Metacognitive Self-Assessment, Competence and Privilege

by Steven Fleisher, Ph.D., California State University Channel Islands

Recently I had students in several of my classes take the Science Literacy Concept Inventory (SLCI) including self-assessment (Nuhfer, et al., 2017). Science literacy addresses one’s understanding of science as a way of knowing about the physical world. This science literacy instrument also includes self-assessment measures that run parallel with the actual competency measures. Self-assessment skills are some of the most important of the metacognitive competencies. Since metacognition involves “thinking about thinking,” the question soon becomes, “but thinking about what?”

Dunlosky and Metcalfe (2009) framed the processes of metacognition across metacognitive knowledge, monitoring, and control. Metacognitive knowledge involves understanding how learning works and how to improve it. Monitoring involves self-assessment of one’s understanding, and control then involves any needed self-regulation. Self-assessment sits at the heart of metacognitive processes since it sets up and facilitates an internal conversation in the learner, for example “Am I understanding this material at the level of competency needed for my upcoming challenge?” This type of monitoring then positions the learner for any needed control or self-regulation, for instance “Do I need a change my focus, or maybe my learning strategy?” Further, self-assessment is affective in nature and is central to how learning works. From a biological perspective, learning involves the building and stabilizing of cognitive as well as affective neural networks. In other words, we not only learn about “stuff”, but if we engage our metacognition (specifically self-assessment in this instance), we are enhancing our learning to include knowing about “self” in relation to knowing about the material.

This Improve with Metacognition posting provides information that was shared with my students to help them see the value of self-assessing and for understanding its relationship with their developing competencies and issues of privilege. Privilege here is defined by factors that influence (advantage or disadvantage) aggregate measures of competence and self-assessment accuracy (Watson, et al., 2019). Those factors involved: (a) whether students were first-generation college students, (b) whether they were non-native English-language students, and (c) whether they had an interest in science.

The figures and tables below result from an analysis of approximately 170 students from my classes. The narrative addresses the relevance of each of the images.

Figure 1 shows the correlation between students’ actual SLCI scores and their self-assessment scores using Knowledge Survey items for each of the SLCI items (KSSLCI). This figure was used to show students that their self-assessments were indeed related to their developing competencies. In Figure 2, students could see how their results on the individual SLCI and KSSLCI items were tracking even more closely than in Figure 1, indicating a fairly strong relationship between their self-assessment scores and actual scores.

scatterplot graph of knowledge survey compared to SCLI scores — Figure 1. Correlation with best-fit line between actual competence measures via a Science Literacy Concept Inventory or SLCI (abscissa) and self-assessed ratings of competence (ordinate) via a knowledge survey of the inventory (KSSLCI) wherein students rate their competence to answer each of the 25 items on the inventory prior to taking the actual test.

scatter plot of SCLI scores and knowledge survey scores by question — Figure 2. Correlation with best-fit line between the group of all my students’ mean competence measures on each item of the Science Literacy Concept Inventory (abscissa) and their self-assessed ratings of competence on each item of the knowledge survey of the inventory (KSSLCI).

Figure 3 demonstrates the differences in science literacy scores and self-assessment scores among their different groups as defined by the number of science courses taken. Students could readily see the relationship between the number of science courses taken and improvement in science literacy. More importantly in this context, students could see that these groups had a significant sense of whether or not they knew the information, as indicated by the close overlapping of each pair of green and red diamonds. Students learn that larger numbers of participants can provide more confidence to where the true means actually lies. Also, I can show the meaning of variation differences within and between groups. In answering questions about how we know that more data would clarify relationships, I bring up an equivalent figure from our national database that shows the locations of the means within 99.9% confidence and the tight relationship between groups’ self-assessed competence and their demonstrated competence.

categorical plot by number of college science courses completed — Figure 3. Categorical plot of my students in five class sections grouped by their self-identified categories of how many college-level science courses that they have actually completed. Revealed here are the groups’ mean SLCI scores and their mean self-assessed ratings. Height of the green (SLCI scores) and red (KSSLCI self-assessments) diamonds reveals with 95% confidence that the actual mean lies within these vertical bounds.

Regarding Figure 4, it is always fun to show students that there’s no significant difference between males and females in science literacy competency. This information comes from the SLCI national database and is based on over 24,000 participants.

categorical plot by binary gender — Figure 4. Categorical plot from our large national database by self-identified binary gender categories shows no significant difference by gender in competence of understanding science as a way of knowing.

It is then interesting to show students in that, in their smaller sample (Figure 5), there is a difference between the science literacy scores of males and females. The perplexed looks on their faces are then addressed by the additional demographic data in Table 1 below.

categorical plot by binary gender for individual class — *Figure 5. Categorical plot of just my students by binary gender reveals a marginal difference between females and males, rather than the gender-neutral result shown in Fig. 4.*

In Table 1, students could see that higher science literacy scores for males in their group were not due to gender, but rather, were due to significantly higher numbers of English as a non-native language for females. In other words, the women in their group were certainly not less intelligent, but had substantial, additional challenges on their plates.

Table 1: percentages of male and female students as first generation, English and non-native speaker, and with respect to self-report interest to major in science

Students then become interested in discovering that the women demonstrated greater self-assessment accuracy than did the men, who tended to overestimate (Figure 6). I like to add here, “that’s why guys don’t ask for directions.” I can get away with saying that since I’m a guy. But more seriously, I point out that rather than simply saying women need to improve in their science learning, we might also want to help men improve in their self-assessment accuracy.

categorical plot by gender including self-assessment data — Figure 6. The categorical plot of SLCI scores (green diamonds) shown in Fig. 5 now adds the self-assessment data (red diamonds) of females and males. The trait of females to more accurately self-assess that appears in our class sample is also shown in our national data. Even small samples taken from our classrooms can yield surprising information.

In Figure 7, students could see there was a strong difference in science literacy scores between Caucasians and Hispanics in my classes. The information in Table 2 below was then essential for them to see. Explaining this ethnicity difference offers a wonderful discussion opportunity for students to understand not only the data but what it reveals is going on with others inside their classrooms.

*Figure 7. The categorical plot of SLCI scores by the two dominant ethnicities in my classroom. My campus is a Hispanic Serving Institution (HSI). The differences shown are statistically significant.*

Table 2 showed that the higher science literacy scores in this sample were not simply due to ethnicity but were impacted by significantly greater numbers of first-generation students and English as a non-native language between groups. These students are not dumb but do not have the benefits in this context of having had a history of education speak in their homes and are navigating issues of English language learning.

Table 2: percentage of white and hispanic students who report to be first generation students, English as non-native speakers, and interested in majoring in science.

When shown Figure 8, which includes self-assessment scores as well as SLCI scores, students were interested to see that both groups demonstrated fairly accurate self-assessment skills, but that Hispanics had even greater self-assessment accuracy than their Caucasian colleagues. Watson et. al (2019) noted that strong self-assessment accuracy for minority groups comes about from a need for being understandably cautious.

categorical plot by ethnicity and including self-assessment — Figure 8. The categorical plot of SLCI scores and self-assessed competence ratings for the two dominant ethnicities in my classroom. Groups’ collective feelings of competence, on average, are close to their actual competence. Explaining these results offered a wonderful discussion opportunity for students.

Figure 9 shows students that self-assessment is real. In seeing that most of their peers fall within an adequate range of self-assessment accuracy (between +/- 20 percentage points), students begin to see the value of putting effort into developing their own self-assessment skills. In general, results from this group of my students are similar to those we get from our larger national database (See our earlier blog post, Paired Self-Assessment—Competence Measures of Academic Ranks Offer a Unique Assessment of Education.)

distribution of self-assessment accuracy for individual course — Figure 9. The distribution of self-assessment accuracy of my students in percentage points (ppts) as measured by individuals’ differences between their self-assessed competence by knowledge survey and their actual competence on the Concept inventory.

Figure 10 below gave me the opportunity to show students the relationship between their predicted item-by-item self-assessment scores (Figure 9) and their postdicted global self-assessment scores. Most of the scores fall between +/- 20 percentage points, indicating good to adequate self-assessment. In other words, once students know what a challenge involves, they are pretty good at self-assessing their competency.

distribution of self-assessment accuracy for individual course after taking SCLI — Figure 10. The distribution of self-assessment accuracy of my students in percentage points (ppts) as measured by individuals’ differences between their postdicted ratings of competence after taking the SLCI and their actual scores of competence on the Inventory. In general, my students’ results are similar in self-assessment measured in both ways.

In order to help students further develop their self-assessment skills and awareness, I encourage them to write down how they feel they did on tests and papers before turning them in (postdicted global self-assessment). Then they can compare their predictions with their actual results in order to fine-tune their internal self-assessment radars. I find that an excellent class discussion question is “Can students self-assess their competence?” Afterward, reviewing the above graphics and results becomes especially relevant. We also review self-assessment as a core metacognitive skill that ties to an understanding of learning and how to improve it, the development of self-efficacy, and how to monitor their developing competencies and control their cognitive strategies.

References

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

Nuhfer, E., Fleisher, S., Cogan, C., Wirth, K., & Gaze, E. (2017). How Random Noise and a Graphical Convention Subverted Behavioral Scientists’ Explanations of Self-Assessment Data: Numeracy Underlies Better Alternatives. Numeracy, Vol 10, Issue 1, Article 4. DOI: http://dx.doi.org/10.5038/1936-4660.10.1.4

Watson, R., Nuhfer, E., Nicholas Moon, K., Fleisher, S., Walter, P., Wirth, K., Cogan, C., Wangeline, A., & Gaze, E. (2019). Paired Measures of Competence and Confidence Illuminate Impacts of Privilege on College Students. Numeracy, Vol 12, Issue 2, Article 2. DOI: https://doi.org/10.5038/1936-4660.12.2.2

Metacognition and Teacher-Student-Curriculum Relationships

by Steven Fleisher, Ph.D., California State University Channel Islands

I have heard many express that teacher-student relationships have nothing in common with families. But while teacher-student relationships are best described as collegial, at least within higher-education, this author believes that much can be learned from family theories and research. In particular, family research provides insights into how to support the development of trust in this context rather than relationships based principally on compliance. In other words, a classroom “is” a family, whether it’s a good one or a bad one. In this posting, we will explore metacognitive processes involved in building and maintaining stable relationships between students and the curriculum, teachers and the curriculum, and between teachers and students.

Family systems theory (Kerr & Bowen, 1988), though originally developed for clinical practice, offers crucial insights into not only teacher-student relationships but teaching and learning as well (Harrison, 2011). While there are many interlocking principles within family systems theory, we will concentrate on emotional stability, differentiation of self, and triangles.

The above triangle provides a representation for the following relationships: students-curriculum, teacher-curriculum, and teacher-students. Although any effective pedagogy would work for this discussion, we will focus specifically on the usefulness of knowledge surveys in this context (http://elixr.merlot.org/assessment-evaluation/knowledge-surveys/knowledge-surveys2) and their role in building metacognitive self-assessment skills.[1] Thus, what are some of the metacognitive processes involved in the relationships on each leg of our triangle? And, what are some of the metacognitive processes that would support those relationships in becoming increasingly stable?

Student-Curriculum Relationships

Along one leg of the triangle, students would increase the stability of their relationships with the curriculum as a function of becoming ever more aware of their learning processes. Regarding the use of knowledge surveys, students would self-assess their confidence to respond to given challenges, compare those responses with their developed competencies, and follow with reflective exercises to discover and understand any gaps between the two. As their self-assessment accuracy improves, their self-regulation skills would improve as well, i.e., adjusting, modifying, or deepening learning strategies or efforts as needed. So, the more students are aware of competencies in the curriculum and the more aware they are of their progress towards those competencies, the better off students will be.

As part of a course, instructors can also guide students in exploring how the material is useful to them personally. Activities can be designed to support exploration and discovery of ways in which course material relates, for example, to career interests, personal growth, interdisciplinary objectives, fostering of purpose, etc. In so doing, the relationships students have with the material can gain greater stability. Ertmer and Newby (1996) noted that expertise in learning involves becoming “strategic, self-regulated, and reflective”, and by bringing these types of exercises into the course, students are supported in the development of all these competencies.

Teacher-Curriculum Relationships

These relationships involve teachers becoming more aware of their practices, their student’s learning, and the connection between their practices and their student’s learning. In other words, the teacher is trying to ensure fit between student understanding and curriculum. Regarding knowledge surveys, teachers would know they are providing a pedagogical tool that supports learning and offers needed visibility for students.

In addition, once teachers have laid out course content in their knowledge surveys, they can look ahead and anticipate which learning strategies would be the best match for upcoming material. Realizing ahead of time the benefits of, let’s say, using structured group work for a particular learning module, teachers could prepare themselves and their students for that type of activity.

Teacher-Student Relationships

These relationships involve the potential for the development of trust. When trust develops in a classroom, students not only know what the expectations involve but are set more at ease to explore creatively their understanding and ways of understanding the material. For instance, students may well become aware of the genuine and honest help being provided by chosen learning strategies. Knowledge surveys are particularly useful in this regard as students have a roadmap for the course and a tool structured to facilitate the improvement of their learning skills.

Teachers also have an interpersonal role in supporting the development of student trust. Family systems theory (Bowen & Kerr, 1988) holds that we all vary in our levels of self-differentiation, which involves how much we, literally, realize that we are separate from others, especially during emotional conflict. In other words, people vary in their abilities to manage emotional reactivity (founded in anxiety) with being able to use one’s intellect to compose chosen and valued responses. Harrison (2011), in applying these principles in a classroom, noted that when teachers are aware of becoming emotionally reactivity (i.e., defensive), but are also aware of using their intellect, as best as possible, to manage the situation (i.e., remaining thoughtful and unbiased in their interactions with students), they are supporting emotional stability and trust.

Kerr and Bowen (1988) also reported that self-differentiation involves distinguishing between thoughts and feelings. This principle gives us another metacognitive tool. When we are aware, for example, that others do not “make” us feel a certain way (i.e., frustrated), but that it involves also our thinking (i.e., students are just being lazy), this affects our ability to manage reactivity. If we are aware of becoming reactive, and aware of distinguishing thoughts and feelings, we can notice and reframe our thoughts (i.e., students are just doing what they need to do), and validate and own our emotions (i.e., okay I’m frustrated), then we are better positioned to respond in ways that attune to our needs as well as those of our students. In this way, we would increase our level of self-differentiation by moving toward less blaming and more autonomy.

Final Note

Kerr and Bowen (1988) also said that supporting stability along all the relationships represented by our triangle not only increases the emotional stability of the system, but provides a cushion for the naturally arising instabilities along individual legs of the triangle. This presence of this stability also serves to further enhance the impact of effective pedagogies. So, when teachers are aware of maintaining the efficacy of their learning strategies, and are aware of applying the above principles of self-differentiation, i.e. engaging in metacognitive instruction, they are better positioned to be responsive and attuned to the needs of their students, thus supporting stability, trust, and improved learning.

References

Ertmer, P. A. & Newby, T. J. (1996). The expert learner: Strategic, self-regulated, and reflective. Instructional Science, 24(1), 1-24. Retrieved from https://link.springer.com/journal/11251

Harrison, V. A. (2011). Live learning: Differentiation of self as the basis for learning. In O.C. Bregman & C. M. White (Eds.), Bringing systems thinking to life: Expanding the horizons for Bowen family systems theory (pp. 75-87). New York, NY: Routledge.

Kerr, M. E. & Bowen, M. (1988). Family evaluation: An approach based on Bowen theory. New York, NY: W.W. Norton & Company.

Image from: https://www.slideshare.net/heatherpanda/essay-2-for-teaching-course-4

[1] Knowledge surveys are comprised of a detailed listing of all learning outcomes for a course (perhaps 150-250 items). Each item begins with an affective root (“I can…”) followed by a cognitive or ability challenge expressed in measurable terms (“…describe at least three functions of the pituitary gland.”). These surveys provide students with a roadmap for the course and a tool structured for building their confidence and accuracy in learning skills.

Positive Affective Environments and Self-Regulation

by Steven Fleisher at CSU Channel Islands

Although challenging at times, providing for positive affective experiences are necessary for student metacognitive and self-regulated learning. In a classroom, caring environments are established when teachers not only provide structure, but also attune to the needs and concerns of their students. As such, effective teachers establish those environments of safety and trust, as opposed to merely environments of compliance (Fleisher, 2006). When trust is experienced, learning is facilitated through the mechanisms of autonomy support as students discover how the academic information best suits their needs. In other words, students are supported in moving toward greater intrinsic (as opposed to extrinsic) motivation and self-regulation, and ultimately enhanced learning and success (Deci & Ryan, 2002; Pintrich, 2003).

Autonomy and Self-Regulation

In an academic context, autonomy refers to students knowing their choices and alternatives and self-initiating their efforts in relation to those alternatives. For Deci and Ryan (1985, 2002), a strong sense of autonomy within a particular academic task would be synonymous with being intrinsically motivated and, thus, intrinsically self-regulated. On the other hand, a low sense of autonomy within a particular academic context would be synonymous with being extrinsically motivated and self-regulated. Students with a low sense of autonomy might say, “You just want us to do what you want, it’s never about us,” while students with a strong sense of autonomy might say, “We can see how this information may be useful someday.” The non-autonomous students feel controlled, whereas the autonomous students know they are in charge of their choices and efforts.

Even more relevant to the classroom, Pintrich (2003) reported that the more intrinsically motivated students have mastery goal-orientations (a focus on effort and effective learning strategy use) as opposed to primarily performance goal-orientations (actually a focus on defending one’s ability). These two positions are best understood under conditions of failure. Performance-orientated students see failure as pointing out their innate inabilities, whereas mastery-oriented students see failure as an opportunity to reevaluate and reapply their efforts and strategies as they build their abilities. Thus, in the long run, mastery-oriented students end up “performing” the best academically.

The extrinsically motivated students perceive that the teacher is in charge, and not themselves, as to whether or not they are rewarded for their work. This extrinsic orientation may facilitate performance, however, it can backfire. These students can become unwilling to put forth a full effort for fear of failure or judgment. These students feel a compulsion for performance, which can result in a refusal to try to meet goals. They may come to prefer unchallenging courses, fail, or drop out entirely. On the other hand, students with intrinsic goal-orientations realize that they are in charge of their reasons for acting. Metacognitively, they are aware of their alternatives and strategies and self-regulate accordingly as they apply the necessary effort toward their learning tasks. These students would sense that the classroom provided an environment for exploring the subject matter in relevant and meaningful ways and they would identify how and where to best apply their learning efforts.

Strategies for the Classroom

As with autonomy (minimum to maximum), motivation and self-regulation exist on a continuum (extrinsic to intrinsic), as opposed to existing at one end or the other. Here are a couple of instructional strategies that I have found that support students in their movement toward greater autonomy and intrinsic motivation and self-regulation.

Knowledge surveys, for example, offer a course tool for organizing content learning and assessing student intellectual development (Nuhfer & Knipp, 2003). These surveys consist of questions that represent the breadth and depth of the course, including the main concepts, the related content information, and the different levels of reasoning to be practiced and assessed. I have found that using knowledge surveys to disclose to student where a course is going and why helps them take charge of their learning. This type of transparency helps students discover ways in which their learning efforts are effective.

Cooperative learning strategies (Millis & Cottell, 1998) provide an ideal counterpart to knowledge surveys. Cooperative learning (for instance, working in groups or teaching your neighbor) offers both positive learning and positive affective experiences. These learning experiences, between students and between teachers and students support the development of autonomy, as well as intrinsic motivation and self-regulation. For example, when students work together effectively in applications of course content, they come to see through one another’s perspectives the relevance of the material, while gaining competency as well as insights into how to gain that competency. When students are aware, by way of the knowledge surveys, of the course content and levels of reasoning required, and when these competencies and related learning strategies are practiced, reflected upon, and attained, learning and metacognitive learning are engaged.

References

Deci, E. L. & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum Press.

Deci, E. L. & Ryan, R. M. (2002). Handbook of self-determination research. Rochester, NY: The University of Rochester Press.

Fleisher, S. C. (2006). Intrinsic self-regulation in the classroom. Academic Exchange Quarterly, 10(4), 199-204.

Millis, B. J. & Cottell, P. G. (1998). Cooperative learning for higher education faculty. American Council on Education: Oryx Press.

Nuhfer, E. & Knipp, D. (2003). The knowledge survey: A tool for all reasons. To Improve the Academy, 21, 59-78.

Pintrich, P. R. (2003). Motivation and classroom learning. In W. M. Reynolds & G. E. Miller (Eds.), Handbook of psychology: Educational psychology, Volume 7. Hoboken, NJ: John Wiley & Sons.

Self-Assessment, It’s A Good Thing To Do

by Stephen Fleisher, CSU Channel Islands

McMillan and Hearn (2008) stated persuasively that:

In the current era of standards-based education, student self-assessment stands alone in its promise of improved student motivation and engagement, and learning. Correctly implemented, student self-assessment can promote intrinsic motivation, internally controlled effort, a mastery goal orientation, and more meaningful learning (p. 40).

In her study of three meta-analyses of medical students’ self-assessment, Blanch-Hartigan (2011) reported that self-assessments did prove to be fairly accurate, as well as improving in later years of study. She noted that if we want to increase our understanding of self-assessment and facilitate its improvement, we need to attend to a few matters. To understand the causes of over- and underestimation, we need to address direction in our analyses (using paired comparisons) along with our correlational studies. We need also to examine key moderators affecting self-assessment accuracy, for instance “how students are being inaccurate and who is inaccurate” (p. 8). Further, the wording and alignment of our self-assessment questions in relation to the criteria and nature of our performance questions are essential to the accuracy of understanding these relationships.

When we establish strong and clear relationships between our self-assessment and performance questions for our students, we facilitate their use of metacognitive monitoring (self-assessment, and attunement to progress and achievement), metacognitive knowledge (understanding how their learning works and how to improve it), and metacognitive control (changing efforts, strategies or actions when required). As instructors, we can then also provide guidance when performance problems occur, reflecting on students’ applications and abilities with their metacognitive monitoring, knowledge, and control.

Self-Assessment and Self-Regulated Learning

For Pintrich (2000), self-regulating learners set goals, and activate prior cognitive and metacognitive knowledge. These goals then serve to establish criteria against which students can self-assess, self-monitor, and self-adjust their learning and learning efforts. In monitoring their learning process, skillful learners make judgments about how well they are learning the material, and eventually they become better able to predict future performance. These students can attune to discrepancies between their goals and their progress, and can make adjustments in learning strategies for memory, problem solving, and reasoning. Additionally, skillful learners tend to attribute low performance to low effort or ineffective use of learning strategies, whereas less skillful learners tend to attribute low performance to an over-generalized lack of ability or to extrinsic things like teacher ability or unfair exams. The importance of the more adaptive attributions of the aforementioned skillful learners is that these points of view are associated with deeper learning rather than surface learning, positive affective experiences, improved self-efficacy, and greater persistence.

Regarding motivational and affective experiences, self-regulating learners adjust their motivational beliefs in relation to their values and interests. Engagement improves when students are interested in and value the course material. Importantly, student motivational beliefs are set in motion early in the learning process, and it is here that instructional skills are most valuable. Regarding self-regulation of behavior, skillful learners see themselves as in charge of their time, tasks, and attention. They know their choices, they self-initiate their actions and efforts, and they know how and when to delay gratification. As well, these learners are inclined to choose challenging tasks rather than avoid them, and they know how to persist (Pintrich, 2000).

McMillan and Hearn (2008) summarize the role and importance of self-assessment:

When students set goals that aid their improved understanding, and then identify criteria, self-evaluate their progress toward learning, reflect on their learning, and generate strategies for more learning, they will show improved performance with meaningful motivation. Surely, those steps will accomplish two important goals—improved student self-efficacy and confidence to learn—as well as high scores on accountability tests (p. 48).

As a teacher, I see one of my objectives being to discover ways to encourage the development of these intellectual tools and methods of thinking in my own students. For example, in one of my most successful courses, a colleague and I worked at great length to create a full set of specific course learning outcomes (several per chapter, plus competencies we cared about personally, for instance, life-long learning). These course outcomes were all established and set into alignment with the published student learning outcomes for the course. Lastly, homework, lectures, class activities, individual and group assignments, plus formative and summative assessments were created and aligned. By the end of this course, students not only have gained knowledge about psychology, but tend to be pleasantly surprised to have learned about their own learning.

References

Blanch-Hartigan, D. (2011). Medical students’ self-assessment of performance: Results from three meta-analyses. Patient Education and Counseling, 84, 3-9.

McMillan, J. H., & Hearn, J. (2008). Student self-assessment: The key to stronger student motivation and higher achievement. Educational Horizons, 87(1), 40-49. http://files.eric.ed.gov/fulltext/EJ815370.pdf

Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.) Handbook of self-regulation. San Diego, CA: Academic.

Metacognition, Self-Regulation, and Trust

by Dr. Steven Fleisher, CSU Channel Islands, Department of Psychology

Early Foundations

I’ve been thinking lately about my journey through doctoral work, which began with studies in Educational Psychology. I was fortunate to be selected by my Dean, Robert Calfee, Graduate School of Education at University of California Riverside, to administer his national and state grants in standards, assessment, and science and technology education. It was there that I began researching self-regulated learning.

Self-Regulated Learning

Just before starting that work, I had completed a Masters Degree in Marriage and Family Counseling, so I was thrilled to discover the relevance of the self-regulation literature. For example, I found it interesting that self-regulation studies began back in the 1960s examining the development of self-control in children. Back then the framework that evolved for self-regulation involved the interaction of personal, behavioral, and environmental factors. Later research in self-regulation focused on motivation, health, mental health, physical skills, career development, decision-making, and, most notable for our purposes, academic performance and success (Zimmerman, 1990), and became known as self-regulated learning.

Since the mid-1980s, self-regulated learning researchers have studied the question: How do students progress toward mastery of their own learning? Pintrich (2000) noted that self-regulated learning involved “an active, constructive process whereby learners set goals for their learning and then attempt to monitor, regulate, and control their cognition, motivation, and behavior, guided and constrained by their goals and the contextual features in the environment” (p. 453). Zimmerman (2001) then established that, “Students are self-regulated to the degree that they are metacognitively, motivationally, and behaviorally active participants in their own learning process” (p. 5). Thus, self-regulated learning theorists believe that learning requires students to become proactive and self-engaged in their learning, and that learning does not happen to them, but by them (see also Leamnson, 1999).

Next Steps

And then everything changed for me. My Dean invited Dr. Bruce Alberts, then President of the National Academy of Sciences, to come to our campus and lecture on science and technology education. Naturally, as Calfee’s Graduate Student Researcher, I asked “Bruce” what he recommended for bringing my research in self-regulated learning to the forefront. His recommendation was to study the, then understudied, role and importance of the teacher-student relationship. Though it required changing doctoral programs to accommodate this recommendation, I did it, adding a Doctorate in Clinical Psychology to several years of coursework in Educational Psychology.

Teacher-Student Relationships

Well, enough about me. It turns out that effective teacher-student relationships provide the foundation from which trust and autonomy develop (I am skipping a lengthy discussion of the psychological principles involved). Suffice it to say, where clear structures are in place (i.e., standards) as well as support, social connections, and the space for trust to develop, students have increased opportunities for exploring how their studies are personally meaningful and supportive of their autonomy, thereby taking charge of their learning.

Additionally, when we examine a continuum of extrinsic to intrinsic motivation, we find the same principles involved as with a scale showing minimum to maximum autonomy, bringing us back to self-regulated learning. Pintrich (2000) included the role of motivation in his foundations for self-regulated learning. Specifically, he reported that a goal orientation toward performance arises when students are motivated extrinsically (i.e., focused on ability as compared to others); however, a goal orientation toward mastery occurs when students are motivated more intrinsically (i.e., focused on effort and learning that is meaningful to them).

The above concepts can help us define our roles as teachers. For instance, we are doing our jobs well when we choose and enact instructional strategies that not only communicate clearly our structures and standards but also provide needed instructional support. I know that when I use knowledge surveys, for example, in building a course and for disclosing to my students the direction and depth of our academic journey together, and support them in taking meaningful ownership of the material, I’m helping their development of metacognitive skill and autonomous self-regulated learning. We teachers can help improve our students’ experience of learning. For them, learning in order to get the grades pales in comparison to learning a subject that engages their curiosity, along with investigative and social skills that will last a lifetime.

References

Leamnson, R. (1999). Thinking about teaching and learning: Developing habits of learning with first year college and university students. Sterling, VA: Stylus.

Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.) Handbook of self-regulation. San Diego, CA: Academic.

Zimmerman, B. J. (1990). Self-regulating academic learning and achievement: The emergence of a social cognitive perspective. Educational Psychology Review, 2(2), 173-201.

Zimmerman, B. J. (2001). Theories of self-regulated learning and academic achievement: An overview and analysis. In B. J. Zimmerman & D. H. Schunk (Eds.) Self-regulated learning and academic achievement: Theoretical perspectives (2e). New York: Lawrence Erlbaum.

Metacognition and Reflective Thinking

By Steven C. Fleisher, California State University Channel Islands

Imagine that we are reading an assignment. As we read, do we think: “How long will this take?” “Will this be on the test?” If so, try this instead. Presume that we are reading the article as preparation for meeting later with an important person such as our supervisor to discuss the article. How would this situation change the questions we ask ourselves? Such thinking can make us aware of what constitutes satisfactory mastery of knowing and how to achieve it.

Think back for a moment to learning a psychomotor skill, such as learning to ride a bicycle. It is normal to master that skill with normal innate balance and strength. We might think: “That’s all there is to it.” However, watching cyclists in a serious bicycle race or triathlon, reveals that reliance only on innate ability cannot produce that kind of performance. That level of expertise requires learning to pedal with cadence, to deliver equal power from both legs, use the gearing appropriately, exploit position within a group of racers and pace oneself relative to challenges. Untrained innate ability can rarely get us far in comparison to the results of informed training.

The same is true in learning. Metacognitive skills (learnable skills) enhance academic performance. People with metacognitive skill will usually outperform others who lack such skill, even others with greater innate intelligence (natural ability). Metacognitive training requires developing three strengths: 1) metacognitive knowledge, 2) metacognitive monitoring, and 3) metacognitive control.

Metacognitive knowledge refers to our understanding about how learning operates and how to improve our learning. We should have enough of this knowledge to articulate how we learn best. For example, we can know when it is best for us to write a reflection about a reading in order to enhance our learning. We should be alert to our misconceptions about how our learning works. When we learn that cramming is not always the best way to study (Believe it!), we must give that up and operate with a better proven practice.

Metacognitive monitoring refers to developed ability to monitor our progress and achievement accurately. For example, self-assessment is a kind of metacognitive monitoring. We should know when we truly understand what we are reading and assess if we are making progress toward solving a problem. When we become accurate and proficient in self-assessment, we are much better informed. We can see when we have mastered certain material well enough, and when we have not.

Metacognitive control. This competency involves having the discipline and control needed to make the best decisions in our own interests. This aspect of metacognition includes acting on changing our efforts or learning strategies, or taking action to recruit help when indicated.

Putting it together. When we engage in metacognitive reflection, we can ask ourselves, for example, “What did we just learn?” “What was problematic, and why?” “What was easy, and why?” “How can we apply what we just learned?” Further, when we gain metacognitive skill, we begin to internalize habits of learning that better establish and stabilize beneficial neural connections.

Reflective Exercises for Students:

Metacognitive knowledge. Consider three learning challenges: acquiring knowledge, acquiring a skill, or making an evidence-based decision. How might the approaches needed to succeed in each of these three separate challenges differ?
Metacognitive monitoring. After you complete your next assignment or project, rate your resultant state of mastery on the following scale of three points: 0 = I have no confidence that I made any meaningful progress toward mastery; 1 = I clearly perceived some gain of mastery, but I need to get farther; 2 = I am currently highly confident that I understand and can meet this challenge.
Next, see if your self-rating causes you to take action such as to re-study the material or to seek help from a peer or an instructor in order to achieve more competence and higher confidence. A critical test will be whether your awareness from monitoring was able to trigger your taking action. Another will come in time. It will be whether your self-assessment proved accurate.
Metacognitive control. To develop better understanding of this, recall an example from life when you made a poor decision that proved to produce a result that you did not desire or that was not in your interests. How did living this experience equip you to better deal with a similar or related life challenge?

References

Chew, S. L. (2010). Improving classroom performance by challenging student misconceptions about learning. Association for Psychological Science: Observer, Vol. 23, No. 4. http://psychologicalscience.org/observer/getArticle.cfm?id=2666

Dunlosky, J. and Metcalf, J. (2009). Metacognition. Thousand Oaks, CA: Sage.

Leamnson, R. N. (1999). Thinking about teaching and learning: Developing habits of learning with first year college and university students. Sterling, VA: Stylus.

Pintrich, P. R. (2002). The role of metacognitive knowledge in learning, teaching, and assessing. Theory Into Practice, 41(4), 219-225.

Wirth, K. (2010). The role of metacognition in teaching Geoscience. Science Education Resource Center, Macalester College. http://serc.carleton.edu/NAGTWorkshops/metacognition/activities/27560.html