The accuracy of a student's self-perception and their ability to achieve learning goals
Dasha Bunks
Liberty University
PSYC 775 - Teaching of Psychology
Dr. Winn
2022
The accuracy of a student's self-perception and their ability to achieve learning goals
Most teachers are all too familiar with the challenge of helping students who remain baffled about why
they are not doing well in class. “But I studied so hard!” students might say. “I don’t understand how I
could have gotten such a low test score!” The frequency of such concerns among students highlights the
difficulty that students face when trying to understand whether they have truly mastered learning a
topic, when they prepared themselves well for exams, and when their learning might fall short. Effective
learning requires metacognition — the process of reflecting upon which learning concepts one has
already mastered, what still needs to be learned, and how to best approach the task of learning. Within
educational contexts, metacognitive perceptions are defined as students’ beliefs about their learning
process, including the degree to which they have mastered learning tasks. Students’ metacognitive
perceptions reflect the degree to which they believe that they understand lesson material and have
mastered learning different concepts or tasks. In this chapter, we review research suggesting that
students’ metacognitive perceptions are often error-prone. We discuss factors that introduce error into
students’ perceptions and offer concrete suggestions designed to help encourage greater accuracy in
students’ perceptions and, in turn, to facilitate student learning.
Accuracy of Metacognitive Perceptions
To the degree that students hold accurate metacognitive perceptions during learning, they can make
wise decisions about how best to allocate their time studying and which strategies to use in order to
learn course material. However, it can be very challenging for students to have true insight into the
quality of their understanding and learning. Laboratory-based research suggests that there are some
learning tasks about which students make quite accurate predictions about their level of learning and
about the degree to which they will perform well on tests of their knowledge (e.g., King, Zechmeister, &
Shaughnessy, 1980; Leonesio & Nelson, 1990; Nelson & Dunlosky, 1991). However, for many learning
tasks, students’ metacognitive perceptions tend to be error-prone. The degree to which students’
perceptions are accurate depends on the topic being studied and the students’ level of ability, as well as
a number of other factors. On average, however, research suggests that students’ metacognitive
perceptions tend to be plagued by error across a variety of academic subjects (for review, see Dunning,
2005). Often students are wildly inaccurate about a host of abilities and personal qualities (e.g., 143
Fischhoff, Slovic, & Lichtenstein, 1977). In the classroom, many students tend to hold overly positive
impressions of their current level of knowledge (e.g., Ehrlinger & Dunning, 2003), the quality of their
performance on academic tests (e.g., Ehrlinger, Johnson, Banner, Dunning, & Kruger, 2008), and the
speed with which they can master and complete future academic tasks (e.g., Buehler, Griffin, & Ross,
1994). One might wonder whether there is any reason to be concerned about overconfidence in
students. After all, confidence is often considered a positive trait. Indeed, research suggests that
students who are confident about their ability to succeed in school tend to perform better on academic
tests than those with less confidence (e.g., Zimmerman, Bandura, & Martinez-Pons, 1992). That said,
negative consequences also stem from being too confident in the classroom. Students who are
overconfident about their ability to succeed in college end up feeling more disconnected and
disillusioned than those with more modest expectations (Robins & Pals, 2001). Overconfidence can also
leave students with mistaken impressions that they are fully prepared for tests and no longer need to
study (Metcalfe & Finn, 2008; Metcalfe & Kornell, 2005). People who have relatively accurate
perceptions regarding their progress in learning tend to use more effective study habits and perform
better on tests than do those with more error-prone views of their knowledge (Thiede, Anderson, &
Therriault, 2003). Overconfidence can be particularly troubling when it exists in those who are truly
performing poorly in the classroom. Top students do often hold overly positive views of their learning
but this seems far less troubling than overconfidence among those with failing test scores. Tragically, it
tends to be the poor students, however, who demonstrate the most overconfidence (Dunning, Johnson,
Ehrlinger, Kruger, 2003; Ehrlinger et al., 2008).
Consider, for example, one study in which college students who had just completed a test in their
psychology course were then asked to estimate how well they had performed. Overconfidence was
common in students’ performance estimates. However, those students scoring in the bottom quartile
were, by far, the most overconfident students in the class. The actual performance of these students
averaged in the 12th percentile in the class. However, these students estimated that they had
performed in the 61st percentile on average (Ehrlinger et al., 2008). They were vastly overconfident
about the quality of their test performance. This seems almost unfair.
Certainly poor students have the most room for improvement and, thus, the greatest need for
understanding when improvement is necessary. However, these students lack knowledge necessary to
know when they have offered answers that are incorrect. Thus, bottom performers in the class struggle
both with their poor grades and with particularly error-prone, overconfident metacognitive perceptions
about their progress in learning. Although overconfidence creates troubling roadblocks to learning, the
opposite issue — under confidence — comes with its own costs in the classroom.
Perhaps the most noteworthy cases in which under confidence impede learning are in the fields of
science, technology, engineering, and math (STEM). A wealth of research suggests that women in
general and, in particular, women who are Black, Hispanic, or American Indian are vastly
underrepresented in STEM fields (e.g., NSB, 2006; NSF, 2009). For example, women account for less than
20% of undergraduate computer science and engineering degrees and less than 25% of the doctoral
degrees awarded in these fields (NSF, 2009). Multiple factors combine to create and perpetuate the lack
of diversity in STEM fields. However, underconfidence is among the factors that dissuade women and
underrepresented minorities from participating in these fields. Women and underrepresented minority
students tend to report lower confidence in their abilities for STEM subjects compared to white men
(Eccles, Barber, Jozefowicz, Malenchuk, & Vida, 1999; Ehrlinger & Dunning, 2003).
This lack of confidence then leads these students to perceive that they are performing poorly in their
classes and, ultimately, to avoid future opportunities related to STEM. For example, Ehrlinger and
Dunning (2003) demonstrated that a tendency for women to hold less confidence in their scientific
ability, compared to men, led women to be less confident about their performance on a science test.
This difference in confidence emerged even though men and women performed equally well on the
test! Further, women’s comparative lack of confidence led them to show less interest in a future science
opportunity than men in the study (see also Ehrlinger, 2008; Goodman et al., 2002). Thus, even talented
students can end up withdrawing from important fields because they lack the confidence necessary to
understand how much they could accomplish.
How Metacognitive Monitoring Relates to Learning
One important reason that students might hold overly positive or, in the case of STEM fields sometimes
negative, metacognitive perceptions is that they do not always strive to hold accurate views of their
progress in the classroom. Students differ in the degree to which they actively engage in metacognitive
monitoring, or monitoring whether and how well they have mastered learning tasks. Individuals who
engage in metacognitive monitoring are more likely to discover gaps in their understanding than are
those who do not monitor.
Metacognitive monitoring allows students to identify which concepts they have mastered, which they
want to work on more, and which concepts might be so difficult that it isn’t worth the time and effort
necessary to truly learn them (Metcalfe & Kornell, 2005; Kornell & Metcalfe, 2006). Past research
suggests that students can draw upon this metacognitive monitoring information when deciding
whether and when to engage in learning tasks (Ehrlinger & Dunning, 2003), how to focus their study
time (e.g., Kornell & Bjork, 2007; Metcalfe & Kornell, 2005), and which strategies to use (e.g, Kornell &
Metcalfe, 2006; Thiede & Dunlosky, 1999).
Metacognitive monitoring has important benefits for student learning. Those who engage in monitoring
perform better on measures of learning than those less likely to monitor. Students who do not monitor
are likely able to maintain confidence in their skill level even when it is not merited. By engaging in
metacognitive monitoring, students take an active role in the process of learning (Chen, 2002; Schraw &
Dennison, 1994; Vrugt & Oort, 2008; Yukselturk & Bulut, 2007). In addition, study strategies that entail
metacognitive monitoring are particularly effective in advancing learning. For example, quizzing oneself
on course material (Roediger & Karpicke, 2006; see Pyc, Agarwal, & Roediger, this volume, for a review)
and summarizing key points from text passages (Thiede, Anderson, & Therriault, 2003) lead to
improvements in learning.
Further, students who are encouraged to monitor their understanding and knowledge show better
student outcomes than those who are not. Students who received instruction in metacognitive
monitoring (i.e., were asked to make confidence judgments) engaged more fully in the classroom,
remembered more course material, and performed better on subject-matter tests than control
participants (Georghiades, 2000). In addition, students required to make judgments of learning in
conjunction with readings in their physics class demonstrated significantly higher text comprehension
than control participants (Koch, 2001).
It is worth noting, however, that the degree to which metacognitive monitoring leads to improvements
in student learning depends heavily on the degree to which students are able to accurately evaluate
their level of knowledge and understanding (Dunlosky, Hertzog, Kennedy, & Theide, 2005). To the
degree that students are able to accurately identify what they have not fully mastered, they can
effectively direct their efforts toward improving mastery (e.g., Kornell & Bjork, 2007; Metcalfe & Finn,
2008; Thiede et al., 2003) and perform better on tests (Dunlosky & Rawson, In Press).
Recommendations for Educators
There are at least two broad strategies that educators can utilize in order to encourage improved
metacognition in the classroom. First, we will discuss how teachers can encourage students to utilize
learning strategies that have been demonstrated to help them gain more accurate perceptions of their
learning and, in turn, to perform better on tests. Finally, we will discuss the ways in which teachers can
encourage positive mindsets that minimize the degree to which students feel threatened by difficulty in
the classroom and, as such, are more open to understanding how they can improve their success in
learning.
Encouraging metacognitive monitoring through study strategies
Students serve as salient reminders that they do not always hold accurate perceptions of their learning.
Further conversation with these frustrated students also tends to reveal other ways in which they lack
insight in the classroom. Often, they fail to understand the best strategies for improving their level of
learning. It is especially important that students learn more effective strategies so that they can better
direct their own learning and have more accurate insight into their learning processes. Students do not
always have good insight into what specific strategies they need to use to improve their learning (e.g.,
Pyc & Dunlosky, 2010), so it is important for educators to explicitly encourage these strategies or use
them when appropriate as part of a classroom setting. For example, students may feel like cramming all
of their studying into a long session just before the test is an effective strategy for learning information.
Indeed, this type of cramming before an exam can leave students feeling very confident of their
knowledge (Baddeley & Longman, 1978; Bjork, 1999; Simon & Bjork, 2001). However, actual sustained
learning is much more likely to result from spacing out one’s study (Bahrick, 1979; Schmidt & Bjork,
1992; see Carpenter, this volume, for a review). Students are particularly likely to mass their study
together into single study sessions when working on difficult material (e.g., Pyc & Dunlosky, 2010).
However, spacing out one’s study over time can hold particularly strong benefits for learning difficult
concepts (Cuddy & Jacoby, 1982). Another common learning strategy that can impede accurate
metacognition is the use of flashcards to learn or study new material. Students who use flashcards may
become overconfident that they have learned association because they have immediate access to an
answer (Koriat & Bjork, 2006). This overconfidence may lead students to neglect practice on items they
do not understand well. A more helpful strategy that gives students better insight into their own
learning is for students to quiz themselves on the study material, which has been show to encourage
long-term learning of material (e.g., Roediger & Karpicke, 2006). When students self-quiz, they are
required to think of questions to ask of themselves (i.e., judge what is important about the topic), and if
used properly, this strategy will help students simulate the testing environment by answering their own
questions without immediate access to answers to the questions. Other effective learning strategies
include summarizing main points of a text (Thiede, Anderson, & Therriault, 2003), and asking students to
make judgments of how well they understood reading or textbook passages for their classes (Koch,
2001). To summarize a text, students need a basic understanding of what the text means and how the
main points in the text relate to one another; this process helps provide students with feedback on what
they have learned or not learned 146 according to how well they can write about the text. Further,
making judgments of understanding for a text overtly asks students to think about what they need to
read over again or spend more time understanding. Overall, more effective learning strategies help give
students more feedback and insight into how they learn and how they learn well. For students, having
better insight into their own learning is associated with improved long-term retention of material and
improved performance in college (Chen, 2002; Schraw & Dennison, 1994; Vrugt & Oort, 2008; Yukselturk
& Bulut, 2007). Teaching these strategies to students and reinforcing them through classroom use (e.g.,
asking students to reflect on how well they understood various parts of a class reading) will give them
the tools to gain better insight into their own learning.
Encouraging metacognitive monitoring through motivation
We know that metacognitive monitoring helps students to see the “big picture” of their learning habits
and current knowledge, which then can provide an opportunity for students to improve learning habits
and increase knowledge when appropriate. Given the opportunity to gain knowledge and improve the
learning experience, why do some students not engage in metacognitive monitoring? Even when
students are taught effective study strategies for learning, they might not be willing to expend the extra
effort inherent in some of the most effective learning strategies. Students are especially vulnerable to
ineffective habits when facing difficult material. Even though spacing study out over time instead of
massing it together creates “desirable difficulties” that promote sustained learning of material (e.g.,
Schmidt & Bjork, 1992; Bjork, 1994; see Clark & Bjork, this volume, for a review), students often choose
to mass study rather than space it over time (Pyc & Dunlosky, 2010).
When students separate study sessions, they must come back to the material and reevaluate what they
remember and need to study harder since the last study session. Students who do not space practice
may have a hard time realizing how much they have left to learn, contributing to overconfidence in their
learning. One strategy that teachers can use to help students use effective learning strategies is to foster
mastery goals in the classroom. Students who hold mastery goals focus mainly on trying to learn new
material, while students holding performance goals tend to focus on appearing intelligent and avoiding
failure (e.g., Elliot & Harackiewicz, 1996; Elliot & McGregor, 2001).
Educators can help students by creating a “culture of learning” in the classroom, which would entail
encouraging students to adopt mastery goals by focusing on acquiring new knowledge and skills, even
when doing so may mean that students look unintelligent or failing sometimes. Students who hold
mastery goals tend to process material more deeply (Elliot, McGregor, & Gable, 1999), seek out help
more often when appropriate (Linnenbrink, 2005) and ultimately have greater learning success than
students holding performance goals (Elliot & McGregor, 1999; Robbins et al., 2004). Similarly, teachers
can encourage learning by teaching students an incremental view of intelligence. An incremental view of
intelligence is characterized by the belief that intelligence is malleable and can be developed. In
contrast, one might hold an entity view of intelligence, which is characterized by the belief that
intelligence is fixed (for review, see Dweck, 1999).
Students’ theories of intelligence influence the goals that students adopt (Dweck & Leggett, 1988; Elliot
& McGregor, 2001). For example, a view of intelligence as malleable encourages students to adopt
learning goals over performance goals (Blackwell et al., 2007; Robins & Pals, 2002). In contrast, an entity
view also predicts a tendency to make helpless attributions when facing difficulty on learning tasks
(Blackwell et al., 2007; Hong et al., 1999; Nussbaum & Dweck, 2008; Robins & Pals, 2002). Students with
an incremental theory, in comparison, make more beneficial attributions in the face of difficulty and
report more positive beliefs about effort (Blackwell et al., 2007). These differences in attributions and
perceptions of effort carry over to help students perform well on learning tasks. For example, Blackwell,
Trzesniewski, and Dweck (2007) found that 147 holding an incremental view of intelligence in the 6th
grade predicted higher grades in students’ math classes two years later.
Other research has shown benefits of teaching students an incremental view of intelligence for math
grades (Aronson et al., 2002) and standardized test scores (Good, Aronson, & Inzlicht, 2003).
Collectively, this research provides intriguing evidence that teaching students an incremental view of
intelligence can improve student achievement. Research suggests that an incremental theory of
intelligence is also associated with more effective study practices (e.g., Cury, Da Fonseca, Zahn, & Elliot,
2008; Kovach, Fleming, & Wilgosh, 2001) and more accurate metacognitive perceptions (Ehrlinger &
Dweck, 2013). For example, Blackwell, Trzesniewski, and Dweck (2007) found that students with an
incremental theory of intelligence reported a greater willingness to engage in positive study strategies
than entity theorist students (e.g., willingness to “work harder in this class from now on” and “spend
more time studying for tests”).
In addition, incremental theorists engaged in reading strategies that aid comprehension more often than
did entity theorists (Law, 2009) and were more likely to report using positive learning strategies (Braten
& Olaussen, 1998; Stipek & Gralinski, 1996), including elaborating and organizing concepts when
studying (Lachman and Andreoletti, 2006; Ommundsen, Haugen & Lund, 2005; Smith, 2005). Past
research has also found that high endorsement of performance goals correlates with lower self-reported
use of self-regulated learning strategies (Bartels & Magun-Jackson, 2009; Vrugt & Oot, 2008).
Ehrlinger and Ward (2012) found that high school students with stronger incremental beliefs more
often reported using deep learning strategies than students with stronger entity views, as measured by
responses to questions like “I reviewed material by trying to see how new material related to things I
knew” and “When I studied I put important ideas into my own words.” We found that stronger
incremental views also predicted greater reported use of metacognitive monitoring, as measured by
agreement with questions like “I asked myself questions to make sure I knew the material I had been
studying” and “I thought about what I already knew and what I still had to learn.” Further, students with
an incremental view offered less overconfident, more accurate self-assessments than those with a
stronger entity view. These findings suggest that incremental theorists might choose self-regulated
strategies and show greater metacognitive accuracy more often than do entity theorists.
References
Baddeley, A. D., & Longman, D. J. A. (1978). The influence of length and frequency of training session on
the rate of learning to type. Ergonomics, 21, 627-635. doi:10.1080/00140137808931764
Bahrick, H. P (1979). Maintenance of knowledge: Questions about memory we forgot to ask. Journal of
Experimental Psychology: General, 108, 296-308. doi: 10.1037/0096-3445.108.3.296
Bartels, J.M., & Magun-Jackson, S. (2009). Approach-avoidance motivation and metacognitive
selfregulation: The role of need for achievement and fear of failure. Learning and Individual
Differences, 19, 459-463. http://dx.doi.org/10.1016/j.lindif.2009.03.008
Bjork, R.A. (1994). Memory and metamemory considerations in the training of human beings. In J.
Metcalfe & A. Shimamura (Eds.), Metacognition: Knowing about knowing (pp. 185–205).
Cambridge, MA: MIT Press.
Bjork, R. A. (1999). Assessing our own competence: Heuristics and illusions. In D. Gopher and A. Koriat
(Eds.), Attention and Performance XVII. Cognitive regulation of performance: Interaction of
theory and application (pp. 435-459). Cambridge, MA: MIT Press.
Blackwell, L., Trzesniewski, K.H., & Dweck, C.S. (2007). Implicit theories of intelligence predict
achievement across an adolescent transition: A longitudinal study and an intervention. Child
Development, 78, 246-263. doi: 10.1111/j.1467-8624.2007.00995.x
Braten, I., & Olaussen, B. S. (1998). The relationship between motivational beliefs and learning strategy
use among Norwegian college students. Contemporary Educational Psychology, 23, 182–194.
http://dx.doi.org/10.1006/ceps.1997.0963
Buehler, R., Griffin, D., & Ross, M. (1994). Exploring the "planning fallacy": Why people underestimate
their task completion times. Journal of Personality and Social Psychology, 67, 366-381. doi:
10.1037/0022-3514.67.3.366
Chen, C. S. (2002). Self-regulated learning strategies and achievement in an Introduction to Information
Systems course. Information Technology, Learning, and Performance Journal, 20, 11-25.
Cuddy, L.J., & Jacoby, L.L. (1982) When forgetting helps memory: An analysis of repetition effects.
Journal of Verbal Learning & Memory, 21, 451-467. http://dx.doi.org/10.1016/S0022-
5371(82)90727-7
Cury, F., Da Fonseca, D., Zahn, I. & Elliot, A. (2008). Implicit theories and IQ test performance: A
sequential mediational analysis. Journal of Experimental Social Psychology, 44, 783-791. doi:
10.1016/j.jesp.2007.07.003
Dunlosky, J., Hertzog, C., Kennedy, M., & Thiede, K. (2005). The self-monitoring approach for effective
learning. Cognitive Technology, 10, 4-11.
Dunlosky, J., & Rawson, K. A. (2012). Overconfidence produces underachievement: Inaccurate self
evaluations undermine students’ learning and retention. Learning and Instruction, 22, 271-280.
doi: 10.1016/j.learninstruc.2011.08.003.
Dunning, D., Johnson, K., Ehrlinger, J., Kruger, J. (2003). Why people fail to recognize their own
incompetence. Current Directions in Psychological Science, 12(3), 83-87. doi: 10.1111/1467-
8721.01235
Dunning, D. (2005). Self-Insight: Roadblocks and Detours on the Path to Knowing Thyself. New York, NY:
Psychology Press. 149 Dweck, C. S. (1999). Self theories: Their role in motivation, personality,
and development. Philadelphia, PA: Psychology Press/Taylor and Francis.
Dweck, C. S., & Leggett, E. L. (1988). A social-cognitive approach to motivation and personality.
Psychological Review, 95, 256-273. doi: 10.1037/0033-295X.95.2.256
Ehrlinger, J., & Dweck, C. S. (2013) Understanding overconfidence: implicit theories, preferential
attention, and distorted self assessment. Manuscript in Preparation, Washington State
University.
Ehrlinger J., & Dunning, D. (2003). How chronic self-views influence (and mislead) estimates of
performance. Journal of Personality and Social Psychology. 84, 5-17. doi: 10.1037/0022-
3514.84.1.5
Ehrlinger, J., Johnson, K., Banner, M., Dunning, D., & Kruger, J. (2008). Why the unskilled are unaware:
Further explorations of (absent) self-insight among the incompetent. Organizational Behavior
and Human Decision Processes, 105, 98-121. doi:10.1016/j.obhdp.2007.05.002
Ehrlinger, J., & Ward, P.W. (2012). The effect of intelligence beliefs on use of self-regulated learning.
Unpublished data, Florida State University.
