng and Zhang, 2016 ). The student writers were passive and found it difficult to keep positive beliefs in writing ( Bruning and Horn, 2000 ). This may be related to learners’ lack of awareness of self-regulation in writing. They may exert more effort learning vocabulary knowledge and grammar for writing, rather than being an agent for writing ( Graham and Harris, 2000 ). Student writers need self-awareness, motivation, and positive behavioral skills for writing ( Zimmerman, 2002 , p.65–66). Metacognitive writing strategies are thus essential to EFL students’ writing performance.

Self-regulation principles, measurements, and practices have a solid ground for enriching second and foreign language learning and teaching ( Teng and Zhang, 2022 ). Through a socio-cognitive approach to writing, Nishino and Atkinson (2015) argued that writing is primarily a cognitive activity and that cognition plays a vital role in writing and its development. To help students become competent English writers and autonomous learners, instructors need to support their development of metacognitive strategies. However, scarce attention was paid to writing strategies from the perspective of metacognition, particularly for low-achieving students in the EFL context. The present study examined Chinese university EFL students’ metacognitive strategies in EFL writing. We aim for the following purposes: (a) to assess the reliability of a new scale, which we named it as metacognitive strategies in writing (MSW) and (b) to explore how different components of MSW predict EFL students’ writing performance. The findings are insightful in helping researchers and classroom practitioners to diagnose the needs of metacognitive strategies in writing and develop guidelines for instructing writing courses for university EFL students. The findings shed lights on how to teach EFL writing and deliver more effective program for writing teacher preparation.

Literature review

Language learning strategies.

Oxford (1990) classified a list of language learning strategies based on cognitive learning theory. These strategies include memory, cognitive, compensatory, affective, social, and metacognitive strategies. Past studies have documented differences in strategy use between more and less successful learners. For example, successful learners use these strategies in larger numbers and at higher frequencies ( Magogwe and Oliver, 2007 ). Most importantly, cognitive and metacognitive strategies are associated with a higher level of language proficiency ( Peacock and Ho, 2003 ). However, contradictory findings were also reported, showing that less successful learners used more strategies than more successful learners did because the former automatized their language learning process ( Oxford and Cohen, 1992 ). Another point worth noting is that unsuccessful learners may adopt a large number of strategies frequently, but it does not necessarily mean that they are able to identify appropriate strategy use. In fact, it was reported that successful learners were able to identify appropriate strategies depending on the task requirements, but unsuccessful learners failed to choose the most appropriate and efficient strategies during the task ( Chamot and El-Dinary, 1999 ).

Although ample research has been reported relating to learners’ proficiency level and strategy use, learner variables, such as cultural background and national origin, could have a strong influence on learners’ strategy use ( Oxford and Nyikos, 1989 ). Therefore, their findings might not be generalizable to learners with completely different cultural backgrounds. In light of this, Lai (2009) conducted a questionnaire survey that investigated the relationships between the language learning strategies used by 418 EFL learners in Taiwan based on learners’ language proficiency and their use of strategies. While the more proficient learners used metacognitive strategies and cognitive strategies most frequently and memory strategies least frequently, the less proficient learners preferred social and memory strategies to cognitive and metacognitive strategies. This finding partially echoes Wu (2008) , who reported that higher-proficiency EFL students in Taiwan used learning strategies more often than lower-proficiency EFL students did, especially the cognitive, metacognitive and social strategies.

Although research documented in the literature examines general language learning strategy use, it is possible that these summarized findings could serve as a reference for the specific examination of metacognitive strategy use during English writing.

Understanding metacognition

Metacognition is multidimensional and domain-general. When we talk about metacognition, we may need to mention the theory of mind ( Flavell, 1979 ). Such theory is the foundation of understanding metacognition. Generally, metacognition is related to self-regulatory capacity because metacognition provides individuals with domain knowledge and regulatory skills that are essential to become an agentive learner in relevant domains ( Schraw, 2001 , p. 7). Metacognition refers to how learners build an awareness of their own thinking processes and executive processes ( Flavell, 1979 ). Metacognition is essential to helping learners regulate their cognitive processes, and finally, becoming an independent thinker and learner. Zhang and Zhang (2019) applied metacognition in second and foreign language learning, and posited that EFL learners need to plan, monitor, and evaluate their cognitive processes for better language learning performance.

Metacognition includes metacognitive knowledge and metacognitive regulation. Flavell (1985) suggested that person, task, and strategy knowledge are three key elements of metacognitive knowledge. Wenden (1998) explained the three elements. For example, person knowledge is the knowledge for the learners to control their cognitive processes. Task knowledge is the knowledge that can be helpful for the learners to understand the purpose, nature, and demands of different task conditions. Strategy knowledge is the knowledge of different important strategies that are helpful for realizing the pre-determined goals. Metacognitive regulation entails three skills: planning, monitoring, and evaluating ( Schraw, 1998 ). Planning refers to the ability to appropriately select the strategies and adequately allocate the resources for completing tasks. Monitoring refers to learners’ capacity to observe their task performance. Evaluating means learners’ capacity to reflect on their learning outcome and the use of different strategies for self-regulation.

Teng et al. (2022) summarized the procedures of understanding metacognition. First, monitoring function and control of cognition are two important functions of metacognition. In order to realize the functions, individuals need to process three major stages, i.e., acquisition, retention, and retrieval. Second, learners need metacognitive knowledge and metacognitive experiences to process the monitoring function. In contrast, they need metacognitive strategies or metacognitive skills to fulfill the needs of control of cognition. Third, metacognitive knowledge, metacognitive experiences, and metacognitive skills are interconnected with each other. Metacognitive knowledge includes person, task, and strategies. Metacognitive experiences include feelings and judgments. Metacognitive skills are important for their metacognitive regulation, which needs learners to plan, monitor, and evaluate their learning process. Finally, reflection is the outcome of the interconnected process of planning, monitoring, and evaluating ( Figure 1 ).

The multifaceted elements of metacognition ( Teng et al., 2022 , p. 171).

Metacognitive strategies in EFL writing

Macaro (2010) maintains that strategic behavior plays a vital role in second language learning success and proposes that strategic behavior should be essential to linguistic knowledge resources. Dornyei (2010) emphasizes that students need a repertoire of appropriate task-related plans, scripts, and self-regulatory strategies that are activated by their ideal L2 selves; that is, learners’ aptitude, motivation, goals, and self-regulatory strategies all interact and affect one another in the SLA process. Writing strategies include rhetorical strategies, metacognitive strategies, cognitive strategies, and social/affective strategies ( Wenden, 1991 ; Riazi, 1997 ). Writers explore rhetorical strategies to organize and present their ideas based on the writing conventions of the target language. Metacognitive strategies are used to monitor the writing process consciously and evaluate the effectiveness of writing actions. Cognitive strategies are used to implement actual writing actions. Social/affective strategies are employed to interact with others and to regulate emotions, motivation, and attitudes in writing.

Wenden (1991) classifies writing strategies based on metacognitive and cognitive frameworks. She distinguishes general executive metacognitive strategies of planning, self-monitoring, and self-evaluating from more specific cognitive strategies, such as clarification, retrieval, resourcing, avoidance, and verification. Each of these metacognitive strategies is discussed below.

Planning for writing involves thinking and self-questioning strategies such as identifying one’s purpose, activating background knowledge, and organizing ideas. Planning is not limited to a specific stage of writing but rather appears recursively throughout the writing process. Flower and Hayes (1981) identified three different types of planning strategies based on the focus of the goal: (1) generating ideas; (2) setting procedural goals; and (3) organizing. Generating ideas includes retrieving information from long-term memory, revising old ideas to incorporate new information, drawing inferences, making connections, and looking for examples, contradictions, and objections. Setting procedural goals includes content goals (e.g., plans for content, text structure and audience, and criteria for evaluation) and process goals (how to proceed, generated by the writer, done at any time during the composing process, followed or preceded by generating ideas, revising strategies, etc.). The third strategy (organizing) includes selecting the most useful materials produced during the generating process and organizing them in the writing plan. Organizing strategies include grouping and sequencing ideas, deciding on the presentation of the text, planning the introduction and conclusions, and structuring the text based on a particular genre. Furthermore, in using these strategies, it is essential to consider the audience, topic, and rhetorical knowledge. Planning in EFL writing determines how writers write in subsequent stages. It engages them in metacognitive activities that allow them to consider the purpose and goals for writing, identify their audience, decide upon voice, and generate a framework for their essays.

Monitoring involves conscious control and regulation of the writing process. Hayes and Flower (1980) include self-monitoring in their model of the cognitive processes of writing, noting that the ability to self-monitor the composing process is an important part of writing strategies. Charles (1990) claims that self-monitoring makes it easier for L2 students to avoid uncertainty about any part of their text, to find direct answers to their queries and to encourage them “to look critically and analytically at their writing and to place themselves in the position of readers” (p. 289). The more important functions of self-monitoring are controlling, directing, and sequencing the composing processes and one’s progress in the task. Monitoring allows the writer to decide whether something needs to be retrieved, whether new ideas need to be further generated, or whether a given subprocess has ended. Monitoring allows L2 writers to evaluate the effectiveness of writing strategies and how and when to check the outcomes of problem-solving processes and strategically regulate the processes according to cognitive goals ( Mayer, 1999 ).

Self-evaluating—experiencing the quality of one’s writing in relation to one’s goals—is crucial for developing an individual’s perception of writing. In self-evaluation, students can recognize weaknesses, identify needs, and make changes ( Zimmerman, 2002 ). In cognitive research, evaluation has been characterized as a strategy for considering the outcome of the undertaken task, an essential metacognitive strategy that successful learners need to execute and control.

Empirical studies on the use of metacognitive writing strategies

Various studies have been conducted on EFL students’ use of metacognitive writing strategies. Employing think-aloud protocols and immediate retrospective interviews, Chien (2012) investigated the differences in writing strategies and English writing achievements of 20 low-achieving and 20 high-achieving student writers in Taiwan. Chien found that high-achieving student writers were more aware of and focused more on, formulating their position statements when planning, generating, revising, and editing their essays and focused more on correcting grammatical and spelling errors. Teng and Zhang (2016) validated questionnaire-based self-regulated strategies in EFL writing and highlighted planning, monitoring, and evaluating in EFL writing. Teng and Huang (2019) also suggested that learners’ self-regulated strategies in writing, as well as their English proficiency and language learning experiences, and significantly influenced their EFL writing. In a recent publication ( Teng et al., 2022 ), two experimental studies were reported. Study 1 adopted a factorial design using exploratory and confirmatory factor analysis to validate a self-regulatory writing strategy questionnaire. Study 2 assessed the predictive effects of the different components of the scale on students’ writing performance. The results supported the construct validity for the six strategy factors, i.e., writing planning, goal-oriented monitoring, goal-oriented evaluation, emotional control, memorization, and metacognitive judgment. The factors also predicted writing performance. Zhang and Qin (2018) also validated the newly developed scale on metacognitive strategies in a multimedia writing context. The results provided evidence for the validation of planning, monitoring, and evaluating strategies. In an early empirical study on the importance of planning in EFL writing, Graham et al. (1995) examined differences between expert and less-skilled L2 writers. They found that expert L2 writers spent considerable time planning and appeared to have higher-level plans and self-conscious control of their planning. In contrast, less-skilled EFL writers were less likely to use knowledge of textual structure in planning, to use heuristic strategies in searching their memory for content, or to establish goals to direct the writing process and were more likely to engage in “knowledge telling” (i.e., writing everything they knew about a topic and stopping when they felt that they had written down everything they knew). Less-skilled writers did not write with goals or plans in mind; rather, they tended to generate ideas through free writing and usually did not organize those ideas. As shown in a longitudinal study ( Teng and Zhang, 2021 ), learners’ L2 writing development was dependent on their initial level of metacognitive knowledge. This is evidence for the strong correlation between metacognitive knowledge and writing.

Nguyen and Gu (2013) explored the impact of strategy-based instruction on promoting learner autonomy (operationally defined as learner self-initiation and learner self-regulation) of students at a Vietnamese university; 37 students were in an experimental group, and 54 students were in two control groups. After an 8-week metacognition training intervention, students in the experimental group were found to have improved their planning, monitoring, and evaluating of a writing task more than those in the two control groups. The findings suggest that strategy-based instruction on task-specific metacognitive self-regulation improves learner autonomy and writing performance. Teng (2020) also incorporated training of metacognitive strategies for EFL learners. There were two groups of learners, i.e., those with group feedback guidance and those with self-explanation guidance. The results supported the positive effects of group metacognitive support on EFL students’ writing. EFL students need to build a certain level of metacognitive awareness to manage themselves as writers.

Bai et al. (2014) conducted a questionnaire survey to explore the relationship between 1,618 Singapore primary school pupils’ reported use of strategies in learning to write and the correlation with their English language proficiency. They found that participants used a wide range of writing strategies at medium frequency. They also reported a significant correlation between the participants’ English language proficiency and the use of writing strategies such as planning, text-generating, revising, monitoring and evaluating, and resourcing. Similar results were also found in Bai and Guo (2021) , wherein high achievers reported higher levels of motivation (i.e., growth mindset, self-efficacy, and interest) and self-regulated learning strategy use than the average achievers, and average achievers reported more strategy use than the low achievers, Ma and Teng (2021) collected qualitative data from two undergraduate university students learning English as L2 in Hong Kong to explore their use of writing strategies. They reported that both students realized the importance of self-evaluation and revision. It seems that the students perceived affordances in the kind of writing that enabled them to play an active role in seeking, interpreting, and using teacher feedback to perform the evaluation and modification of their own work. However, variations in engagement in the process of learning to write and their metacognitive knowledge development were also detected. For example, students’ varying degrees of engagement may result in various degrees of developing metacognitive awareness. Teng et al. (2022) validated a new instrument, i.e., the Metacognitive Academic Writing Strategies Questionnaire (MAWSQ). Analyses were conducted through a series of Confirmatory factor analyses (CFA). Results supported two hypothesized models, i.e., an eight-factor correlated model and a one-factor second-order model. Model comparisons supported the role of metacognition as a higher-order construct. Metacognition also explains the eight metacognitive strategies, including declarative knowledge, procedural knowledge, conditional knowledge, planning, monitoring, evaluating, information management, and debugging strategies. Those strategies also significantly influenced EFL writing performance.

Overall, the studies on metacognition development reviewed in this section highlight the importance of the high-level cognitive processes involved in composing, the development of the autonomous and self-regulated use of effective writing strategies, and the formation of positive attitudes about writing. Metacognitively oriented learners are aware of both their own learner characteristics and the writing task and are able to select, employ, monitor, and evaluate their use of metacognitive strategies.

The present study

Metacognition functions as an important predictor in EFL writing performance. We aim for two purposes in the present study. First, we attempted to validate a questionnaire on metacognitive strategies in writing. Second, we assessed the predictive effects of different metacognitive strategies in the outcome EFL writing. The present study sheds light on learners’ awareness and use of metacognitive writing strategies. The present study includes two questions:

• What is the evidence to support the validity and reliability of metacognitive strategies in writing?
• What is the evidence for the predictive effects of metacognitive strategies on EFL writing proficiency?

Materials and methods

Participants.

The present study included 503 participants. They were undergraduate students at a university in China. They were first-year students with Chinese as their first language and English as a foreign language. They had received at least 6 years of formal English instruction. Writing is a subject to be taught in college English and a compulsory course for all the participants. We selected the participants because they were all enrolled in a university English course. The first author was teaching the participants, and the sample of participants was a convenient sample. Among the 503 students, 351 were men and 152 were women. An unequal gender balance may be because most of the students were from science and engineering majors. Originally, there were 700 students who responded to the questionnaire. We finally selected data from 503 students for data analysis. Some participants’ data were excluded because of missing values or because some were unable to take the writing test. They attended the study voluntarily by signing the consent form.

Questionnaire development

The questionnaire, which was named Metacognitive Strategies in Writing (MSW), was developed through item generation, reference consultation, initial piloting, psychometric evaluation, and exploratory factor analysis (EFA) in a pilot study. We first invited 10 students to reflect on their writing practices and strategies. The students were mainly interviewed about the strategies they adopted for writing. We generated approximately 50 items based on analyzing the transcriptions of learners’ interviews. In the next stage, we consulted relevant literature on metacognition, self-regulation, and language learning strategies ( Schraw and Dennison, 1994 ; Oxford, 2013 ; Teng et al., 2022 ). We selected the items that fit with metacognition theories. In the third stage, we invited the 10 students to check the items. In the fourth stage, which was psychometric evaluation, we invited two researchers in L2 writing to assess the items. Based on the comments, we finally removed 10 items. In the final stage, we ran an EFA with a sample of 360 students with similar backgrounds. We deleted 10 items with unsatisfactory factor loading values. The final questionnaire includes 30 items, which are in the Appendix .

This questionnaire was a novel one as it was based on metacognition theory, through which the focus was on understanding metacognitive knowledge and regulation in learning to write. We adopted a seven-point Likert scale (i.e., from 1, Strongly disagree to 7, Strongly agree). MSW focuses on metacognitive knowledge and metacognitive regulation. Metacognitive knowledge includes three factors, i.e., person, task, and strategies. Metacognitive regulation includes three factors: planning, monitoring, and evaluating. Cronbach’s alpha, which ranged from 0.81 to 0.90 for the six factors, ensured the internal consistency of responses to the items. The questionnaires were administered to the participants in Chinese. The author translated into Chinese while a research assistant was invited to check the translated items through back translation.

Writing test

A writing test from IELTS (writing task 2) was adopted to measure learners’ writing proficiency. Students were required to write at least 250 words within 1 h. Students were asked to respond to the topic provided by giving and justifying an opinion, discussing the topic, summarizing details, outlining problems, identifying possible solutions and supporting what they wrote with reasons, arguments and relevant examples. The topic proposed the possible influence of social media sites on personal relationships.

The marking scheme was consistent with the writing rubrics in IELTS. However, we adjusted it to fit with our school assessment needs. Each learner was awarded with six marks for task response, coherence and cohesion, lexical resource, and grammatical range and accuracy. The maximum possible score was 24 points. A total of 40 English teachers were paid to rate the writing. The teachers did not know the participants’ identities. They also joined a training session on the marking scheme. Disagreements on marking were subject to further discussion. The Cronbach’s alpha for the test was.85, indicating acceptable reliability.

We invited 20 EFL teachers to help us distribute a QR code to the students through WeChat group. The students spent an average of 6 min completing the questionnaire. The writing test was administered as an exercise for all students during class. They needed to complete it within 1 h. The format for the writing test was a paper-and-pencil format. All participants received the same format for the questionnaire and the writing test.

Data analysis

The final dataset was run through a series of confirmatory factor analyses (CFAs). STATA was used for data analysis. CFA is used to test a theoretical model by confirming factors, correlations, covariance patterns, and residual or error values within a data matrix ( Byrne, 2016 ). We used the maximum likelihood (ML) estimation method. The model fit was evaluated through the following statistics: a chi-square statistic, the degrees of freedom (df), p value, the ratio of chi-square χ 2 divided by the df, the root mean square error of approximation (RMSEA), the standardized root mean square residual (SRMR), the comparative fit index (CFI), and the Tucker–Lewis Index (TLI; DiStefano and Hess, 2005 ). The following criteria are a relatively good fit between the hypothesized model and the observed data: the value of RMSEA should be close to 0.06, the value of SRMR should be close to 0.08, and the values for CFI and TLI should be close to 0.95 ( Hu and Bentler, 1999 ). Finally, multiple regression analysis was adopted to evaluate the predictive effects of MSW on students’ writing proficiency.

Descriptive statistics

The kurtosis and skewness values for the metacognitive strategies in writing, as well as the mean and standard deviation, are shown in Table 1 . The means of the six factors ranged from 3.346 to 4.079, with the two factors, monitoring and evaluating, greater than 4. There were no noticeable variations based on the standard deviation values.

Means, standard deviations. and normality test.

Exploratory factor analysis in the pilot study

Exploratory factor analysis was conducted on a sample of 360 learners from similar background in the pilot study. We examined the adequacy of the sample. The Kaiser-Meyer-Olkin value was 0.914, which appropriate for EFA ( Tabachnick and Fidell, 2001 ). Bartlett’s test of sphericity was significant, p < 0 .001; thus, the matrix was adequate for factor analysis. We adopted principal component analysis as a factor extraction method. We finally extracted six factors that explained 57.411% of the variance ( Table 2 ). The scree plot showed a considerable drop after the sixth factor, for which we excluded other possible factors. Based on key theories in metacognition, we named the six factors as following: person, task, strategies, planning, monitoring, and evaluating.

Extraction results for the six factors.

The six factors’ eigenvalues exceeded 1. The next step was to examine the factor loadings. We deleted 10 items with factor loadings lower than 0.4. The final version included 30 items across six factors ( Table 3 ). Items’ factor loadings ranged from 0.534 to 0.772, while communality ranged from 0.531 to 0.754. The items hence fit their respective factors well.

Results on factor loadings and the communality.

Construct validity of metacognitive strategies in writing through CFA

The data fitness metrics for metacognitive strategies in writing are displayed in Table 4 . Table 4 shows that the RMSEA was 0.073, less than 0.08, indicating a good fit; CFI, TLI, CNFI, IFI, and GFI all exceeded 0.9, which was ideal for adaptability. Although the χ 2 /df was 7.916, larger than 3, the scale on metacognitive strategies in writing still showed reliability when taken as a whole.

Model fit indices for metacognitive writing strategies.

According to Figure 2 and Table 5 , the factor loadings for Person, Task, Strategy, Planning and Evaluating were all greater than 0.5, while Monitoring was 0.41. Additionally, the average variance extracted (AVE) for each variable was 0.47, and the model’s convergent validity was good, as evidenced by the composite reliability (CR) being 0.84, indicating that the model had satisfactory convergent validity.

A first-order model of metacognitive strategies in writing. Prs, Person; Tsk, Task; Str, Strategy; Pln, Planning; Mnt, Monitoring; and Evl, Evaluating.

Convergent validity of the model.

Predictive effect of metacognitive strategies in writing on EFL writing

Figure 3 presents the correlations between metacognitive strategies in writing and L2 learners’ writing proficiency in English. The findings indicated that each of the six metacognitive strategies was significantly correlated with learners’ English writing performance. Writing performance (WP) was correlated with Person ( r  = 0.264), Task ( r  = 0.500), Planning ( r  = 0.584), and Monitoring ( r  = 0.408). Strategy ( r  = 0.470) and Evaluating ( r  = 0.470) were significantly correlated with WP.

Spearman correlation for metacognitive writing strategies and L2 learners’ proficiency in English. Persontotal, Person; Tasktotal, Task; Strategytotal, Strategy; Planningtotal, Planning; Monitoringtotal, Monitoring; and Evaluatingtotal, Evaluating.

Moreover, we adopted a structural equation model to investigate the degree to which metacognitive strategies in writing predicted learners’ L2 writing proficiency. Table 6 presents the model fitness indices. For our model, seven indices (i.e., χ 2 /df, RMSEA, CFI, TLI, NFI, WIFI, and GFI) indicated acceptable model fit ( Table 6 ). Figure 4 shows a structural equation model of the relationship between metacognitive strategies in writing and writing proficiency. The six variables on the left side of the model represent the six factors of metacognitive strategies in writing. The only rectangular variable on the right side of the model was EFL learners’ writing proficiency. The findings demonstrated that metacognitive strategies in writing had a predictive power of 0.65 for L2 learners’ writing proficiency, indicating that it could account for 65% of the variances in writing performance.

Model fit indices for metacognitive writing strategies on writing performance.

The structural equation model of metacognitive strategies in writing proficiency.

Regression analysis was employed in the study to show the extent to which each factor impacts writing performance. The results presented in Table 7 demonstrate that all factors significantly predicted writing competence ( p  < 0.001), with the exception of Strategy ( p  = 0.344). Planning had the greatest effect on writing abilities, and Task had the least effect. Notably, monitoring and evaluating also had a great effect on EFL learners’ writing proficiency. According to the findings, there was no multicollinearity among the strategies, as indicated by the variance inflation factor (VIF), which was less than 3. In addition, the residuals adhered to a normal distribution, as shown in Figure 5 . This offered a trustworthy foundation for the regression analysis results.

Linear regression results.

Normal P–P plot of regression standardized residual.

Discussion and conclusion

Overall, the present study aims to answer two research questions. The first research question entails the validation of a newly developed scale, which we named Metacognitive Strategies in Writing (MSW). The scale was developed based on metacognition theory. The findings supported the factorial structure of the scale. The second research question aims to answer the predictive effects of different factors of MSW in writing performance. Overall, the findings provided evidence for the factorial structure of MSW. The findings also suggested the predictive effects of different factors on writing performance.

Validation of MSW

First, MSW is with satisfactory psychometric properties. The six factors were reliable in terms of conceptual and empirical evidence. The six factors were distinct but correlated with each other. Consistent with previous studies ( Teng et al., 2022 ), metacognition is an important construct that can explain the significant correlations of different lower-order metacognitive dimensions in writing. In line with Schraw and Moshman (1995) , metacognition is a domain that can explain self-regulatory capacity. The present study thus provides insights into metacognition theory, which can entail person, task, strategies, planning, monitoring, and evaluating ( Schraw and Dennison, 1994 ). These strategies are interconnected and reflect the metacognitive process in writing. To build metacognitive awareness, learners need to be engaged in self-reflection and controlling of cognition ( Paris and Winograd, 1990 ). In terms of writing, student writers need to assess their knowledge states and executive abilities to orchestrate different dimensions of metacognitive awareness. Overall, the sum of the six strategies in writing indicates EFL student writers’ overall level of metacognitive awareness in writing.

The six factors were interpreted through metacognitive knowledge and regulation. The two paradigms were also conceptualized in early studies ( Flavell, 1979 ; Schraw, 1998 ; Wenden, 1998 ). In the present study, the two paradigms can represent key elements of metacognition. Person, task, and strategies represent learners’ beliefs and knowledge about themselves. Planning, monitoring, and evaluating reflect the process of cultivating one’ self-regulatory capacity for learning to write ( Teng and Zhang, 2016 ; Teng et al., 2022 ). The findings showed a positive and significant relationship between metacognitive knowledge and regulation ( Pugalee, 2001 ; Teng, 2016 ). We may need to reconsider the strong connection between metacognitive knowledge and regulation. The positive correlation may reflect the need of both knowledge and regulation in learning to write. For example, EFL students may need cognitive, metacognitive, and regulatory skills and strategies for writing ( Teng, 2020 ). The importance of metacognitive knowledge and regulation may reflect the argument by Wolters (1999) that learners’ engagement, effort, and achievement are influenced by their metacognitive knowledge and regulation. Hence, metacognition is essential to the development of self-regulated capacity ( Efklides, 2008 ), build identity as a student writer ( Zimmerman and Risemberg, 1997 , p.76), and develop self-awareness in processing their second and foreign language learning ( Zhang and Zhang, 2019 ).

Overall, the MSW data suggest that the student writers adopted metacognitive knowledge, i.e., person, task, and strategies, to understand their strengths and weakness in writing, demands in writing, and solutions for solving problems in writing. The data also suggest that the planning strategy should be used. In the planning stage, the student writers directed their attention to fulfilling the goal of the task, planning thoroughly, evaluating the relevance and effectiveness of ideas, and eliminating inappropriate examples. Data regarding the second subscale (monitoring) reflected that students tended to use some metacognitive monitoring strategies. During the monitoring stage, the student writers focused on the overall essay development, concentrating on expanding and developing their initial ideas, evaluating their essay for clear development and focus/unity, and ignoring interruptions posed by language constraints, such as grammar and vocabulary. For the third subscale (self-evaluating), student writers tended to use certain metacognitive strategies. Student writers prioritized their attention to evaluating the unity and effectiveness of their writing before editing local errors, such as grammar, vocabulary, mechanics, and sentence variation.

Predictive effects of metacognitive strategies in writing

The findings suggest the predictive effects of metacognitive strategies in writing. The results confirmed that the metacognitive strategies significantly predicted learners’ writing performance, which was consistent with previous studies ( Teng and Huang, 2019 ; Teng et al., 2022 ). One reason is that student writers’ meager metacognitive knowledge base could result in unsatisfactory cognitive monitoring of production and progress toward the writing task goal, which, in turn, may also affect their writing performance ( Teng et al., 2022 ). For example, lower-level writers tended to be bound to the local areas of writing, focusing on language correctness, while higher-level writers tended to focus on developing ideas and revising at the discourse level, saving editing until later ( Teng and Huang, 2019 ). As supported in previous studies ( Chien, 2012 ; Bai et al., 2014 ), higher level student writers were more aware of metacognitive strategies and used them more frequently in writing.

The argument revealed, at least for this particular sample and the chosen test, a strong and significant link between the writing abilities of EFL students and the factors of person, task, strategy, planning, monitoring, and evaluation. The EFL learners’ writing performance variations were accounted for by the six metacognitive components. The findings complement cognitive writing model of Flower and Hayes (1981) , which recognizes the abilities in process writing such as planning, monitoring, and reviewing. Writing necessitates the adaptive use of emotional strategies, performance strategies, and cognitive strategies ( Teng et al., 2022 ). The effectiveness of the strategies highlights the personal, behavioral, and environmental impacts on the regulatory capacity in learning to write ( Zimmerman and Risemberg, 1997 ).

In our study, person and task significantly predicted writing performance with a large effect size. According to earlier research ( Brown, 1987 ; Schraw, 2001 ), learners who have declarative, procedural, and conditional knowledge are more likely to become strategic learners. These results provide evidence for the idea that to master writing, EFL learners need to be able to distinguish among the various strategies, employ the appropriate strategies, and apply these strategies in their writing. The results also support earlier research that metacognitive knowledge is crucial for encouraging active involvement in applying their understanding of the writing process, recognizing the kinds of strategies useful in the growth of writing, and improving students’ writing outputs ( Ruan, 2014 ).

In terms of metacognitive regulation, planning, monitoring, and evaluating are also important for writing performance. The effect size was quite large in the current study, for which we can detect similar results in previous studies ( Teng, 2019 ; Teng et al., 2022 ). The writing abilities of students who were more self-controlled in their writing were higher in terms of goal setting, time management, and planning for writing resources ( Teng and Zhang, 2016 ). We argue that Chinese EFL students need an awareness of planning ahead and monitoring and evaluating their planning tactics to produce successful written essays. The success of EFL academic writing depends heavily on this method. Academic writing development may be seen as a complex process for student writers because it depends on how strategically they seek information and modify their planning techniques. Students who have prepared well for academic writing are typically those who have a high level of metacognitive awareness of their writing-related objectives ( Zhang and Qin, 2018 ). When composing their essays, lower-level writers often experienced difficulty in transferring ideas to paper during the planning, monitoring, and self-evaluating stages. The constraints in the lower-level writers’ knowledge system, including their limited linguistic competence (grammar and vocabulary), their confusion about their role as writers, their lack of knowledge strategies for overcoming writing difficulties, and their lack of knowledge of how and when to apply those strategies, impeded their composition of a meaningful essay. Consequently, many students tended to simultaneously engage in a few different stages of writing—planning, composing, revising, and editing—without any extra attention resources to monitor the overall unity and coherence of the essay, thus making the essay messy and confusing.

Limitations and implications

Despite the positive findings, we still need to acknowledge some limitations of this study. First, the strategies described in the questionnaire were still scarce, although we showed excellent content validity. Due to the limited amount of time the learners could invest in data collection, we did not assess metacognitive experiences, another crucial component of metacognition. Interview data with students were not conducted to yield adequate methods connected to metacognitive experiences. Second, a self-report questionnaire served as the foundation for this study. Because they are dependent on the use of self-reported information, surveys may not fully reflect learners’ actual metacognitive awareness and activities. The quantitative data in future studies should be triangulated with interview data. Third, the writing test should include additional activity categories that can gauge various writing abilities. We only used one writing performance indicator. The performance of student writers may also be impacted by individual characteristics, including their language learning experiences and English proficiency level ( Teng and Huang, 2019 ). Future studies might look at learners’ individual differences and their use of different metacognitive strategies.

However, there are also some implications based on the findings. Our findings suggest directions for pedagogy as well as future research. Considerations include issues of focus on form, development of metacognitive awareness to support metacognitive knowledge and strategies, and appreciation of the many aspects of metacognitive awareness that good L2 writing entails.

Data collected from the surveys suggest a strong connection between EFL student writers’ metacognitive knowledge and the regulation strategies they employ. Helping students become more aware of themselves as writers and the metacognitive resources upon which they can draw during the writing process may help them develop their writing competence. Language teachers and instructors should clearly instruct the importance of metacognitive strategies for EFL student writers. Related to this, metacognitive training should help students develop such awareness in learning to write. However, an important step in developing productive pedagogy for metacognitive training is assessing learners’ needs and understandings of their metacognitive strategies. The MSW might potentially contribute to EFL writing assessment in China. The MSW monitoring subscale identified the important first step in writing—planning—as a potential problem. So far as these Chinese EFL non-English major student writers were concerned, regardless of their level of English class or their majors, it seems that many of them may need to faster a metacognitive awareness. As a result, it might be helpful to provide these students with additional lessons on metacognitive strategies to address their concerns and the problems evident in their English writing. While dealing with grammatical errors is essential to writing instruction, the students should focus not only on identifying the errors and fixing them but also on finding out why they make those mistakes and how to avoid making them again. In other words, instead of correcting the errors, they should also develop their awareness of metacognitive strategies to improve their overall language competence. The instructors may also explicitly teach and demonstrate effective strategies to enhance vocabulary acquisition, such as making learners aware of lexical morphology (including word roots and suffixes), synonyms, antonyms, word categories, and similar spellings.

Clearly, it should not be assumed that learners who do not score high on norm-referenced assessments of their L2 writing need to focus exclusively on their metacognitive strategies, even though that is where they may think they need to work. Rather, these learners need to consider not only metacognitive strategies but also discourse organization and considerations of audience, voice, and genre ( Hyland, 2007 ). It is only through an approach raising their awareness of the various aspects that contribute to good writing and through work on writing and revision strategies that they will progress optimally. Additionally, to implement these recommendations for pedagogy, teachers themselves must have substantial knowledge, professional development, and practice regarding approaches to support L2 writing. In the Chinese context, knowledge must be processed and understood in light of the metacognition and experiences of students, colleagues, and the community.

Data availability statement

Ethics statement.

The studies involving human participants were reviewed and approved by Hainan University. The patients/participants provided their written informed consent to participate in this study.

Author contributions

CQ: Coordinated the study, drafted, and revised the manuscript. RZ: Data collection, drafted literature review. YX: Participated in the design of the study, revised the manuscript and performed the statistical analysis and data interpretation. All authors proofread and approved the final manuscript.

This article is supported by the Project from the Education Department of Hainan Province, Project number: Hnky2020ZD-9.

Conflict of interest

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

Publisher’s note

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

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2022.1071907/full#supplementary-material

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Metacognition: Understanding How You Learn [Tips, Examples, & Infographic]

Metacognition is the process of thinking about how your brain works . It involves actions like planning, monitoring, and assessing your learning and performance. Usually, teachers develop metacognitive skills in their students in order to enhance their learning abilities and help them adapt to new challenges. However, improving metacognition on your own is also important because it can help you identify your strengths and weaknesses as a learner.

Metacognitive practices include awareness, critical thinking, and effective learning . There are many ways to improve these skills and boost your academic performance. By using these practices, you will also learn how to recognize the limits of your knowledge, go beyond those limits, and become the best version of yourself. This article will discuss the benefits of metacognitive practices and how you can incorporate them into your daily life. We recommend you start your cognitive growth by reading and analyzing our research essay samples.

📜 What Is Metacognition?

• 🤔 20 Metacognitive Questions
• 🚀 7 Strategies to Study Smarter
• 👨‍🏫 Conclusion + Final Tips

🔗 References

The term metacognition comes from the Greek word meta , meaning “beyond,” and the Latin word cognoscere , meaning “getting to know.” The concept was first introduced by an American developmental psychologist, John Flavell, in 1979. Flavell described metacognition as people’s understanding of their own cognitive processes.

Metacognition is one’s ability to control their own thinking processes by monitoring, organizing, and adapting to various learning situations. Additionally, metacognitive skills allow people to reflect on learning tasks and select the appropriate strategies to deal with them successfully.

Importance of Metacognition in Education

Metacognition is a critical component for students to succeed in new learning environments like online education, international exchange programs, or project-based classes. Metacognitive skills have become a necessary foundation for all disciplines and contexts because these practices help students understand what they know, what they don’t know, and how they can change that.

These are the benefits of metacognition in education:

• It helps you achieve better academic performance. Research has shown that metacognitive skills can compensate for knowledge limitations as well as improve your grades and understanding of the subject matter.
• It improves independent learning. Having mastered metacognitive skills, students can monitor their progress inside and outside the classroom.
• It boosts emotional and social growth. As they gain awareness of individual strengths and weaknesses, students develop healthy self-esteem and act more confidently when socializing with peers, professors, and future employees.

Metacognitive Skills in Learning

Consider how these metacognitive skills can rocket your learning experience to a new level.

Knowing your blind spots.

It’s more than okay to admit you don’t know something. Understanding the most complex subjects starts with simple awareness. Teachers highly value students who aren’t afraid to ask additional questions when they don’t know something.

Evaluating your performance.

You can advance more efficiently by monitoring every step you take towards your goal. For example, when preparing for a test like SAT or ACT , pay attention to the memorizing strategies that work the best for you. Following them to save a lot of time in the future!

Spending enough time on preparation.

We don’t recommend rushing when it comes to learning. Complex tasks usually require spending ample time on research. Whenever you’re preparing for a test or an exam, it’s better to practice daily instead of cramming the night before.

Regular goal-setting.

Setting concrete goals can inspire you to develop necessary problem-solving skills and understand how to overcome challenges. We recommend thinking about your plan in detail and trying to foresee the potential problems in your way. Besides, there’s nothing more inspirational than a sense of accomplishment when you cross a goal off your to-do list!

Asking for feedback.

Asking your teacher or peers for feedback greatly influences the quality of your learning process. Sometimes other people see our strengths and weaknesses better than we do. However, note that listening to feedback is not enough. You have to incorporate it into your life in order to achieve personal growth .

Keeping a diary.

Keeping a diary helps you to organize your thoughts and make them more straightforward. It is also a beneficial tool for tracking your progress over the years. Once you start journaling regularly, you will feel an urge to express your thoughts and emotions, which is very helpful when you want to reflect on them later.

Taking time to self-reflect.

Self-reflection improves your memory and keeps your mind active. Looking back on what subjects you liked and disliked, what tasks you found stressful, and what mistakes you typically made can give you a better understanding of your academic path.

🤔 20 Questions to Evaluate Your Metacognition

The best way to master metacognitive skills is to ask yourself the right questions. By asking yourself psychological questions, you encourage self-reflection and can think deeply about yourself as a person and learner.

Here’s a list of questions that will improve your self-awareness.

🚀 Metacognitive Strategies – How to Study Smarter

Everyone can master metacognitive skills if they put enough time and effort into them. Consider these strategies that will help you to improve your metacognitive abilities.

• Reflect on your prior knowledge. Sitting back to refresh your knowledge in a world of constant informational flow is vital. For example, when attending a new course, check out the topics beforehand. Ask yourself what you know about them already. This will prepare you to absorb the new material better.
• Try various memorizing techniques. There are several memorization techniques, including mind maps, storytelling, mental imagery, and simply speaking out loud. Learning which approach works for you can save you time and enhance your long-term memory. Next time you’re preparing for a text, experiment with several memorization methods to find out which ones work for you.
• Review your tests and exam results. Reviewing your exams is a powerful tool to inspire your critical thinking. Spend some time analyzing your mistakes and consider why these questions were challenging for you. This way, you can detect your blind spots and avoid similar mistakes in the future.
• Do self-testing. Self-testing is a helpful technique to assess your knowledge before an exam. When self-testing becomes a part of your studying routine, you learn to identify what you know and don’t know. When preparing for an exam next time, try to solve some tasks yourself to gain more confidence in your knowledge.
• Figure out your learning style. Experts usually identify four types of learners: visual, auditory, kinesthetic, and reading or writing. It is essential to find the kind of learning that suits you best to make studying enjoyable. For example, if learning history with the help of textbooks doesn’t work for you, try watching some video lectures. Keep experimenting!
• Take regular breaks. When mastering your metacognitive skills, you might become absorbed in your learning process. However, staying concentrated and aware of what you’re trying to achieve is essential to learning a new skill. It is better to pause and reflect on what you’ve already achieved and what your next steps can be.
• Write down your thoughts. Whether you’re trying to learn new material or work on improving your metacognitive skills, always put your ideas on paper. This will help you put in order your thoughts and see connections you might not have noticed before. Moreover, this is a great way to track your personal growth.

👨‍🏫 How to Develop Metacognitive Skills – Final Tips

Metacognitive strategies are one of the most effective methods to improve your academic performance and make you stand out. Mastering metacognitive skills takes time and concentration, but they can significantly contribute to your learning and future success. Here are some tips to make metacognitive skills a part of your study routine.

• Think aloud when learning new material. Simply talk aloud to yourself or share what you find fascinating with your friends or classmates.
• Be observant. Pay close attention to people who inspire you and try to identify what traits made them successful.
• Experiment with your learning styles. Don’t be afraid to try new things even if you’ve studied with flashcards for your whole life. See what else works for you.
• Don’t be afraid to admit that you don’t know something. This will improve your self-awareness and spark your curiosity.
• Set goals, even minor ones. Consider what techniques you can apply to reach your goal and reflect on what you find challenging.

If you feel like you spend a lot of time studying, but all the hard work doesn’t improve your exam performance, this might indicate that you need to work on your metacognitive skills. The concept of “learning how to learn” can make studying enjoyable as well as boost your academic performance. The key to mastering metacognitive skills is asking self-reflective questions to encourage you to think deeper. Remember that it’s never too late to integrate metacognition into your studies because learning is a life-long journey!

• How Metacognition Boosts Learning | Edutopia
• Metacognitive Prompts To Help Students Reflect On Their Learning
• 5 Metacognitive Questions For Students Learning New Material | Edutopia
• Metacognitive Study Strategies | Learning Center
• Metacognitive Strategies (How People Learn) | Center for Teaching Innovation
• 50 Questions To Help Students Think About What They Think
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Metacognitive Development, Essay Example

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Educational Children’s Shows and Metacognitive Development

The children’s television show Dora the Explorer is an animated program that is intended to be both entertaining and educational. The show centers on the character of Dora, a young girl who is constantly seeking adventure by going to new and different places in each episode. The framework of the show allows the character to learn new things in every episode. In most episodes Dora faces some sort of challenge or obstacle as she attempts to reach her destination, and the character typically asks viewers to help her overcome these challenges. Children watching the show are encouraged to call out answers to the questions Dora asks, or to otherwise assist her in her quest. This engagement with the viewing audience provides an opportunity for metacognitive development in the children who watch the show.

The most basic definition of metacogniton is that it involves “thinking about thinking;” in other words it involves not just the process of thinking, but also the process of learning how to think and how to develop a greater capacity to think. A show such as Dora the Explorer does not just invite viewers to provide answers to specific questions; it also illustrates the fundamental concepts involved in problem solving. In this context it is not just the questions and answers are valuable to children in terms of learning about the specific ideas involved in each set of questions and answers; it is also emphasizes the larger processes involved in critical thinking. This presents opportunities for children to learn answers to questions, while also learning how to utilize problem-solving skills in a more general sense. Along with Dora the Explorer there are a wide variety of other children’s shows that support metacognitive development; one notable example is the show Sesame Street. Viewers are presented with new and different ideas in each episode, which also serves to support the processes of learning itself.

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Cognition And Metacognition Essay Examples

Type of paper: Essay

Topic: Cognitive Psychology , Information , Psychology , Education , Brain , Learning , Behavior , Students

Words: 2000

Published: 02/29/2020

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Introduction: An Overview

Cognition is a mental process used in acquiring knowledge, and how this knowledge is used. This encompasses features like judging, memory, sensation, and thinking. Hence, it shows how human beings get information, apply it in their daily living, and transform their lives accordingly. Cognition and the mind are closely interrelated since the mind processes information and how such information is used. Cognition can be classified into two types: conscious or unconscious; and natural or artificial (Lovett, 2013). Since time immemorial, it has been considered an abstract characteristic of living organisms and over the years been studied as a property of the brain. Under the subject of psychology, it shows mental performance and its state of intelligence as it applies to human beings. Furthermore, it is used in the field of artificial intelligence where high-automated machines are operated by using programmed or set conditions. Studies reveal that mental processes occur in human brain and there are factors that affect those mental processes. Research carried out recently has indicated solid concepts like concretization, abstraction and meta-reasoning. Cognitive psychology is also closely related to disciplines like philosophy, neuroscience, and linguistics (Bruning, Schraw & Ronning, 1999).

Interdisciplinary Approach

The central purpose of cognitive psychology is based on how people receive, process and store information. The vast applications of cognitive research include enhancing accuracy in decision-making, boosting memory and steering educational curricula to develop learning. Interdisciplinary learning features integration of multidisciplinary knowledge over a range a fundamental program subject matter or focus. The interdisciplinary approach towards cognitive psychology comprises of computer science, cognitive science, neuroscience, artificial intelligence, and philosophy. Its experimental nature has led to dependence on these other disciplines to provide for. Due to the experimental nature of cognitive psychology, there has been the reliance on these other disciplines to give simulations and experiments that are essential. These experiments give results, which are then compared directly to the already studied human behavior. Hence, this has contributed to three main categories namely experimental, computational, and neural cognitive psychology (Chick, Karis & Kernahan, 2009). Experimental cognitive psychology deals with the application of experimental methods in the study of behavior and the processes involved in it. Therefore, animal subjects and human participants are employed in the study of these topics: memory, neural substrates, emotion, development processes, sensation, and perception. Experimental methods are normally applied in the natural sciences as a way of exploring human cognition through a natural science. The complex human behavior together with mental processes, the ambiguous interpretation, and the unconscious processes by which they are subject to leads to an emphasis on proper methods within experimental psychology. Computational cognitive psychology involves the improvement of mathematical computational representations majorly of human cognition by employing dynamic systems enhanced through computer science related-techniques and methods. Computational neuroscience deals with the information systems of how the brain and neuro transduction of electrical current as important concepts. Computational psychology and neuroscience have largely led to the explanation of most of the mysteries in nature. It is closely associated with linguistics. The overlap happens naturally because psychologists are keen in the acquirement, utilization, and conservation of language. Neural cognitive psychology explores neuroscience methods that deal with models that assist in understanding the neural fundamentals about cognition in human beings. It gives a layout of the understanding of how the composition and working of the brain is related to definite psychological processes. It attempts to look at how the brain's mental processes are dependable on the bodies’ cognitive abilities in storing and producing new memories, recognize objects and people, enhance language, and increase our sense of reason and problem-solving.

Emergence of Cognitive Psychology as a Discipline

As a psychology discipline, cognitive psychology is involved in the assessment of mental processes. The cognitive revolution was an outcome of attempts by researchers to enhance theories leading to the mind on the fundamentals of computational measures and procedures. The important pillars of cognitive revolution was the incapability of behaviorism to differentiate between memory and performance; hence not able to account for complex learning in totality complex learning.

Decline of Behaviorism

Behaviorism encompasses the study of the theory that relates distinct behavior to recognizable objective stimulus, which is conditioned to functioning minus the recourse aimed to using internal mental processes. Through behaviorism, behavior is possibly studied without the use of any mental processes. Behaviorism caused the segregation of cognitive psychology. Not only that, but it was discredited because the last part of its definition denotes the lack of application of a mental processing groundwork which proves crucial in cognitive psychology. Furthermore, the decline of behaviorism has caused an incorporated outlook regarding cognitive psychology that assists in establishing experimental models which enhances studying; this is vital in identification of the use of mental processes, which are contrary to behaviorism, which is only achieved using behaviorism (Chick, Karis & Kernahan, 2009). Cognitive psychology is among the leading areas of study. It is also a diverse discipline of psychology since it involves a number of various categories of psychology. Among the few professions benefitting from cognitive psychology are academicians and the people involved in learning and comprehending children’s learning and development; also, enhancing curricula programs that are of advanced level of learning (Lovett, 2013). Common among such professions are architects, engineers and designers who try to understand how people reason and how information is mentally processed in the brain.

Cognition and Metacognition Interrelationship

Metacognition, as related to cognition, refers to any cognitive process or knowledge that controls or monitors cognition. The core interest here is the mention of the similarities occurring between metacognitive and executive control functions that correlate with how these processes are implemented in the brain of humans. Reviewing brain damaging studies show a circuitry of attentional networks involved in the said control processes, whose source is found in mid-frontal areas. These local points are normally active during error correction, emotional regulation, and conflict resolution. Looking at the developmental approach to the organization of the anatomy active in the executive control offers an internal perspective on how these mechanisms are affected by learning and maturation, and their relation to metacognitive activity (Bransford & Brown, 2000).

Teaching Metacognition as an Improvement to Learning

Though critically essential, metacognition has always been overlooked as an element of learning. This is because effective learning entails goal setting and planning, adapting, and monitoring of one’s advancement. By teaching students these skills, it is believed that student learning will considerably increase. Metacognition is directly related to self-efficacy. Self-efficacy refers to the measure of belief that an individual holds towards his or her own capability to complete tasks effectively and reach their goals. Academic success is seen in terms of the grades posted by students in their respective subject categories, which is reflected in their GPA. The leading question is whether there is a leading relationship between self-efficacy and academic success. Self-efficacy and metacognition have are closely interlinked, since each affect another (Bruning, Schraw & Ronning, 1999). This is because students in educational institutions are mostly related to this issue meaning they are the best examples to use for comparison.

A Case Study: Relationship between Metacognition and Self-Efficacy

This study reached out to 456 students but those who took part in the undertaking were 72. Not every student turned up for helping out in the study. Those who turned up are those who have specialized in the field of psychology and they were chosen to help in the research process. This is to make the researchers gather effective and enough information since the students involved would provide information that is reliable, as they have majored on the field. They were in this case helpful because it was not a hard task engaging them. They made everything easy and fast, as they were able to participate actively providing effective responses. These students were instructed to prepare self-reports on their performances as well as filling out some questionnaires online. They were required to fill out the questionnaires correctly and submit it through the internet as it was carried out online. The students were majorly Asians and Whites and filling out questionnaires was one of the most effective ways to gather information on such a research procedure. The other issue, which made it possible to gather sufficient information, is the fact that they were from both male and female genders. Carrying out research on the difference between self-efficacy and academic success required both genders so that the difference would clearly come out in relation to their performance. The other aspect that made it possible for researchers to gather effective information is the fact that they were from different years in the university (Bransford, John, Brown & Cocking, 2000). All these aspects were used to make certain that by the end of the study they achieve what they intended to. Several research findings have documented that males usually have high self-efficacy as compared to the females. In this case, the female gender ability is undermined by role of sex. There are labels in various cultures that view females as individuals who are not able to undertake some things and make the best out of their efforts. It is clear that many individuals discriminate against them and view the male gender as those who are so able. The other thing about discrimination against the female gender is the expectation that is hold for them. May people in the society hold low expectation for the females meaning they have it in mind that no matter what, the female gender will always not be as able as the male gender (Bransford & Brown, 2000). The judgment that their abilities are caused the accomplishment has effect on the result rather than their actual abilities. Those who believe in themselves have the chance to work under hard conditions and perform tasks that are hard and make it through. Those who view the tasks as hard lower their morale and are not able to work. Such self-drives are what determine the type of life an individual will live because it all starts with the individual. Generally, individuals with high self-efficacy always engage in hard tasks willingly while those with low self-efficacy do not. Academic success on the other had does not contribute to success in life because the level of education without commitment and high self-efficacy makes the individual in question not to succeed (Tanner, 2012). Experience is an important element in any field. People who have practiced in different fields for a long time, even if they did not have the right qualifications in the first place, will perform better than people who are fresh from colleges and universities. Instead of people preoccupying themselves with better they are well educated, they focus more on the purpose of education. In trying to understand the purpose of education, one is then able to focus beyond academic goals. Schools are not about intellectual development; education should focus more on bringing up people who are caring, competent, and loving people. Education should be viewed as a way of sustaining or creating a democratic society. It should not be viewed as platform for economic investment, and make corporate profits for organizations.

Referencees

Bruning, R. H., Schraw, G. J., & Ronning, R. R. (1999). Cognitive psychology and instruction (3rd ed.). Upper Saddle River, N.J.: Merrill. Bransford J.D., Brown A.L., and Cocking RR (2000). How people learn: Brain, mind, experience, and school. Washington, D.C.: National Academy Press. Tanner K.D. (2012). Promoting student metacognition. CBE—Life Sciences Education 11, 113-120. Bransford, John D., Brown Ann L., and Cocking Rodney R. (2000). How people learn: Brain, mind, experience, and school. Washington, D.C.: National Academy Press. Chick, N., Karis, T., & Kernahan, Cyndi. (2009). Learning from their own learning: how metacognitive and meta-affective reflections enhance learning in race-related courses. International Journal for the Scholarship of Teaching and Learning, 3(1). 1-28. Lovett, Marsha C. (2013). Make exams worth more than the grade. In Matthew Kaplan, Naomi Silver, Danielle LaVague-Manty, and Deborah Meizlish (Eds.), Using reflection and metacognition to improve student learning: Across the disciplines, across the academy. Sterling, VA: Stylus.

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