Metacognitive Self-assessment in Privilege and Equity – Part 1 Conceptualizing Privilege and its Consequences

by Rachel Watson, University of Wyoming
Ed Nuhfer, California State University (Retired)
Cinzia Cervato, Iowa State University
Ami Wangeline, Laramie County Community College

Demographics of metacognition and privilege

The Introduction to this series asserted that lives of privilege in the K-12 years confer relevant experiences advantageous to acquire the competence required for lifelong learning and entry into professions that require college degrees. Healthy self-efficacy is necessary to succeed in college. Such self-efficacy comes only after acquiring self-assessment accuracy through practice in using the relevant experiences for attuning the feelings of competence with demonstrable competence. We concur with Tarricone (2011) in her recognition of affect as an essential component of the self-assessment (or awareness) component of metacognition: the “‘feeling of knowing’ that accompanies problem-solving, the ability to distinguish ideas about which we are confident….”

A surprising finding from our paired measures is how closely the mean self-assessments of performance of groups of people track with their actual mean performances. According to the prevailing consensus of psychologists, mean self-assessments of knowledge are supposed to confirm that people, on average, overestimate their demonstrable knowledge. According to a few educators, self-reported knowledge is supposed to be just random noise with no meaningful relationship to demonstrable knowledge. Data published in 2016 and 2017 in Numeracy from two reliable, well-aligned instruments revealed that such is not the case. Our reports in Numeracy shared earlier on this blog (see Figures 2 and 3 at this link) confirm that people, on average, self-assess reasonably well.

In 2019, by employing the paired measures, we found that particular groups of peoples’ average competence varied measurably, and their average self-assessed competence closely tracked their demonstrable competence. In brief, different demographic groups, on average, not only performed differently but also felt differently about their performance, and their feelings were accurate.

Conceptualizing privilege and its consequences

Multiple systems (structural, attitudinal, institutional, economic, racial, cultural, etc.) produce privilege, and all individuals and groups experience privilege and disadvantage in some aspects of their lives. We visualize each system as a hierarchical continuum, along which at one end lie those systematically marginalized/minoritized, and those afforded the most advantages lie at the other. Because people live and work within multiple systems, each person likely operates at different positions along different continuums.

Those favored by privilege are often unaware of their part in maintaining a hierarchy that exerts its toll on those of lesser privilege. As part of our studies of the effects on those with different statuses of privilege, we discovered that instruments that can measure cognitive competence and self-assessments of their competence offer richer assessments than competency scores. They also inform us about how students feel and how accurately they self-assess their competence. Students’ histories of privilege seem to influence how effectively they can initially do the kinds of metacognition conducive to furthering intellectual development when they enter college.

Sometimes a group’s hierarchy results from a lopsided division into some criterion-based majority/minority split. There, advantages, benefits, status, and even acceptance, deference, and respect often become inequitably and systematically conferred by identity on the majority group but not on the underrepresented minority groups.

Being a minority can invite being marked as “inferior,” with an unwarranted majority negative bias toward the minority, presuming the latter have inferior cognitive competence and even lower capacity for feeling than the majority. Perpetual exposure to such bias can influence the minority group to doubt themselves and unjustifiably underestimate their competence and capacity to perform. By employing paired measures, Wirth et al. (2021, p. 152 Figs.6.7 & 6.8) found recently that undergraduate women, who are the less represented binary gender in science, consistently underestimated their actual abilities relative to men (the majority) in science literacy.

We found that in the majority ethnic group (white Caucasians), both binary genders, on average, significantly outperformed their counterparts in the minority group (all other self-identified ethnicities combined) in both the competence scores of science literacy and the mean self-assessed competency ratings (Figure 1).

Figure 1. Graph of gender performance on measures of self-assessed competence ratings and demonstrated competence scores across ethnic majority/minority categories. This graph represents ten years of data collection of paired measures, but we only recently began to collect non-binary gender data within the last year, so this group is sparsely represented. Horizontal colored lines coded to the colored circles’ legend mark the positions of the means of scores and ratings in percent at the 95% confidence level.

Notably, in Figure 1, the non-binary gender groups, majority or minority, were the strongest academic group of the three gender categories based on SLCI scores. Still, relative to their performance, the non-binary groups felt that they performed less well than they actually did.

On a different SLCI dataset with a survey item on sexual preference rather than gender, researcher Kali Nicholas Moon (2018) found the same degree of diminished self-assessed competence relative to demonstrated competence for the small LGBT group (see Fig. 7 p. 24 of this link). Simply being a minority may predispose a group to doubt their competence, even if they “know their stuff” better than most.

These mean differences in performance shown in Figure 1 are immense. For perspective, pre-post measures in a GE college course or two in science rarely produce more than mean differences of more than a couple of percentage points on the SLCI. In both majority and minority groups, females, on average, underestimated their performance, whereas males overestimated theirs.

If a group receives constant messages that their thinking may be inferior, it is hardly surprising that they internalize feelings of inferiority that are damaging. Our evidence above from several groups verifies such a tendency. We showed that lower feelings of competence parallel significant deficit performance on a test of understanding science, an area relevant to achieving intellectual growth and meeting academic aspirations. Whether this signifies a general tendency of underconfidence in minority groups for meeting their aspirations in other areas remains undetermined.

Perpetuating privilege in higher education

Academe nurtures many hierarchies. Across institutions, “Best Colleges” rating lists perpetuate a myth that institutions that make the list are, in all ways, for all students “better than” those not on the list. Some state institutions actively promote a “flagship reputation,” implying the state’s other schools as “inferior.” Being in a community of peers that reinforces such hierarchical valuing confers damaging messaging of inferiority to those attending the “inferior” institutions, much as an ethnic majority casts negative messages to the minority.

Within institutions, different disciplines are valued differently, and people experience differential privileges across the departments and programs that focus on educating to support different disciplines. The degrees of consequences of stress, alienation, and physical endangerment are minor compared to those experienced by socially marginalized/minoritized groups. Nevertheless, advocating for any change in an established hierarchy in any community is perceived as disruptive by some and can provide consequences of diminished privilege. National communities of academic research often prove no exception.

Takeaways

Hierarchies usually define privilege, and the majority group often supports hierarchies detrimental to the well-being of minority groups. Although test scores are the prevalent measures used to measure learning mastery, paired measures of cognitive competence and self-assessed competence provide additional information about students’ affective feelings about content mastery and their developing capacity for accurate self-assessment. This information helps reveal the inequity across groups and monitors how well students can employ the higher education environment for advancing their understanding of specialty content and understanding of self. Paired measures confirm that groups of varied privilege fare differently in employing that environment for meeting their aspirations.

Understanding Bias in the Disciplines: Part 2 – the Physical and Quantitative Sciences

by Ed Nuhfer, California State University (Retired)
Eric Gaze, Bowdoin College
Paul Walter, St Edwards University
Simone Mcknight (Simone Erchov), Global Systems Technology

In Part 1, we summarized psychologists’ current understanding of bias. In Part 2, we connect conceptual reasoning and metacognition and show how bias challenges clear reasoning even in “objective” fields like science and math.

Science as conceptual

College catalogs’ explanations of general education (GE) requirements almost universally indicate that the desired learning outcome of the required introductory science course is to produce a conceptual understanding of the nature of science and how it operates. Focusing only on learning disciplinary content in GE courses squeezes out stakeholders’ awareness that a unifying outcome even exists.

Wherever a GE metadisciplinary requirement (for example, science) specifies a choice of a course from among the metadiscipline’s different content disciplines (for example, biology, chemistry, physics, geology), each course must communicate an understanding of the way of knowing established in the metadiscipline. That outcome is what the various content disciplines share in common. A student can then understand how different courses emphasizing different content can effectively teach the same GE outcome.

The guest editor led a team of ten investigators from four institutions and separate science disciplines (biology, chemistry, environmental science, geology, geography, and physics). Their original proposal was to investigate ways to improve the learning in the GE science courses. While articulating what they held in common as professing the metadiscipline of “science,” the investigators soon recognized that the GE courses they took as students had focused on disciplinary content but scarcely used that content to develop an understanding of science as a way of knowing. After confronting the issue of teaching with such a unifying emphasis, they later turned to the problem of assessing success in producing this different kind of understanding.

Upon discovering no suitable off-the-shelf assessment instrument to meet this need, they constructed the Science Literacy Concept Inventory (SLCI). This instrument later made possible this guest-edited series and the confirmation of knowledge surveys as valid assessments of student learning.

Concept inventories test understanding the concepts that are the supporting framework for larger overarching blocks of knowledge or thematic ways of thinking or doing. The SLCI tests nine concepts specific to science and three more related to the practice of science and connecting science’s way of knowing with contributions from other requisite GE metadisciplines.

Self-assessment’s essential role in becoming educated

Self-assessment is partly cognitive (the knowledge one has) and partly affective (what one feels about the sufficiency of that knowledge to address a present challenge). Self-assessment accuracy confirms how well a person can align both when confronting a challenge.

Developing good self-assessment accuracy begins with an awareness that having a deeper understanding starts to feel different from merely having surface knowledge needed to pass a multiple-choice test. The ability to accurately feel when deep learning has occurred reveals to the individual when sufficient preparation for a challenge has, in fact, been achieved. We can increase learners’ capacity for metacognition by requiring frequent self-assessments that give them the practice needed to develop self-assessment accuracy. No place needs teaching such metacognition more than the introductory GE courses.

Regarding our example of science, the 25 items on the SLCI that test understanding of the twelve concepts derive from actual cases and events in science. Their connection to bias lies in learning that when things go wrong when doing or learning science, some concept is unconsciously being ignored or violated. Violations are often traceable to bias that hijacked the ability to use available evidence.

We often say: “Metacognition is thinking about thinking.” When encountering science, we seek to teach students to “think about” (1) “What am I feeling that I want to be true and why do I have that feeling?” and (2) “When I encounter a scientific topic in popular media, can I articulate what concept of science’s way of knowing was involved in creating the knowledge addressed in the article?”

Examples of bias in physical science

“Misconceptions research” constitutes a block of science education scholarship. Schools do not teach the misconceptions. Instead, people develop preferred explanations for the physical world from conversations that mostly occur in pre-college years. One such explanation addresses why summers are warm and winters are cold. The explanation that Earth is closer to the sun in summer is common and acquired by hearing it as a child. The explanation is affectively comfortable because it is easy, with the ease coming from repeatedly using the neural network that contains the explanation to explain the seasonal temperatures we experience. We eventually come to believe that it is true. However, it is not true. It is a misconception.

When a misconception becomes ingrained in our brain neurology over many years of repeated use, we cannot easily break our habit of invoking the neural network that holds the misconception until we can bypass it by constructing a new network that holds the correct explanation. Still, the latter will not yield a network that is more comfortable to invoke until usage sufficiently ingrains it. Our bias tendency is to invoke the most ingrained explanation because doing so is easy.

Even when individuals learn better, they often revert to invoking the older, ingrained misconception. After physicists developed the Force Concept Inventory (FCI) to assess students’ understanding of conceptual relationships about force and motion, they discovered that GE physics courses only temporarily dislodged students’ misconceptions. Many students soon reverted to invoking their previous misconceptions. The same investigators revolutionized physics education by confirming that active learning instruction better promoted overcoming misconceptions than did traditional lecturing.

The pedagogy that succeeds seemingly activates a more extensive neural network (through interactive discussing, individual and team work on problem challenges, writing, visualizing through drawing, etc.) than was activated to initially install the misconception (learning it through a brief encounter).

Biases that add wanting to believe something as true or untrue are especially difficult to dislodge. An example of the power of bias with emotional attachment comes from geoscience.

Nearly all school children in America today are familiar with the plate tectonics model, moving continents, and ephemeral ocean basins. Yet, few realize that the central ideas of plate tectonics once were scorned as “Germanic pseudoscience” in the United States. That happened because a few prominent American geoscientists so much wanted to believe their established explanations as true that their affect hijacked these experts’ ability to perceive the evidence. These geoscientists also exercised enough influence in the U. S. to keep plate tectonics out of American introductory level textbooks. American universities introduced plate tectonics in introductory GE courses only years later than did Europe.

Example of Bias in Quantitative Reasoning

People usually cite mathematics as the most dispassionate discipline and the least likely for bias to corrupt. However, researchers Dan Kahan and colleagues demonstrated that bias also disrupts peoples’ ability to use quantitative data and think clearly.

Researchers asked participants to resolve whether a skin cream effectively treated a skin rash. Participants received data for subjects who did or did not use the skin cream. Among users, the rash got better in 223 cases and got worse in 75 cases. Of subjects who did not use the skin cream, the rash got better in 107 cases and worse in 21 cases.

Participants then used the data to select from two choices: (A) People who used the cream were more likely to get better or (B) People who used the cream were more likely to get worse. More than half of the participants (59%) selected the answer not supported by the data. This query was primarily a numeracy test in deducing the meaning of numbers.

Then, using the same numbers, the researchers added affective bait. They replaced the skin cream query with a query about the effects of gun control on crime in two cities. One city allowed concealed gun carry, and another banned concealed gun carry. Participants had to decide whether the data showed that concealed carry bans increased or decreased crime.

Self-identified conservative Republicans and liberal Democrats responded with a desire to believe acquired from their party affiliations. The result was even more erroneous than the skin cream case participants. Republicans greatly overestimated increased crime from gun bans, but no more than Democrats overestimated decreased crime from gun bans (Figure 1). When operating from “my-side” bias planted by either party, citizens significantly lost their ability to think critically and use numerical evidence. This was true whether the self-identified partisans had low or high numeracy skills.

Figure 1. Effect of bias on interpreting simple quantitative information (from Kahan et al. 2013, Fig. 8). Numerical data needed to answer whether a cream effectively treated a rash triggered low bias responses. When researchers employed the same data to determine whether gun control effectively changed crime, polarizing emotions triggered by partisanship significantly subverted the use of evidence toward what one wanted to believe.

Takeaway

Decisions and conclusions that appear based on solely objective data rarely are. Increasing metacognitive capacity produces awareness of the prevalence of bias.

Understanding Bias in the Disciplines: Part 1 – the Behavioral Sciences

by Simone Mcknight (Simone Erchov), Global Systems Technology
Ed Nuhfer, California State University (Retired)
Eric Gaze, Bowdoin College
Paul Walter, St Edwards University

Bias as conceptual

Bias arises from human brain mechanisms that process information in ways that make decision-making quicker and more efficient at the cognitive/neural level. Bias is an innate human survival mechanism, and we all employ it.

Bias is a widely known and commonly understood psychological construct. The common understanding of bias is “an inclination or predisposition for or against something.” People recognize bias by its outcome—the preference to accept specific explanations or attributions as true.

In everyday conversation, discussions about bias occur in preferences and notions people have on various topics. For example, people know that biases may influence the development of prejudice (e.g., ageism, sexism, racism, tribalism, nationalism), political, or religious beliefs.

A deeper look reveals that some of these preferences are unconscious. Nevertheless, they derive from a related process called cognitive bias, a propensity to use preferential reasoning to assess objective data in a biased way. This entry introduces the concept of bias, provides an example from the behavioral sciences, and explains why metacognition can be a valuable tool to counteract bias. In Part 2, which follows this entry, we provide further examples from hard science, field science, and mathematics.

Where bias comes from

Biases develop from the mechanisms by which the human brain processes information as efficiently as possible. These unconscious and automatic mechanisms make decision-making more efficient at the cognitive/neural level. Most mechanisms that help the human brain make fast decisions are credited to adaptive survival. Like other survival mechanisms, bias loses value and can be a detriment in a modern civilized world where threats to our survival are infrequent challenges. Cognitive biases are subconscious errors in thinking that lead to misinterpreting future information from the environment. These errors, in turn, impact the rationality and accuracy of decisions and judgments.

When we frame unconscious bias within the context of cognitive bias and survival, it is easier to understand how all of us have inclinations to employ bias and why any discipline that humans manage is subject to bias. Knowing this makes it easier to account for the frequent biases affecting the understanding and interpreting of diverse kinds of data.

People easily believe that bias only exists in “subjective” disciplines or contexts where opinions and beliefs seem to guide decisions and behavior. However, bias manifests in how humans process information at the cognitive level. Although it is easier to understand bias as a subjective tendency, the typical way we process information means that bias can pervade all of our cognition.

Intuitively, disciplines relying on tangible evidence, logical arguments, and natural laws of the physical universe would seem factually based and less influenced by feelings and opinion. After all, “objective disciplines” do not predicate their findings on beliefs about what “should be.” Instead, they measure tangible entities and gather data. However, even in the “hard science” disciplines, the development of a research question, the data collected, and the interpretations of data are vulnerable to bias. Tangible entities such as matter and energy are subject to biases as simple as differences in perception of the measured readings on the same instrument. In the behavioral sciences, where investigative findings are not constrained by natural law, bias can be even harder to detect. Thus, all scientists carry bias into their practice of science, and students carry bias into their learning of it.

Metacognition can help counter our tendencies toward bias because it involves bringing relevant information about a process (e.g., conducting research, learning, or teaching) into awareness and then using that awareness to guide subsequent behaviors.

Consequences of bias

Bias impacts individual understanding of the world, the self, and how the self navigates the world – our schemas. These perceptions may impact elements of identity or characterological elements that influence the likelihood of behaving in one way versus another.

Bias should be assumed as a potentially influential factor in any human endeavor. Sometimes bias develops for an explanation after hearing it in childhood and then invoking that explanation for years. Even after seeing the evidence against that bias, our initial explanations are difficult to replace with ones better supported by evidence because we remain anchored to that initial knowledge. Adding a personal emotional attachment to an erroneous explanation makes replacing it even more difficult. Scientists can have emotional attachments to particular explanations of phenomena, especially their own explanations. Then, it becomes easy to selectively block out or undervalue evidence that modifies or contradicts the favored explanation (also known as confirmation bias).

Self-assessment, an example of long-standing bias in behavioral science

As noted in the introduction, this blog series focuses on our team’s work related to self-assessment. Our findings countered results from scores of researchers who replicated and verified the testing done in a seminal paper by Kruger and Dunning (1999). Their research asserted that most people were overconfident about their abilities, and the least competent people had the most overly optimistic perceptions of their competence. Researchers later named the phenomenon the “Dunning-Kruger effect,” and the public frequently deployed “the effect” as a label to disparage targeted groups as incompetent. “The effect” held attraction because it seemed logical that people who lacked competence also lacked the skills needed to recognize their deficits. Quite simply, people wanted to believe it, and replication created a consensus with high confidence in concluding that people, in general, cannot accurately self-assess.

While a few researchers did warn about likely weaknesses in the seminal paper, most behavioral scientists selectively ignored the warnings and repeatedly employed the original methodology. This trend of replication continued in peer-reviewed behavioral science publications through at least 2021.

Fortunately, the robust information storage and retrieval system that characterizes the metadiscipline of science (which is a characteristic distinguishing science from technology as ways of knowing) makes it possible to challenge a bias established in one discipline by researchers from another. Through publications and open-access databases, the arguments that challenge an established bias then become available. In this case, the validity of “the effect” resided mainly in mathematical arguments and not, as presumed, arguments that resided solely within the expertise of behavioral scientists.

No mathematics journal had ever hosted arguments addressing the numeracy of arguments that established and perpetuated the belief in “the effect.” However, mathematics journals offered the benefit of reviewers who specialized in quantitative reasoning and were not emotionally attached to any consensus established in behavioral science journals. These reviewers agreed that the long-standing arguments for supporting the Dunning-Kruger effect were mathematically flawed.

In 2016 and 2017, Numeracy published two articles from our group that detailed the mathematical arguments that established the Dunning-Kruger effect conclusions and why these arguments are untenable. When examined by methods the mathematics reviewers verified as valid, our data indicated that people were generally good at self-assessing their competence and confirmed that there were no marked tendencies toward overconfidence. Experts and novices proved as likely to underestimate their abilities as to overestimate them. Further, the percentage of those who egregiously overestimated their abilities was small, in the range of about 5% to 6% of participants. However, our findings confirmed a vital conclusion of Kruger and Dunning (1999): experts self-assess better than novices (variance decreases as expertise increases), and self-assessment accuracy is attainable through training and practice.

By 2021, the information released in Numeracy began to penetrate the behavioral science journals. This blog series, our earlier posts on this site, and archived presentations to various audiences (e.g., the National Numeracy Network, the Geological Society of America) further broadened awareness of our findings.

Interim takeaways

Humans construct their learning from mentally processing life experiences. During such processing, we simultaneously construct some misconceptions and biases. The habit of drawing on a misconception or bias to explain phenomena ingrains it and makes it difficult to replace with correct reasoning. Affective attachments to any bias make overcoming the bias extremely challenging, even for the most accomplished scholars.

It is essential to realize that we can reduce bias by employing metacognition to recognize bias originating from within us at the individual level and by considering bias that influences us but is originated from or encouraged by groups. In the case above, we were able to explain the bias within the Behavioral Sciences disciplines by showing how repeatedly mistaking mathematical artifacts as products of human behavior produced a consensus that held understanding self-assessment captive for over two decades.

Metacognitive self-assessment seems necessary for initially knowing self and later for recognizing one’s own personal biases. Self-assessment accuracy is valuable in using available evidence well and reducing the opportunity for bias to hijack our ability to reason. Developing better self-assessment accuracy appears to be a very worthy objective of becoming educated.

Introduction: Why self-assessment matters and how we determined its validity

By Ed Nuhfer, Guest Editor, California State University (retired)

There are few exercises of thinking more metacognitive than self-assessment. For over twenty years, behavioral scientists accepted that the “Dunning-Kruger effect,” which portrays most people as “unskilled and unaware of it,” correctly described the general nature of human self-assessment. Only people with significant expertise in a topic were capable of self-assessing themselves accurately, while those with the least expertise supposedly held highly overinflated views of their abilities.

The authors of this guest series have engaged in a collaborative effort to understand self-assessment for over a decade. They documented how the “Dunning-Kruger effect,” from its start, rested on specious mathematical arguments. Unlike what the “effect” asserts, most people do not hold overly inflated views of their competence, regardless of their level of expertise. We summarized some of our peer-reviewed work in earlier articles in “Improve with Metacognition (IwM).” These are discoverable by using “Dunning-Kruger effect” in IwM’s search window.

Confirming that people, in general, are capable of self-assessing their competence affirms the validity of self-assessment measures. The measures inform efforts in guiding students to improve their self-assessment accuracy.

This introduction presents commonalities that unify the series’ entries to follow. In the entries, we hotlink the references available as open-source within the blogs’ text and place all other references cited at the end.

Why self-assessment matters

After an educator becomes aware of metacognition’s importance, teaching practice should evolve beyond finding the best pedagogical techniques for teaching content and assessing student learning. The “place beyond” focuses on teaching the student how to develop a personal association with content as a basis for understanding self and exercising higher-order thinking. Capturing the changes in developing content expertise together with self in a written teaching/learning philosophy expedites understanding how to achieve both. Self-assessment could be the most valuable of all the varieties of metacognition that we employ to deepen our understanding.

Visualization is conducive to connecting essential themes in this series of blogs that stress becoming better educated through self-assessment. Figure 1 depicts the role and value of self-assessment from birth at the top of the figure to becoming a competent, autonomous lifelong learner by graduation from college at the bottom.

Figure 1. Relationship of self-assessment to developing self-regulation in learning.

Let us walk through this figure, beginning with early life Stage #1 at the top. This stage occurs throughout the K-12 years, when our home, local communities, and schools provide the opportunities for choices and efforts that lead to experiences that prepare us to learn. In studies of Stage 1, John A. Ross made the vital distinction between self-assessment (estimating immediate competence to meet a challenge) and self-efficacy (perceiving one’s personal capacity to acquire competence through future learning). Developing healthy self-efficacy requires considerable practice in self-assessment to develop consistent self-assessment accuracy.

Stage 1 is a time that confers much inequity of privilege. Growing up in a home with a college-educated parent, attending schools that support rich opportunities taught in one’s native language, and living in a community of peers from homes of the well-educated provide choices, opportunities, and experiences relevant to preparing for higher education. Over 17 or 18 years, these relevant self-assessments sum to significant advantages for those living in privilege when they enter college.

However, these early-stage self-assessments occur by chance. The one-directional black arrows through Stage 2 communicate that nearly all the self-assessments are occurring without any intentional feedback from a mentor to deliberately improve self-assessment accuracy. Sadly, this state of non-feedback continues for nearly all students experiencing college-level learning too. Thereby, higher education largely fails to mitigate the inequities of being raised in a privileged environment.

The red two-directional arrows at Stage 3 begin what the guest editor and authors of this series advocate as a very different kind of educating to that commonly practiced in American institutions of education. We believe education could and should provide self-assessments by design, hundreds in each course, all followed by prompt feedback, to utilize the disciplinary content for intentionally improving self-assessment accuracy. Prompt feedback begins to allow the internal calibration needed for improving self-assessment accuracy (Stage #4).

One reason to deliberately incorporate self-assessment practice and feedback is to educate for social justice. Our work indicates that we can enable the healthy self-efficacy needed to succeed in the kinds of thinking and professions that require a college education by strengthening the self-assessment accuracy of students and thus make up for the lack of years of accumulated relevant self-assessments in the backgrounds of those lesser privileged.

By encouraging attention to self-assessment accuracy, we seek to develop students’ felt awareness of surface learning changing toward the higher competence characterized by deep understanding (Stage #5). Awareness of the feeling characteristic when one attains the competence of deep understanding enables better judgment for when one has adequately prepared for a test or produced an assignment of high quality and ready for submission.

People attain Stage #6, self-regulation, when they understand how they learn, can articulate it, and can begin to coach others on how to learn through effort, using available resources, and accurately doing self-assessment. At that stage, a person has not only developed the capacity for lifelong learning, but has developed the capacity to spread good habits of mind by mentoring others. Thus the arrows on each side of Figure 1 lead back to the top and signify both the reflection needed to realize how one’s privileges were relevant to their learning success and cycling that awareness to a younger generation in home, school, and community.

A critical point to recognize is that programs that do not develop students’ self-assessment accuracy are less likely to produce graduates with healthy self-efficacy or the capacity for lifelong learning than programs that do. We should not just be training people to grow in content skills and expertise but also educating them to grow in knowing themselves. The authors of this series have engaged for years in designing and doing such educating.

The common basis of investigations

The aspirations expressed above have a basis in hard data from assessing the science literacy of over 30,000 students and “paired measures” on about 9,000 students with peer-reviewed validated instruments. These paired measures allowed us to compare self-assessed competence ratings on a task and actual performance measures of competence on that same task.

Knowledge surveys serve as the primary tool through which we can give “…self-assessments by design, hundreds in each course all followed by prompt feedback.” Well-designed knowledge surveys develop each concept with detailed challenges that align well with the assessment of actual mastery of the concept. Ratings (measures of self-assessed competence) expressed on knowledge surveys, and scores (measures of demonstrated competence) expressed on tests and assignments are scaled from 0 to 100 percentage points and are directly comparable.

When the difference between the paired measures is zero, there is zero error in self-assessment. When the difference (self-assessed minus demonstrated) is a positive number, the participant tends toward overconfidence. When the difference is negative, the participant has a tendency toward under-confidence.

In our studies that established the validity of self-assessment, our demonstrated competence data in our paired measures came mainly from the validated instrument, the Science Literacy Concept Inventory or “SLCI.” Our self-assessed competence data comes from knowledge surveys and global single-queries tightly aligned with the SLCI. Our team members incorporate self-created knowledge surveys of course content into their higher education courses. Knowledge surveys have proven to be powerful research tools and classroom tools for developing self-assessment accuracy.

Summary overview of this blog series

IwM is one of the few places where the connection between bias and metacognition has directly been addressed (e.g., see a fine entry by Dana Melone). The initial two entries of this series will address metacognitive self-assessment’s relation to the concept of bias.

Later contributions to this series consider privilege and understanding the roles of affect, self-assessment, and metacognition when educating to mitigate the disadvantages of lesser privilege. Other entries will explore the connection between self-assessment, participant use of feedback, mindset, and metacognition’s role in supporting the development of a growth mindset. Near the end of this series, we will address knowledge surveys, the instruments that incorporate the disciplinary content of any college course to improve learning and develop self-assessment accuracy.

We will conclude with a final wrap-up entry of this series to aid readers’ awareness that what students should “think about” when they “think about thinking” ought to provide a map for reaching a deeper understanding of what it means to become educated and to acquire the capacity for lifelong learning.

The College Transition: Making Time Tangible

by Mary L. Hebert, PhD; Director, Regional Center for Learning Disabilities; Fairleigh Dickinson University

In preparing students for the college transition, it behooves them to reflect on the differences between high school and college. Important considerations for reflection include questions related to the difference of the pace and volume of work, and the degree of independence required for that work. Students with learning differences are statistically more at risk of challenge and adjustment issues and consequently the incompletion of college. The more metacognitively they enter their new academic environment, the greater the likelihood they will be prepared, build upon their self-efficacy and self-advocacy. Using metacognition as a tool to pause, reflect, and pivot accordingly has the potential to optimize capacity to adapt and adjust to the context of one’s learning environment.

Making Time Tangible

Executive function issues can have a significant impact on college students. Many factors can contribute to this. For students who have a learning disability, high co-morbidity rates are noted in the literature (Mohammadi et al., 2019). The executive function skill sets are some of the most critical to manage the rigor and independence of the adult learning experience. A student learning in an adult context are often adjusting to a living and learning environment on a college campus for the first time. Common symptoms of executive function challenges include a distorted sense of time, procrastination, difficulty engaging and disengaging in tasks, and cognitive shifts in task management. The more tangible and observable time can be made, the greater the likelihood of manipulating time and advantageously managing it towards the achievement of one’s immediate, short term and longer term goals.

It takes a synthesis of academic, social, and emotional skill sets to operate collaboratively during a time of transition. In work with new students, it is prudent to encourage and sharpen metacognitive reflection on the process of recognizing time as something that is tangible and malleable and now on the student to manipulate accordingly to accommodate their new adult learning environment. Enriched self-awareness of one’s challenges as well as strengths in regard to executive function, has the potential to support enriched self-competence. Both are cornerstones for success.

**Reflect and plan: tackle time management, don’t let it tackle you!**

One of the metacognitive tasks that a supportive adult can encourage when a student prepares for the college transition is to create a weekly schedule with their courses listed on the schedule. Likely, the student will observe that there is far more white space than ‘ink on the page’ or black space. I tell the student that I am far less concerned about the ink on the page. Why they ask? Because the ink on the page very nicely identifies where they have to be, for what and with whom. I ask students what they notice about their schedule in comparison to their high school schedule, which is often structured from 7:00 am until 3:00 pm, or even later, given extracurricular commitments and homework. Next, I ask students to identify and list not only academic commitments but study time, wellness hygiene tasks (eating, sleeping, doctor’s appointments, exercise), social time, and other responsibilities and suggest plotting how many hours these will take during the 24 hours day.

It becomes evident during this task that college success is highly dependent on the use of the white space. Academic coaching has become a popular and sought out experience. In fact, embracing a coaching experience correlates with a higher GPA, retention and success for students (Capstick et al. 2019). While academic coaching has the potential to offset executive function challenges and is excellent to have available, ultimately the goal is internalization of metacognitive skills that support more independent and effective executive function. Consequently, the coaching model should focus on internalization as the goal.

Executive function skills are essential to sustain motivation and support perseverance in academics, particularly for students with a learning difference. If executive function skills are challenged and the student does not possess adequate focus, stamina, and organization, there is potential for impact on academic performance. This can increase risk for poor grades and low self-efficacy, and have the potential to compromise the completion of academic tasks. Metacognition facilitates success through promoting self-awareness of one’s executive skill profile of strengths and challenges, and then using that awareness to promote self-monitoring and checking in on one’s task management.

Making time tangible is a powerful strategy in managing executive function symptoms. Metacognitive reflection of the college schedule is a power-tool to support college students who now are in the driver’s seat of managing time rather than being a passenger with others who have managed it for them. The internalization of this skill will be essential to the successful navigation of the ‘white space.’

This added layer of independence and competence will lead to a position of empowerment in the transition to college and be a skill set necessary for career readiness.

References

Capstick, M.K., Harrell-Williams, L.M., Cockrum, C.D. et al. Exploring the Effectiveness of Academic Coaching for Academically At-Risk College Students. Innov High Educ 44, 219–231 (2019). https://doi.org/10.1007/s10755-019-9459-1

Mohammadi M-R, Zarafshan H, Khaleghi A, et al. Prevalence of ADHD and Its Comorbidities in a Population-Based Sample. Journal of Attention Disorders. 2021;25(8):1058-1067. doi:10.1177/1087054719886372

Writing metacognitive learning objectives for metacognitive training that supports student learning

by Patrick Cunningham, Ph.D., Rose-Hulman Institute of Technology

Teaching through the COVID-19 pandemic has highlighted disparities in how students approach their learning. Some have continued to excel with hybrid and online instruction while others, and more than usual, have struggled. Compounding these struggles, these students also find themselves behind or with notable gaps in their prerequisite knowledge for following courses. A significant component of these struggles may be due to not having developed independence in their learning. Engaging in explicit metacognitive activities directly addresses this disparity, improving students’ abilities to overcome these struggles. Given the present challenges of living through COVID-19, this is more important now than ever. However, creating activities with metacognitive focus is likely unfamiliar and there are not a lot of resources to guide their development. Here I seek to demonstrate an accessible approach, an entry point, for supporting students’ growth as more skillful and independent learners grounded in metacognition.

Cognitive Learning Objectives are Just the Start

Creating explicit learning objective is one means by which educators commonly try to support students’ independence in learning. Typically learning objectives focus on the cognitive domain, often based on Bloom’s Taxonomy. The cognitive domain refers to how we think about or process information. Bloom’s taxonomy for the cognitive domain is comprised of Remembering, Understanding, Applying, Analyzing, Evaluating, and Creating (Krathwohl, 2002). Each of these gives an indication how a student is expected to engage or use the material we are teaching. For constructing learning objectives, there are lists of action verbs associated with each Bloom category.

Consider this cognitive learning objective for a computer programming course.

Students will be able to create and implement functions with inputs and an output in C++ programs to accomplish a specified task on an Arduino board with a prewired circuit.

This learning objective is specific to a lesson and targets the Apply level of Bloom’s taxonomy. (The approach I am presenting could equally apply to broader course-level learning objectives, but I think the specificity here makes the example more tangible.) This objective uses good action verbs (bolded) and has a prescribed scope and context. But is it adequate for guiding student learning if they are struggling with it?

Metacognitive Learning Objectives can Direct Learning Activities

Cognitive learning objectives point students to what they should be able to do with the information but do not usually provide guidance for how they should go about developing their ability to do so. Metacognition illuminates the path to developing our cognitive abilities. As a result, metacognitive training can support students’ attainment of cognitive learning objectives. Such training requires metacognitive learning objectives.

Metacognitive learning objectives focus on our awareness of the different ways we process information and how we regulate and refine how we process information. Metacognitive knowledge includes knowledge of how people (and we as individuals) process information, strategies for processing information and monitoring our thinking, and knowledge of the cognitive demands of specific tasks (Cunningham, et al., 2017). As we engage in learning we draw on this knowledge and regulate our thinking processes by planning our engagement, monitoring our progress and processes, adjusting or controlling our approaches, and evaluating the learning experience (Cunningham, et al., 2017). Metacognitive monitoring and evaluation feed back into our metacognitive knowledge, reinforcing, revising, or adding to it.

Example Implementation of Metacognitive Learning Objectives

Considering our example cognitive learning objective, how could we focus metacognitive training to support student attainment of it? Two possibilities include 1) focusing on improving students’ metacognitive knowledge of strategies to practice and build proficiency with writing functions or 2) supporting students’ accurate self-assessment of their ability to demonstrate this skill. Instructors can use their knowledge of their students’ current strategies to decide which approach (or both) to take. For example, if it appears that most students are employing limited learning strategies, such as memorizing examples by reviewing notes and homework, I might focus on teaching students about a wider range of effective learning strategies. The associated metacognitive learning objective could be:

Students will select and implement at least two different elaborative learning strategies and provide a rationale for how they support greater fluency with functions.

The instructional module could differentiate categories of learning objectives (e.g., memorization, elaboration, and organization), demonstrate a few examples, and provide a more complete list of elaborative learning strategies (Seli & Dembo, 2019). Then students could pick one to do in class and one to do as homework. If, on the other hand, it appears that most students are struggling to self-assess their level of understanding, I might focus on teaching students how to better monitor their learning. The associated metacognitive learning objective could be:

Students will compare their function written for a specific application, and completed without supports, to a model solution, using this as evidence to defend and calibrate their learning self-assessment.

Here the instructional module could be a prompt for students to create and implement a function, from scratch without using notes or previously written code. After completing their solutions, students would be given access to model solutions. In comparing their solution to the model, they could note similarities, differences, and errors. Then students could explain their self-assessment of their level of understanding to a neighbor or in a short paragraph using the specific comparisons for evidence. These examples are metacognitive because they require students to intentionally think about and make choices about their learning and to articulate their rationale and assessment of the impact on their learning. I believe it is important to be explicit with students about the metacognitive aim – to help them become more skillful learners. This promotes transfer to other learning activities within the class and to their learning in other classes.

Implementing and Supporting Your Metacognitive Outcomes

In summary, to create actionable metacognitive learning objectives I recommend,

clarifying the cognitive learning objective(s) you aim to support
investigating and collecting evidence for what aspect(s) of learning students are struggling with
connecting the struggle(s) to elements of metacognition
drafting a metacognitive learning objective(s) that address the struggle(s)

Armed with your metacognitive learning objectives you can then craft metacognitive training to implement and assess them. Share them with a colleague or someone from your institution’s teaching and learning center to further refine them. You may want to explore further resources on metacognition and learning such as Nilson’s (2013) Creating Self-Regulated Learners, Seli and Dembo’s (2019) Motivation and learning strategies for college success, and Svinicki’s GAMES© survey in (Svinicki, 2004). Or you could watch my Skillful Learning YouTube video, What is Metacognition and Why Should I Care?.

If metacognition is less familiar to you, avoid overwhelm by choosing one element of metacognition at a time. For example, beyond the above examples, you could focus on metacognitive planning to support students better navigating an open-ended project. Or you could help students better articulate what it means to learn something or experience the myth of multitasking (we are task switchers), which are elements pertaining to metacognitive knowledge of how people process knowledge. Learn about that element of metacognition, develop a metacognitive learning objective for it, create the training materials, and implement them with your students. You will be supporting your students’ development as learners generally, while you also promote deeper learning of your cognitive course learning objectives. Over time, you will have developed a library of metacognitive learning objectives and training, which you could have students explore and self-select from based on their needs.

Acknowledgements

This blog post is based upon metacognition research supported by the National Science Foundation under Grant Nos. 1932969, 1932958, and 1932947. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.

References

Cunningham, P. J., Matusovich, H. M., Hunter, D. A., Williams, S. A., & Bhaduri, S. (2017). Beginning to Understand Student Indicators of Metacognition. In the proceedings of the American Society for Engineering Education (ASEE) Annual Conference & Exposition, Columbus, OH.

Krathwohl, D. R. (2002). A revision of Bloom’s taxonomy: An overview. Theory into practice, 41(4), 212-218.

Nilson, L. (2013). Creating self-regulated learners: Strategies to strengthen students? self-awareness and learning skills. Stylus Publishing, LLC.

Seli, H., & Dembo, M. H. (2019). Motivation and learning strategies for college success: A focus on self-regulated learning. Routledge.

Svinicki, M. D. (2004). Learning and motivation in the postsecondary classroom. Anker Publishing Company.

Promoting Learning Integrity Through Metacognition and Self-Assessment

by Lauren Scharff, Ph.D., U. S. Air Force Academy*

When we think of integrity within the educational realm, we typically think about “academic integrity” and instances of cheating and plagiarism. While there is plenty of reason for concern, I believe that in many cases these instances are an unfortunate end result of more foundational “learning integrity” issues rather than deep character flaws representing lack of moral principles and virtues.

Learning integrity occurs when choices for learning behaviors match a learner’s goals and self-beliefs. Integrity in this sense is more like a state of wholeness or integrated completeness. It’s hard to imagine this form of integrity without self-assessment; one needs to self-assess in order to know oneself. For example, are one’s actions aligned with one’s beliefs? Are one’s motivations aligned with one’s goals? Metacognition is a process by which we gain awareness (self-assess) and use that awareness to self-regulate. Thus, through metacognition, we can more successfully align our personal goals and behaviors, enhancing our integrity.

Metacognitive Learning and Typical Challenges

When students are being metacognitive about their learning, they take the time to think about (bring into awareness) what an assignment or task will require for success. They then make a plan for action based on their understanding of that assignment as well their understanding of their abilities and current context. After that, they begin to carry out that plan (self-regulation). As they do so, they take pauses to reflect on whether or not their plan is working (self-awareness/self-assessment). Based on that interim assessment, they potentially shift their plan or learning strategies in order to better support their success at the task at hand (further self-regulation).

That explanation of a metacognitive learning may sound easy, but if that were the case, we should see it happening more consistently. As a quick example, imagine a student is reading a text and then realizes that they are several pages into the assignment and they don’t remember much of what they’ve read (awareness). If they are being metacognitive, they should come up with a different strategy to help them better engage with the text and then use that alternate strategy (self-regulation). Instead, many students simply keep reading as they had been (just to get the assignment finished), essentially wasting their time and short-cutting their long-term goals.

Why don’t most students engage in metacognition? There are several meaningful barriers to doing so:

Pausing to self-assess is not a habitual behavior for them
It takes time to pause and reflect in order to build awareness
They may not be aware of effective alternate strategies
They may avoid alternate strategies because they perceive them to take more time or effort
They are focused on “finishing” a task rather than learning from it
They don’t realize that some short-term reinforcements don’t really align with their long-term goals

These barriers prevent many students engaging in metacognition, which then makes it more likely that their learning choices are 1) not guided by awareness of their learning state and 2) not aligned with their learning goals and/or the learning expectations of the instructor. This misalignment can then lead to a breakdown of learning integrity with respect to the notion of “completeness” or “wholeness.”

For example, students often claim that they want to develop expertise in their major in order to support their success in their future careers. They want to be “good students.” But they take short-cuts with their learning, such as cramming or relying on example problem workout steps, both of which lead to illusions of learning rather than deep learning and long-term retention. These actions are often rewarded in the short term by good grades on exams and homework assignments. Unfortunately, if they engage in short-cutting their learning consistently enough, when long-term learning is expected or assessed, some students might end up feeling desperate and engage in blatant cheating.

Promoting Learning Integrity by Providing Support for Self-Assessment and Metacognition

Promoting learning integrity will involve more than simply encouraging students to pause, self-reflect, and practice self-regulation, i.e. engage in metacognition. As alluded to by the list of barriers above, being metacognitive requires effort, which also implies that learning integrity requires effort. Like many other self-improvement behaviors, developing metacognition requires multiple opportunities to practice and develop into a way of doing things.

Fortunately, as instructors we can help provide regular opportunities for reflection and self-assessment, and we can share possible alternative learning strategies. Together these should promote metacognition, leading to alignment of goals and behaviors and to increased learning integrity. The Improve with Metacognition website offers many suggestions and examples used by instructors across the disciplines and educational levels.

To wrap up this post, I highlight knowledge surveys as one way by which to promote the practice and skill of self-assessment within our courses. Knowledge surveys are shared with students at the start of a unit so students can use them to guide their learning and self-assess prior to the summative assessment. Well-designed knowledge survey questions articulate granular learning expectations and are in clear alignment with course assessments. (Thus, their implementation also supports teaching integrity!)

When answering the questions, students rate themselves on their ability to answer the question (similar to a confidence rating) as opposed to fully writing out the answer to the question. Comparisons can be made between the confidence ratings and actual performance on an exam or other assessment (self-assessment accuracy). For a more detailed example of the incorporation of knowledge surveys into a course, as well as student and instructor reflections, see “Supporting Student Self-Assessment with Knowledge Surveys” (Scharff, 2018).

By making the knowledge surveys a meaningful part of the course (some points assigned, regular discussion of the questions, and sharing of students’ self-assessment accuracy), instructors support the development of self-assessment habits, which then provide a foundation to metacognition, and in turn, learning integrity.

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* Disclaimer: The views expressed in this document are those of the author and do not reflect the official policy or position of the U. S. Air Force, Department of Defense, or the U. S. Govt.

Using Metacognition to Facilitate Scholarly Identity

by Anton Tolman, Ph.D., Guest Editor

This is the final and concluding blog for our series. I want to thank my colleagues for their time and effort in this project: Steven Pearlman, Christopher Lee, and Benjamin Johnson. Speaking for all of us, we hope you found our thoughts helpful in enriching your own thinking regarding metacognition and its importance in student learning.

The topics of this series, critical thinking, inclusive classrooms, student motivation, and succeeding with collaborative learning, are all essential themes in local and national discussions right now concerning student engagement and effective teaching. Each of the blogs in the series also touched on resistance to change (faculty or student), either explicitly or implicitly, the role of humility, and the development of metacognitive skills in achieving successful outcomes. Enhancing metacognition in ourselves and in our students is an ongoing progression, a journey, and we are happy to be walking it with you. In this last blog, I address the connection between metacognition and development of students’ personal narrative, their identity as scholars or educated persons. I believe this is the true heart of higher education and the core of its value to society.

You Can’t Change What You Can’t See

This phrase is an axiom in clinical work with clients. Clients often come to see a therapist knowing that things are wrong in their lives, but they don’t understand the reasons why or do not see a path forward to healing. They can’t change their lives for the better until they begin to “see” the nature of their problems, accept responsibility for their own role in those problems, and imagine and start to walk the road ahead.

This axiom applies to students. They usually come to college based on the promise of an economic benefit like higher paying jobs, or because they see a degree as a requirement for future goals. Many, if not most, see the purpose of education as learning facts or information and therefore, see the role of professors as experts who teach them content. When they are confronted with assignments that ask them to use critical thinking, solve problems, or work together, they can become easily frustrated. Thus, the terms “jumping through hoops” and “busy work” are commonly found in student conversations about their classes. These forms of resistance (Tolman & Kremling, 2017) are understandable because many students can’t see that the real goal of higher education is skill development, not content; it is not easily visible to them. Like the therapist’s clients, they won’t make progress until they develop the capacity to recognize the underlying issues and see the path ahead as one of purpose and value.

Student resistance to learning begins to diminish when students evaluate their own attitudes and behaviors and connect those behaviors to their academic performance. When they learn to develop metacognitive skills they can “see” previously unseen patterns in themselves and others: they recognize their own complicity in their academic struggles and begin to grasp that they are not just consumers of external information or persons being judged by some authority figure. This empowers them to assume responsibility, take action on their own, to succeed, to grow, and to become part of a community of learners.

Seeing is Believing: Shifting Identity in Higher Education

In his recent blog, Taraban (2020) describes identity as an ongoing form of development grounded in episodic memory: the story we tell ourselves about who we are. This self-narrated story is strongly shaped by the boundaries of what students “see” as the purpose of education, their personal goals, and how they approach learning. If students’ sense of identity about who they are does not change from that of being consumers of content or “students”, then we have failed them.

If we were to adopt the model of cognitive apprenticeship (Collins, Brown, & Newman, 1987) in our teaching, seeing ourselves more as mentors to students, then our major task becomes to shift their story, their identity, to that of being apprentices, not students. As apprentices, they are learning new skills under the guidance of an expert who cares for them, and who asks them to constantly re-evaluate what they are learning, consider how they are learning it, and when and how to use what they are learning. This entails a transition towards seeing themselves as participating members of the academy, as scholars and educated persons who contribute to society; metacognition is at the core of this identity shift.

Undergraduate research is a great example of this as articulated by Charity-Hudley, Dickter, and Franz (2017). They explain that the mechanism of action of this “high impact” practice on student success and retention, especially for minority or under-represented students, begins as students enter into a mentored relationship with a professor. Moving away from the traditional “student” role enables them to realize there is more to learning besides getting a grade or completing course assignments. Metacognitive activity like learning to reflect and ask their own questions, carry out their own research, generate new data, challenge their own ideas as well as existing ideas in the discipline, and create new understandings, makes them a contributor to knowledge, not just a consumer. They are a scholar, or at least a scholar-apprentice, and those episodic memories begin to shift their own narrative identity — who they see themselves to be, how they interpret their own life and future. Of course, participating in research is not the only path available to this outcome.

This student progression requires creating opportunities for students to develop and use their metacognitive skills. In both Steve Pearlman’s (this blog series) and Hale’s (2012) potent arguments, the development of metacognitive and critical thinking skills is integral to development of a “personal intellectual narrative”; you cannot discuss metacognition without referencing aspects of critical thinking, and you cannot explain critical thinking without referring to the metacognitive processes involved. As Hale (2012) says, cultivation of an “intellectual language” is a key process in this development; it inducts students into the “Great Conversation” and it becomes part of their own personal history of intellectual development. The more we integrate metacognitive opportunities in our classes, and across the curriculum, the more likely we are to observe this transition occur.

Suggestions for Teaching

Here are some thoughts about ways to incorporate metacognitive practices that promote personal narratives in students:

Emphasize transparency and relevance. Explain the purposes of our assignments not just for short-term outcomes (learn something for a grade), but for the long-term (learn something to enhance a career and personal life, contribute to society); define and set expectations about the value of metacognition and its role in professional thinking within your discipline.
Assign metacognitive tasks that require students to evaluate their strengths and weaknesses as learners, identify learning strategies they are using and those they are not, and ask them to connect this information to their personal and career goals. Benjamin Johnson’s description of the Personal Learning Plan (this blog series), based partly on completing practical metacognitive inventories and evaluating how to improve is an example.
Emphasize the value of students thinking about their own development over time and their personal histories; reflective writing assignments, in all fields, are useful for this.
Use inquiry assignments requiring students to develop their own questions, do their own research, and apply it to course content and their lives.
Create opportunities for students to make their own thinking visible to themselves. Encourage them to question their learning, their assumptions, and acknowledge their areas of confusion as a community of learners. Hale (2012) suggests learning logs, real-time student writing of their thinking, questions, and descriptions of how they are approaching content, assignments, and preparation.
Shift your role from “sage on the stage” to a mentor of cognitive apprentices. Model professional thinking; demonstrate metacognition and critical thinking and help the students recognize it and practice it. One way I do this is to ask, and continuously reinforce, that students call me Coach T. In my syllabus I explain the rationale for this: my purpose is to facilitate their learning, give them exercises to improve, and to clarify or assist, but the basic responsibility for their learning, as with any athlete, actor, or musician, lies with themselves.
Evaluate your course design: what are the memories and personal experiences your students will take away relevant to metacognition? Do your assignments focus primarily on content acquisition or do they promote skill development, a sense of growth and progress towards becoming a scholar, ability to speak the intellectual language of the discipline and to reason within its context? What are your course objectives and where do they point your students: towards content, or towards becoming scholars?

These teaching practices help students “connect the dots” and see patterns they did not know existed: how they approach learning, how well they are learning, the purpose of education, and their own intellectual growth and development. Doing this reduces resistance and shifts their understanding of learning and of themselves. When we move our perspective from content to skills and weave metacognitive development into the fabric of our class, we create an environment encouraging the exploration of new personal narratives and identity for our students. This brings us closer towards achieving the potential that higher education has to offer. If you are already doing these things, hone your work, expand your empathy, and become more transparent. If you are not, you can see the road ahead, and you don’t have to travel it alone. Reach out, learn from others, and find greater joy in what you do.

References:

Charity-Hudley, A.H., Dickter, C.L., & Franz, H.A. (2017). The Indispensable Guide to Undergraduate Research: Success In and Beyond College. New York: Teachers College Press.

Collins, A., Brown, J. S., & Newman, S. E. (1987). Cognitive apprenticeship: Teaching the craft of reading, writing and mathematics (Technical Report No. 403). BBN Laboratories, Cambridge, MA.

Hale, E. (2012). Conceptualizing a personal intellectual history/narrative: The importance of strong-sense metacognition to thinking critically. In M.F. Shaughnessy (Ed). Critical Thinking and Higher Order Thinking. Nova Science Publishers, Inc.

Taraban, R. (2020, June 25). Metacognition and the Development of Self. ImproveWithMetacognition.com. https://www.improvewithmetacognition.com/metacognition-and-self-identity/

Tolman, A.O. & Kremling, J. (2017). Why Students Resist Learning: A Practical Model for Understanding and Helping Students. Sterling, VA: Stylus Publishing.

Metacognition and the Development of Self-Identity

by Roman Taraban, Ph.D. Texas Tech University

The question “What do you want to be when you grow up” should be familiar to all of us, as well as the typical responses: a firefighter, a pilot, a doctor, a nurse, a teacher, an astronaut. We playfully pose this question to children, not fully realizing we are inquiring about their ultimate self-identity – the deep and personal awareness of who they are. Children may not have a self-identity beyond “child,” “son” “daughter,” “student,” “soccer goalie,” “Girl Scout.” But over time, that will change.

So when does self-identity emerge, and how does metacognition help it along its developmental path? In this post, I propose that the emergence of self-identity is a lifelong process that begins in early childhood and has strong underpinnings in memory research. Flavell (1987) brings in the metacognitive factor, in part, through his discussion of metacognitive experiences. We all have self-identity, however, we know little about how to monitor and regulate it metacognitively in order to develop and maintain a healthy and adaptive sense of self.

Who Am I? Where Is My Life Going?

Self-identity emerges out of a specific kind of memory, known as episodic memory. Episodic memory enables a person to recall personally experienced events and to re-live those experiences in the here-and-now (Tulving, 2002). Fivush (2011) refers to the organized coherent sense of self that emerges from episodic experiences as autobiographical memory. Autobiographical memory allows a person to construct an evolving life story that creates a coherent sense of self-identity, of who we are. Thinking about these memory processes would seem to be a perfect place for metacognition to play a major role.

Autobiographical memory and, with it, narrative identity, develop starting in early childhood. A child’s identity is influenced, in part, by the opportunities for relating personal events through conversations with caregivers and friends. Mothers who are elaborative with their children before their preschool years have children who produce more coherent self-narratives by the end of their preschool years (Fivush, 2011). One way, for example, is by asking open-ended questions with some guiding information – e.g., What did we do at the park today? Parents, teachers, and friends continue to shape identity long into adulthood with the questions they ask and the personal experiences that they share. These interactions prompt reflections on one’s own experiences and resonate to the questions Who am I? Where is my life going?

Metacognitive Experiences

John H. Flavell, an American developmental psychologist, labeled higher-level cognition as metacognition and is regarded as a founding scholar in metacognitive research. A major component in Flavell’s theory is a metacognitive experience, which is “any kind of [a]ffective or cognitive conscious experience that is pertinent to the conduct of intellectual life” (Flavell, 1987, p. 24). Flavell suggests that there is a developmental element in individuals’ adaptive responsiveness to these experiences: “As one grows older one learns how to interpret and respond appropriately to these experiences” (p. 24). When do we have metacognitive experiences? According to Flavell, “when the cognitive situation is something between completely novel and completely familiar…where it is important to make correct inferences, judgments, and decisions” (p. 24).

The question of how and when self-identity evolves in college students was explored in an edited book on undergraduate research experiences (Taraban & Blanton, 2008). Students’ responses have the character of metacognitive experiences – i.e., conscious experiences in which inferences, judgments, and decisions are critical. It is metacognitive experiences like these that help us to theoretically bridge the development of self-identity from the nurturing discourses of mothers with young children, to the choice of fields of study in high school and college, and ultimately to a relatively stable identity as an adult professional:

Wyatt McMahon: Thus, as I grew up, when people asked me what I wanted to be, I realized that I wanted to help improve society, but I was not sure how.

Robin Henne: Before the tour [of Texas Tech Biology], I had no idea that research was even possible for biology majors; following the tour, I was convinced that research was what I wanted to do for my career.

Susan Harrell Yee: When I first started as a freshman at Texas Tech University, I chose environmental engineering as my major. It seemed a wise decision – I liked math and I liked ecology, and environmental engineering seemed to be a logical combination of the two. But after a single day, I knew the engineering route was not for me.

Engineering Identity

An area of great interest in current scholarly research involves engineering identity. Engineering educators are interested in how engineering students view themselves early on in their training (Loshbaugh & Claar, 2007), as well as what it means more generally to think of oneself as an engineer (Godwin, 2016; Morelock, 2017). The poignancy of this issue struck me when leading a discussion with graduate engineering students. The topic of discussion was, in part, personal narrative, which is the autobiographical narrative we create about ourselves and which is the basis of self-identity. It was evident from their comments that embracing a self-identity was not instantaneous upon choosing professional training. The following conveyed a sense of the struggle:

For the majority of my life, I have always been a “student” studying to become insert profession.

I sometimes to this day don’t consider myself as an engineer. I feel like throughout my time [here], I’ve always just been an “engineering student”.

I have struggled to see myself as an engineer but the older I get and the more secure I become in my field the easier it is to own and step into that narrative.

The Role of Metacognition

We are surrounded by instances of introspection regarding self-identity. Neal Diamond, the 20^th century pop singer, presented his reflections as an existential crisis: I am…I said. Walt Whitman, the 19^th century poet, gave a transcendental response in 52 parts in “Song of Myself,” and Reverend William Holmes Borders, Sr., a civil-rights activist, in the 1950s proclaimed “I Am Somebody” in a poem of self identity. Although we all have a sense of self-identity, very little explicit attention has been given in research to ways of metacognitively monitoring and guiding the development of a healthy and adaptive sense of self. This is one area where extending metacognitive theory beyond its current bounds could have a significant role in helping us to know who we are and to reach our true potential.

References

Fivush, R. (2011). The development of autobiographical memory. Annual Review of Psychology, 62, 559-582.

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

Godwin, A. (2016). The development of a measure of engineering identity. In Proceedings, ASEE Annual Conference & Exposition, New Orleans, LA.

Loshbaugh, H., & Claar, B. (2007). Geeks are chic: Cultural identity and engineering students’ pathways to the profession. In Proceedings ASEE Annual Conference & Exposition, Honolulu, HI.

Morelock, J. R. (2017). A systematic literature review of engineering identity: Definitions, factors, and interventions affecting development, and means of measurement. European Journal of Engineering Education, 42(6), 1240-1262.

Taraban, R., & Blanton, R. L. (Eds.). (2008). Creating effective undergraduate research programs in science: The transformation from student to scientist. New York: Teachers College Press.

Tulving, E. (2002). Episodic memory: From mind to brain. Annual Review of Psychology, 53, 1-25.

To Infinity and Beyond: Metacognition Outside the Classroom

by Kyle E. Conlon, Ph.D., Stephen F. Austin State University

My wife, Lauren, and I met in graduate school while pursuing our doctoral degrees in social psychology. Since then, we’ve taught abroad in London, moved to two different states, landed jobs at the same institution—our offices are literally right next to each other’s—bought a house, and had a child. It’s fair to say that our personal and professional lives interweave. One of the great joys of having an academic partner is having someone with whom I can share the challenges and triumphs of teaching. Although we have long promoted the benefits of metacognition in our classrooms, we use metacognition in so many other domains of our lives as well. But the link between metacognitive practice in the classroom and real-world problem solving isn’t always clear for students.

In this post, I’ll discuss how facilitating metacognition among your students can benefit them long after they’ve finished your class, with an emphasis on two important life goals: financial planning and healthy eating.

Metacognition and Money

At first glance, a college student may find little connection between thinking about his or her test performance in an introductory psychology class and building a well-diversified investment portfolio years later. But the two are more intimately linked than they appear. Students who possess high metacognitive awareness are able to identify, assess, and reflect on the effectiveness of their study strategies. This process requires the development and cultivation of accurate self-assessment and self-monitoring skills (Dunlosky & Metcalfe, 2009). As teachers, then, we serve as primary stakeholders in our students’ metacognitive development.

Just as successful students think about their own thinking, successful investors spend a lot of time thinking about how to manage their money—how to invest it (stocks, bonds, REITs, etc.), how long to invest it, how to reallocate earnings over time, and so on. Smart investing is virtually impossible without metacognition: it requires you to continually assess and reassess your financial strategies as the markets move and shake.

Even if your students don’t plan on being the next Warren Buffet, financial thinking will play a central role in their lives. Budgeting, buying a house or a car, saving for retirement, paying off debt—all of these actions require some level of financial literacy (not to mention self-control). Of course, I’m not saying that students need a degree in finance to accomplish these goals, just that they are more easily attainable with strong metacognitive skills.

Indeed, financial security is elusive for many; for instance, the 2018 Report on the Economic Well-Being of U.S. Households found that many adults would struggle with a modest unexpected expense. There are real financial obstacles that families face, for sure. Because financial literacy has broad implications, from participation in the stock market (Van Rooj et al., 2011) to retirement planning (Lusardi & Mitchell, 2007), the transfer of metacognitive skills from academic to financial decisions may be especially paramount.

Admittedly, when I was an 18-year-old college student, I didn’t think much about this stuff. (I was too busy studying for my psychology exams!) But now, years later, living on a family budget, I have a deep appreciation for how the metacognitive awareness I cultivated as a student prepared me to think about and plan for my financial future. For your students, the exams will end, but the challenges of adulthood lie ahead. Successfully navigating many of these challenges will require your students to be metacognitive about money.

Metacognition and Food

As with planning for one’s financial future, eating healthy food is a considerable challenge that involves tradeoffs: Do I eat the salad so I can keep my cholesterol low, or do I enjoy this piece of delicious fried chicken right now, cholesterol be damned? Anyone who’s ever struggled with eating healthy food knows that peak motivation tends to occur shortly after committing to the goal. You go to the grocery store and buy all the fruits and vegetables to replace the unhealthy food in your fridge, only to throw away most of it later that same week. Why is eating healthfully so difficult?

There is an important role for metacognition here. When I teach my Health Psychology students about healthy eating, I draw the habit cycle on the whiteboard: cue à routine à reward (Duhigg, 2012). I tell students that breaking a bad habit requires changing one piece of the cycle (routine). Keep the cue (“I’m hungry”) and the reward (“I feel good”) the same, just change the routine from mindlessly eating a bag of potato chips to purposefully eating an apple. Implicit in this notion is the need to be aware of what you’re eating and the benefits of doing so—in other words, metacognition. Another idea is to have students draw out their steps through the grocery store so they can see which aisles they tend to avoid and which aisles they tend to visit (the ones with processed food). Students gain metacognitive awareness by literally retracing their steps.

In college, I survived on sugar, sugar, and more sugar. (One category short of Buddy the Elf’s four main food groups.) Since then, my metabolism has slowed considerably. Fortunately, with the help of metacognition, I’ve changed my diet for the better. I also cook most meals for our family, so I’m constantly thinking about meal plans, combinations of healthy ingredients, and so on. For me, as for many people, healthy eating didn’t occur overnight; it was a long process of habit change aided by awareness and reflection of the food I was consuming. The good news for your students is that they have several opportunities every day to think intently about their food choices.

The Broad Reach of Metacognition

As a teacher, I love those “lightbulb” moments when a student makes a connection that was previously unnoticed. In this post, I’ve tried to connect metacognition in the classroom to two important life domains. By fostering metacognition, you’re indirectly and perhaps unknowingly teaching your students how to make sound decisions about their finances and eating habits—and probably hundreds of other important life decisions. Metacognition is not limited to exam grades and paper rubrics; it’s not confined to our classrooms. It’s one of those special, omnipresent skills that will help students flourish in ways they’ll never see coming.

References

Board of Governors of the Federal Reserve System (2019). Report on the economic well-being of U.S. households in 2018. https://www.federalreserve.gov/publications/files/2018-report-economic-well-being-us-households-201905.pdf

Duhigg, C. (2012). The power of habit: Why we do what we do in life and business. Random House.

Dunlosky, J., & Metcalfe, J. (2009). Metacognition. Sage Publications, Inc.

Lusardi, A., & Mitchell, O. S. (2007). Financial literacy and retirement preparedness: Evidence and implications for financial education. Business Economics, 42(1), 35‒44.

Van Rooj, M., Lusardi, A., & Alessie, R. (2011). Financial literacy and stock market participation. Journal of Financial Economics, 101(2), 449‒472.

How Metacognition Helps Develop a New Skill

by Roman Taraban, Ph.D., Texas Tech University

Metacognition is often described in terms of its general utility for monitoring cognitive processes and regulating information processing and behavior. Within memory research, metacognition is concerned with assuring the encoding, retention, and retrieval of information. A sense of knowing-you-know is captured in tip-of-the-tongue phenomena. Estimating what you know through studying is captured by judgments of learning. In everyday reading, monitoring themes and connections between ideas in a reading passage might arouse metacognitive awareness that you do not understand a passage that you are reading, and so you deliberately take steps to repair comprehension. Overall, research shows that metacognition can be an effective aid in these common situations involving memory, learning, and comprehension (Dunlosky & Metcalfe, 2008).

image from https://www.champagnecollaborations.com/keepingitreal/keeoing-it-real-getting-started

But what about new situations? If you are suddenly struck with a great idea, can metacognition help? If you want to learn a new skill, how does metacognition come into play? Often, we want to develop fluency, we want to accurately and quickly solve problems. The classic model of skill development proposed by Fitts and Posner (1967) did not explicitly incorporate metacognition into the process. A recent model by Chein and Schneider (2012), however, does give metacognition a prominent role. In this blog, I will review the Fitts and Posner model, introduce the Chein and Schneider model, and suggest ways that the latter model can inform learning and development.

In Fitts and Posner’s (1967) classic description of the development of skilled performance there are three overlapping phases:

Initially, facts and rules for a task are encoded in declarative memory, i.e., the part of memory that stores information.
The person then begins practicing the task, which initiates proceduralization (i.e., encoding the action sequences into procedural memory), which is that part of memory dedicated to action sequences. Errors are eliminated during this phase and performance becomes smooth. This phase is conscious and effortful and gradually shifts into the final phase.
As practice continues, the action sequence, carried out by procedural memory, becomes automatic and does not draw heavily on cognitive resources.

An example of this sequence is navigating from point A to point B, like from your home to your office. Initially, the process depends on finding streets and paying attention to where you are at any given time, correcting for wrong turns, and other details. After many trials, you leave home and get to the office without a great deal of effort or awareness. Details that are not critical to performance will fall out of attention. For instance, you might forget the names of minor streets as they are no longer necessary for you to find your way. Another more academic example of Fitts and Posner includes learning how to solve math problems (Tenison & Anderson, 2016). In math problems, for instance, retrieval of relevant facts from declarative memory and calculation via procedural memory become accurate and automatic along with speed-up of processing.

Chein and Schneider (2012) present an extension of the Fitts and Posner model in their account of the changes that take place from the outset of learning a new task to the point where performance becomes automatic. What is distinctive about their model is how they describe metacognition. Metacognition, the first stage of skill development, “guides the establishment of new routines” (p. 78) through “task preparation” (p. 80) and “task sequencing and initiation” (p. 79). “[T]he metacognitive system aids the learner in the establishing the strategies and behavioral routines that support the execution of the task” (p. 79). Chein and Schneider suggest that the role of metacognition could go deeper and become a characteristic pattern of a person’s thoughts and behaviors: “We speculate that individuals who possess a strong ability to perform in novel contexts may have an especially well-developed metacognitive system which allows them to rapidly acquire new behavioral routines and to consider the likely effectiveness of alternative learning strategies (e.g., rote rehearsal vs. generating explanations to oneself; Chi, 2000).”

In the Chein and Schneider model, metacognition is the initiator and the organizer. Metacognitive processing recruits and organizes the resources necessary to succeed at learning a task. These could be cognitive resources, physical resources, and people resources. If, for example, I want to learn to code in Java, I should consider what I need to succeed, which might include YouTube tutorials, a MOOC, a tutor, a time-management plan, and so on. Monitoring and regulating the cognitive processes that follow getting things set up are also part of the work of metacognition, as originally conceived by Flavell (1979). However, Chein and Schneider emphasize the importance of getting the bigger picture right at the outset. In other words, metacognition can work as a planning tool. We tend to fall into thinking of metacognition as a guide for when things go awry. While we know that it can be helpful in setting learning goals so that we can track progress towards those goals and resources to help us achieve them, we may fall into thinking of metacognition as a “check-in” when things go wrong. Of course, metacognition can be that too, but metacognition can be helpful on the front end, especially when it comes to longer-term, challenging, and demanding goals that we set for ourselves. Often, success depends on developing and following a multi-faceted and longer-term plan of learning and development.

In summary, the significant contribution to our understanding of metacognition that Chein and Schneider (2012) make is that metacognitive processing is responsible for setting up the initial goals and resources as a person confronts a new task. With effective configuration of learning at this stage and sufficient practice, performance will become fluent, fast, and relatively free of error. The Chein and Schneider model suggests that learning and practice should be preceded by thoughtful reflection on the resources needed to succeed in the learning task and garnering and organizing those resources at the outset. Metacognition as initiator and organizer sets the person off on a path of successful learning.

References

Chein, J. M., & Schneider, W. (2012). The brain’s learning and control architecture. Current Directions in Psychological Science, 21, 78-84.

Chi, M. T. (2000). Self-explaining expository texts: The dual processes of generating inferences and repairing mental models. In R. Glaser (Ed.), Advances in instructional psychology, (Vol. 5), pp. 161-238. Mahwah, NJ: Erlbaum.

Dunlosky, J., & Metcalfe, J. (2008). Metacognition. SAGE, Los Angeles

Fitts, P. M., & Posner, M. I. (1967). Human performance. Belmont, CA: Brooks/Cole.

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

Tenison, C., & Anderson, J. R. (2016). Modeling the distinct phases of skill acquisition. Journal of Experimental Psychology: Learning, Memory, and Cognition, 42(5), 749-767.

Developing Metacognition with Student Learning Portfolios

In this IDEA paper #44, The Learning Portfolio: A Powerful Idea for Significant Learning, Dr. John Zubizarreta shares models and guidance for incorporating learning portfolios. He also makes powerful arguments regarding the ability of portfolios to engage students in meaningful reflection about their learning, which in turn will support a metacognitive development and life-long learning.

In Remembrance of Dr. Gregg Schraw and Dr. Marty Carr

By Hillary Steiner, Ph.D., Kennesaw State University and Aaron S. Richmond, Ph. D., Metropolitan State University of Denver

In this first blog post of 2018 we remember two educational psychologists with interests in metacognition who recently passed away. Aaron Richmond and Hillary Steiner describe how their personal and professional interactions with these scholars influenced their own work.

From Aaron: In my career as an educational psychologist, I was more than lucky to work with Gregg—I was honored. On September 16^th, 2016, Gregg passed away with his sweet wife Lori by his side. Gregg was a prolific researcher in metacognition and in other educational research fields. He published over 90 journal articles, 15 books, and 45 book chapters. He sat on several editorial boards including the Journal of Educational Psychology, Metacognition and Learning, Educational Psychology Review, etc. He was an active member of Division C (Learning and instruction) in the American Educational Research Association, and several other regional academic conferences such as Northern Rocky Mountain Educational Research Association (NRMERA).Yes, Gregg was a prolific scholar, however, his greatest gift was to his students and colleagues. One of my dear friends and fellow metacognitive researcher Rayne Sperling at Pennsylvania State University wrote, “Gregg’s confidence in me and steady, supportive guidance provided the self-efficacy boost I needed in order to believe in myself as a scholar with something to say. As co-chair of my dissertation, mentor throughout my career, and dear friend always, Gregg was a strong, positive force in my life. Now, my own doc students tell me I am a wonderful, supportive mentor, and I always tell them, “I am just doing what I was taught; mentoring as I was mentored.” Gregg taught me this too. His mentoring continues with the students he mentored (and there are a lot of us) who now have students of our own.” (McCrudden, 2016, p. 681).

I had followed Gregg’s career, seen him at conferences—in awe of course with a star-struck gaze and for me, Gregg was a research icon. He was a mega-god for which I was not worthy. However, when I first met Gregg at NRMERA in the fall of 2003, I was a dewy-eyed graduate student who had plucked up the courage to introduce myself to discuss metacognitive research. I quickly realized that yes—he was a research god, but more importantly he was a kind, generous, supportive, and inclusive person / human. He listened to my good ideas and listened to my half-cocked ideas that needed serious fine-tuning. After that fateful day in Jackson Hole, Wyoming I knew that I had gained a mentor of all things. Gregg supported me through my career in both research and teaching. We published together, and he was one of my advocates. He advanced my career like so many others. He doled out sound and sincere professional advice willingly. For example, Gregg, Fred Kuch, and I were working on some metacognition research together and my students were working quite hard and doing a great job on the project. Mind you, I am at a large state university with no graduate students so these were undergraduate students. Gregg was so impressed with one of my students (because of the mentorship he and his students had provided me which I had passed on to my students) he offered to write her a letter of recommendation for graduate school. I found this simple but powerful and impactful gesture to be astonishing and yet typical of Gregg’s passion for advancing high quality scholars in the field of metacognition and educational psychology. This was just one simple example of how Gregg went out of his way to help people and support their goals and pursuits.

In the end, Gregg didn’t have to be, but he was my mentor like so many others. Gregg am I indebted to you and you will truly be missed. The field of metacognition lost a great scholar, mentor, and friend.

From Hillary:

On July 30, 2017, the field of metacognition lost another great. Dr. Martha “Marty” Carr, Professor of Educational Psychology at the University of Georgia, passed away at the young age of 59. A prolific researcher who mentored countless students to become scholars in their own right, Marty combined her interests in metacognition, motivation, giftedness, and mathematics achievement to impact the field of educational psychology in a unique way, asking big questions about how children’s metacognitive strategies influence the gender differences that emerge in mathematics achievement, and how metacognition differs in gifted children.

Marty began her career in developmental psychology at the University of Notre Dame under the tutelage of John Borkowski, followed by a postdoctoral stint at the Max Planck Institute for Psychological Research in Germany, where she quickly made important contributions related to the influence of motivation and metacognition on children’s learning strategy development. After joining the faculty of the University of Georgia in 1989, where she remained for her entire career, she began to cultivate additional interests in giftedness and mathematics strategy development. These varied interests dovetailed throughout the years, as she wrote about metacognition in gifted children, motivational and self-regulatory components of underachievement, and metacognitive influences on gender differences in math. Marty’s work was known for its methodological rigor, its unique application of developmental models and methods to learning processes, and its applicability to the classroom. She was recognized in particular for groundbreaking work on the predictors and influential factors of gender differences in mathematics. Her contributions led to national recognition and leadership, including presidency of the American Psychological Association’s Educational Psychology Division (Division 15), presidency of the Women in Mathematics Education division of the National Council for Teachers in Mathematics, and numerous awards, including the American MENSA Education and Research Foundation Award for Excellence.

As my dissertation advisor in the early 2000’s, Marty was the first person to make me feel like a scholar. She recognized my interests in giftedness and cognitive development and provided the perfect combination of support and encouragement that helped me craft a line of research that continues to this day. And I am not alone. At her memorial service, several students commented on how much her mentorship had meant to them. According to student Kellie Templeman, “her skill in striking the balance between technical knowledge, compassionate guidance, and tireless work ethic was what separated her from any other professor I have worked with.” She promoted metacognition in her own students by asking them to reflect constantly on the “why” questions of their individual projects and to remain goal-driven. As another former student noted, Marty pushed us to “keep going, get busy, and keep writing,” learning from our mistakes as we went. Yet, as a devoted mother who had many outside interests, including marathon running and working with animals (especially cats and horses) Marty was also an excellent model of work-life balance.

When I attended the American Educational Research Association conference as a graduate student, Marty introduced me to Gregg Schraw, who was to be my assigned mentor for the week. I was starry-eyed at meeting such a great figure in my field, but later realized that others were equally starry-eyed to meet Marty. Marty and Gregg were truly giants in educational psychology whose contributions have transformed the way we think about metacognition. May we continue to honor their memory in our own work.

References

McCrudden, M. T. (2016). Remembering Gregg Schraw. Educational Psychology Review, 28(4), 673-690.

Tackling your “Laundry” List through Metacognitive Goal Setting

by Tara Beziat at Auburn University at Montgomery

On almost every to-do list I make these days is the word “Laundry.” With two kiddos and a husband who is an avid exerciser, our laundry quickly piles up. Recently, when I told my husband I had everything washed, I paused and thought about my goal of getting the laundry done. I can never actually get it all done. The goal is too broad and it is not time bound. I paused again and thought here I go again being metacognitive: I have goals; I am monitoring them and seeing if I meet them; I realized I needed to make adjustments. In going through this metacognitive process at home, I realized there were applications in my classroom too. I needed to help my students reframe their goals of “reading the textbook or “studying” and build better plans to reach them.

Backwards Planning

The first thing we need to do with goal setting is to build better plans to reach those goals, which research suggests could involve working backwards from the end state of those goals, (Jooyoung, Lu & Hedgock; 2017). It seems that when we have distant goals that involve many tasks, like a comprehensive exam, mid-term project or final presentation, a variety of issues come into play. Inadvertently, obstacles or “speed bumps” slow down our momentum towards the end goal and leave us discouraged. By starting with the end goal (e.g. comprehensive exam) and working backwards to the present time, we often anticipate these potential hurdles. This type planning also leads to the creation of sub-goals. The relatively immediacy of these sub-goals and then the completion of them leads to greater motivation in meeting the final goal.

What this means in my course is that I need to help students develop a timeline, so they see all of the tasks and activities they need to do to reach their end goals. As we develop this timeline, we will work backwards. As we chart out the plan for success, we can acknowledge potential hurdles that may require them to take more time with one task or even shift their preparation. If a large project is due the Monday after the Iron Bowl, a significant event here in Alabama, they may need to consider when they can work on the project prior to that game. By forecasting these “speed bumps,” and planning out the steps in reverse to reach their ultimate goals.

Set Specific Goals

Schunk (1990) identified specificity as one of the keys in goal setting. When we set specific goals, we can better gauge the amount of time and effort it will take to complete this goal. Specificity also allows for better monitoring, a key component in being metacognitive, and can lead to increased self-efficacy as one meets these goals. So students’ goals of “doing well in the course” or “studying harder” are not specific enough and need to be adjusted. To do well in the course, students need to consider what does this actually mean and what sub-tasks are involved to reach this goal. For example, they need to consider what they need to get on the various quizzes and assignments in the course if they want to have an A. This leads to a discussion about preparing for class, allocating study time and allocating time to assignments for the course. All of these can go on this timeline where we work backwards.

Time-Bound Goals

The proximity of the goal plays a key factor in our motivation (Schunk, 1990). Goals that are proximal are more motivating than distal goals. This again goes back to why it is important to plan backwards. It allows us to set up intermediate proximal goals during the semester so we can reach the distal goals. Students (and even professors) often say they are going to study in the afternoons or they are going to read over the weekend. Invariably, “speed bumps” occur and the studying and reading are pushed aside. By blocking out time in your schedule, just like you block out time to attend class, with start times and end times you are more likely to devoted your undivided attention to the task. Dr. Paul Pacheco-Vega provides great advice about planning and how to set up your calendar to get your tasks done. He even shows how to adjust your schedule for when those speed bumps occur. The key is to set aside time in your calendar but also to be aware of that life may just throw you a curve.

By helping my students reframe their goals and build a backwards timeline of how to accomplish their goals, I increase the chances of my students not only being successful in my course but also in their future courses. I am also helping them become more metacognitive. They are learning metacognitive strategies related to setting goals and monitoring and evaluating their progress toward this goal. As an added benefit this approach may lead to higher self-efficacy and increased learning.

Metacognitive strategies are not just for the classroom or academic environment, they have helped me improve my laundry process too! I have set better goals for my chore of doing laundry. I start with the end goal, to have all of the laundry washed and put away by Monday morning. The “laundry” is limited to the clothes in the hampers on Friday. I then set out to complete one load of laundry on Friday, Saturday and Sunday and then I put it away on Monday. This plans leaves lots of room for the numerous unforeseen hurdles in rearing two children under two.

Jooyoung, P., Lu, F., Hedgcock, W. (2017). Forward and Backward Planning and Goal Pursuit. Psychological Science. DOI:10.1177/0956797617715510

Schunk, D. H. (1990). Goal Setting and Self-Efficacy During Self-Regulated Learning. Educational Psychologist, 25(1), 71-86

The Promotion of Metacognition Through Soft Skills

by Mary Hebert, Fairleigh Dickinson University

Downloadable

Description of Activity:

I teach a course in Metacognitive Strategies which focuses on the social and emotional components to academic success. These are referred to as ‘soft skills’ (emotional intelligence, interpersonal and intrapersonal awareness, emotional regulation, problem solving etc.) The course is presented to students who are members of the Regional Center at FDU who have been diagnosed with a language-based learning disability and or ADHD/ADD. Weekly journal reflections are completed based on a prompt that reflects a soft skill that is being addressed in the lecture. These journal entries serve as a means of enhancing metacognition and reflection of the material and focus on strategies of incorporating the skill into practice of academic performance.

An additional element involves a final project which requires the students to identify an individual they have admired for their successful accomplishment of some specific achievement. They are required to interview this individual and discover the soft skills associated with their accomplishment, not the ‘hard skills’ which are traditionally aligned with success (GPA, School Attended, Titles achieved etc). Furthermore, the student specifically is asked to assess their own soft skill set, including areas that are strengths and those to develop, and implement a plan of incorporating these into their academic goals and pursuits.

Further details of the activities can be found here.

Motivation and Context:

The class is designed to explore the ‘soft skills’, which include the social and emotional skills that are associated with academic success. The assignments are designed to provide tangible exercises that, when explored in a metacognitive manner and applied purposefully with a plan, can result in success and improve the academic and career course of an individual. The goal is improved self-regulation and critical thinking in regard to specific social and emotional skills that are highly correlated with academic success.

Nuts and Bolts:

The specific intent of this course and its assigned exercises is to weave an academic experience with the content of metacognition and soft skills that are connected to academic and career success. Specific topics addressed include emotional intelligence, personal responsibility, grit, self-motivation, interdependence, active listening, self-awareness, life-long learning, motivation, growth mindset, and goal setting. Students participate in discussion, reflection exercises, and the final project requiring them to take the knowledge of soft skills presented in class, think critically and analyze these topics, and implement them by carrying out an interview and create a presentation. The final project of interview and presentation is a culmination of analyzing a ‘story of success’ that from a distance may have looked easily attained for the interviewee. The task is for the student to discover through inquiry about soft skills, how in fact these played a critical role in the successful outcome for the interviewee. The students acquire insight into the ‘reality’ of the achievement, reflect on the soft skills they have developed and ones that they would benefit from developing further. A key feature is working on the plans of implementation which demonstrates improved critical thinking and capacity for self-regulation of good decision making and goal attainment.

The result is metacognitive ‘boot camp’ in regard to the less frequented content in the classroom that are key to academic effectiveness. Metacognition has been associated with improved critical thinking skills (Magno, 2010). Students are given knowledge about soft skills, asked to discuss through oral and written means of reflection, and then take it a step further and asked to apply the concepts to their own academic tasks throughout the semester. This sequence of knowledge acquisition, analysis, and application are the nuts and bolts of weaving the material together.

Outcomes:

The highly interactive nature of the course forces the contemplation necessary for students to adopt a more metacognitive approach to learning and their goals beyond the classroom. Critical thinking and self -regulation related to the connection between soft skill development and their academic and learning capacity is improved. As a counselor within the program that serves the students, I meet with each student individually one time per week during their freshmen year. I have observed that students begin to synthesize the course material with their academic functioning and improve their approach to matters related to their courses, studying, and academic goals. Many students begin to consider options to their approach in regard to their broader education and learning environment.

The culminating final project results in enhanced awareness of the interdependent nature of soft skills and hard skills for overall success in learning and career effectiveness. Presentations have been extraordinarily diverse with students choosing political figures, doctors, artists, students, business people, professors, peers, parents, coaches etc. Each year the series of presentations showcases the synthesis of soft skills and how growing awareness and purposeful use of these optimizes success academically as well as in career endeavors. Students demonstrate through their writing and oral reflection of their own use of soft skills, goals of further developing targeted soft skills during college to assist them in achieving academic success as well as future career success.

Lessons Learned and Future Directions:

The literature is clear in support of the importance of soft skills both in the classroom and in life. While some time during the course is spent connecting the material to career endeavors, future directions might include more of this element. In addition, it would be worthy to have a ‘maintenance program’ that extends beyond the time of the course, so that as the freshmen students progress, they are provided with opportunities to review and integrate the soft skill concepts throughout their remaining years of their college experience.

As a higher order thinking strategy, metacognition offers the opportunity to enhance and tap into the potential of the brain power within each student. Greater flexibility and awareness in thinking is the outcome and the continued goal of this form of application of metacognition.

Reference

Magno, C. Metacognition Learning (2010) 5: 137. doi:10.1007/s11409-010-9054-4

Fighting Imposter Syndrome Through Metacognition

By Charity S. Peak, Ph.D.

Have you ever felt like an imposter at work? Taught a class that was not your expertise? Felt intimidated before giving a presentation? Nearly every faculty member experiences this imposter phenomenon at some point. After all, as faculty we work around incredibly smart and talented people who shine from being experts in their field. Additionally, people drawn to academia naturally feel compelled to be knowledgeable and often find themselves to be inadequate when they are not (Huston, 2009).

Imposter syndrome is “an overwhelming sense of being a fraud, a phony, of not being good enough for [a] job, despite much evidence to the contrary” (Kaplan, 2009). Apart from accomplishing significant professional milestones, people cannot seem to internally acknowledge their success or feel deserving. This sense of being an imposter is prevalent among women but is increasingly being revealed by men as well. Although the condition is often referred to as a syndrome, it is important to understand that it is NOT actually a diagnosable mental illness found in the DSM-V. Instead, it is an affliction, similar to test or performance anxiety, experienced by a variety of high-achieving individuals that can be treated successfully using metacognition and self-regulation.

Reactions to imposter syndrome vary widely and by individual. Typically, imposter phenomenon starts with a self-sabotaging internal dialogue, such as:

Who do I think I am? I’m not smart enough to teach this class or present on this topic.
What if my students ask me a question that I can’t answer?
What if someone finds out I don’t know what I’m talking about?
I’m not cut out for this. I really can’t do this.

A physical reaction similar to other stressful situations (fight, flight, or freeze) often follows:

Increased blood pressure
Blushing
Sweating
Shaking
Tonic immobility (i.e., mental block or “deer in headlights”)

Faculty in these situations tend to respond in one of two ways:

Undercompensating by becoming submissive, overly agreeable or even apologetic
Overcompensating with defensive, bossy and aggressive behaviors

Recognize symptoms when they arise and recenter yourself through breathing:

Assume a comfortable posture
Close your eyes if possible
Focus on the sensations of your body
Breathe in through your nose and out through your mouth
When your mind wanders, gently bring it back to your breath
Breathe in, breathe out
Repeat for at least 10 breaths and up to 5 minutes

Reconstruct a new, positive internal dialogue. Talk to yourself as you would a good friend by being supportive and confidence-building.
Posture yourself as confident. It turns out that “fake it till you make it” works with regard to physical posture. People who use Power Poses for 2 minutes demonstrate higher levels of confidence-building hormones (testosterone) as opposed to stress-inducing hormones (cortisol) (Carney, Cuddy & Yap, 2010; Cuddy, 2012).
Acknowledge the limits of your knowledge. Instead of hiding your lack of expertise, build a repertoire of ways to deflect difficult questions, such as:

What do you think?
I don’t know. Does anyone want to look it up and tell us the answer?
Great question. Can we talk about that more after class (or meeting)?
Let’s not dive too deeply into that issue because it might distract us from today’s agenda.
Good thought. Does anyone want to collaborate to address that concern?
Here is what I know, and here is what I don’t know (Huston, 2009).

Avoid “teaching as telling.” Rather than lecturing, which requires great preparation and pressure to be the expert in the room, move toward new pedagogical models of facilitation which turn the teaching burden over to the students, such as jigsaw and gallery walk.
Know that you are not alone. It is plausible that nearly everyone in the room has felt this way at one point or another in their careers, even though they may not readily share these thoughts with others. Normalizing the feelings to yourself will start to defuse your anxiety.
Share the issue with others you trust. A mentor or even a small community of colleagues can collaboratively strategize about how to address the issue.
Recognize external factors that might contribute. Often people blame themselves for toxic situations which were created by outside circumstances. If the situation persists, consider declining future involvement to avoid setting yourself up for difficulties.

“Awareness is half the battle” really does apply to imposter syndrome. Through metacognition, you can conquer the self-defeating thoughts and behaviors that might prevent you from succeeding in your personal and professional life. Intentional self-monitoring of negative internal dialogue followed by practicing self-regulation through the simple strategies outlined above is the antidote to imposter syndrome. So next time you feel yourself break into a sweat (figuratively or literally), assume a Power Pose and leverage metacognition to triumph over your doubts!

Metacognition promotes success by helping us overcome self-defeating thoughts. Share on X
Resources:

Carney, D. R., Cuddy, A. J., & Yap, A. J. (2010). Power posing brief nonverbal displays affect neuroendocrine levels and risk tolerance. Psychological Science, 21(10), 1363-1368. doi: 10.1177/0956797610383437

Cuddy, A. (2012, October 1). Your body language shapes who you are [Video file]. Retrieved from https://www.ted.com/talks/amy_cuddy_your_body_language_shapes_who_you_are?language=en

Huston, T. (2009). Teaching What You Don’t Know. Cambridge, MA: Harvard University Press.

Kaplan, K. (2009). Unmasking the impostor. Nature, 459(21): 468-469. doi: 10.1038/nj7245-468a

Making sense of how I learn: Metacognitive capital and the first year university student

By Lodge and Larmar, This article focuses on how significant it is to encourage metacognitive processing as a means of increasing student retention, enhancing university engagement and lifelong learning.

Larmar, S. & Lodge, J. (2014). Making sense of how I learn: Metacognitive capital and the first year

university student. The International Journal of the First Year in Higher Education, 5(1). 93-105. doi:

10.5204/intjfyhe.v5i1.193

Lodge and Larmar article