Learning Monitoring and Academic Performance


As a matter of fact, education is significantly important in the modern world. According to the recent findings, a positive learning experience in school and college contributes to success in a future life (Rae, 2015). It is worth highlighting that the majority of scientists tend to think that academic success is a vital component for the person who aims to achieve a lot in life. The role of education should not be undervalued (Rae, 2015). A number of researchers delved into the link between monitoring accuracy and exam performance, among them are the following ones, namely Kruger, Dunning, Randy Isaacson, Fujita, Frumos, Young, Fry, Thiede, Efklides, Wiley, Anderson, and Griffin. This paper is focused on the critical analysis of the works presented by the stated above researchers. It is essential to find the answer to the question of whether there is a relation between monitoring accuracy and exam performance. There is a hypothesis that students with high monitoring accuracy show significantly better results on the exam. The primary purpose of the paper is to provide the answer to the research question, namely whether people who monitor their learning better are more likely to perform better on exams.

Literature Review

Thiede, Griffin, Wiley, and Anderson (2010) conducted a qualitative study that included two experiments with a group of at-risk college students and typical college students. The at-risk participants were involved in a low-performance level in comprehension subjects, whereas the typical students performed fairly well. The study focused on highlighting the role of meta-comprehension accuracy in the development of cues that enhance the ability to create a more efficient memory retrieval process. The first experiment involved the subjection of the participants to a comprehension process that was followed by a delayed test to summarize the comprehended ideas. The results revealed that the at-risk students performed relatively lowly in summarizing the concepts than their typical student counterparts.

These findings were influenced by the fact that the at-risk students did not develop accurate cues to help them in the development of effective memory for the summarization test. The second experiment involved the development of a concept mapping model that facilitated the development of the appropriate cues for the at-risk participants. The result of the new paradigm was a positive enhancement of the ability of the at-risk students to summarize the concepts. This study affirms the hypothesis that self-regulation with ultimate accuracy in the development of cues can improve performance in learning for students. However, the study also reveals that there are incidences where the development of cues can be inaccurate, leading to relatively low performance in tests. Students should, therefore, consider developing better meta-comprehension skills to facilitate the development of efficiency in cues development.

Furthermore, Young and Fry (2008) also delve into the investigation of the impact metacognition processes and awareness might have on students and their academic performance. They conducted a study to reveal the efficiency of metacognitive strategy and its possible implications. The researchers are sure that the implementation of metacognition to monitor and control the students cognition could have a beneficial effect on their final results (Young & Fry, 2008). Young and Fry tend to examine the correlation between metacognition and measures of academic achievement within a classroom setting. They used undergraduate and graduate education students at a small institution in Southeast Texas to collect the data needed for their study. In the course of the investigation, the researchers prove the great impact metacognitive processes and skills have on students academic performance. They conclude that the basic knowledge about how they teach helps students to acquire the new knowledge more efficiently and to apply it to new learning situations, which increases their performance.

Moreover, Young and Fry outline the great difference between graduate and undergraduate students with regard to regulation of cognition factor but not the knowledge. This difference lies in the performance level and basic cognitive skills, which differ depending on the level of metacognition skills. The given study also proves the significant role of metacognition in the learning process as it creates the basis for successful learning and has a direct influence on students academic performance.

Kruger and Dunning (2009), in their article Unskilled and Unaware of It: How Difficulties in Recognizing One’s Own Incompetence Lead to Inflated Self-Assessments, are focused on the issues related to high self-esteem and metacognitive competence (Kruger & Dunning, 2009). In the study, the researchers come to the conclusion that metacognitive distortion occurs when people with low qualification level make wrong assumptions and choices; however, they are unable to face the fact that they are wrong because they are unskilled and have low qualification level (Kruger & Dunning, 2009). Moreover, such people have high self-esteem, whereas educated and professional people usually underestimate their abilities and suffer from a lack of confidence, thinking that other people are more competent. The experiment conducted by the researchers proved that unskilled people are really unaware of their incompetence because of high self-esteem. The experiment proved the hypothesis. After the test on logic and grammar, individuals who were sure that they are going to take first place happened to receive the 60th position (Kruger & Dunning, 2009). Kruger and Dunning highlighted that metacognitive development, inflated expectations, and inability to realize that conclusions and assumptions are wrong influence the way a person perceives the world around and self.

Frumos (2015) is focused on the investigation of the question related to metacognitive development in relation to the academic success of college students. The researcher points out that there better monitoring contributes to better exam performance (Frumos, 2015). Monitoring accuracy is beneficial for the efficient control of students’ performance. It should be stated that the researcher presents a detailed analysis of the data that reflect the not only local but nationwide situation as well. The author proposes to promote self-education as it positively influences cognitive development (Frumos, 2015). Data and findings of Frumos prove his hypothesis. The expert notes that overconfidence may result in poor academic performance. In addition, the author of the article Metacognitive Monitoring Accuracy and Academic Performance at University Students claims that monitoring contributes to better academic results on both namely local and global levels.

Isaacson and Fujita (2006), in their work Metacognitive Knowledge Monitoring and Self-Regulated Learning: Academic Success and Reflections on Learning, examine the relation between self-regulated education and academic performance. Eighty-four students took part in the experiment; the participants were asked to answer the questions that were related to three categories, and among them were the following ones, namely:

  1. How many hours did you dedicate to learning?
  2. What is your level of confidence?
  3. What do you think of the result of the test? (Before grading, however, the test was completed) (Isaacson & Fujita, 2006).

During the test, the students were provided with an opportunity to choose questions. According to the results of the experiment, students with higher academic performance were more realistic concerning their learning experience and showed better results. Moreover, they were more successful in choosing those questions that they knew in comparison to students who were not so competent. However, they overestimated their knowledge and were too confident (Isaacson & Fujita, 2006). The author claims that self-regulation is significant, and that is, should be implemented in the learning process (Isaacson & Fujita, 2006). As a matter of fact, students who realize that self-regulation is essential and are skillful in it are aware not only of their strengths but weaknesses as well. Furthermore, they are able to determine the way towards improvement. Self-regulation is significant for the success of the learning process. In addition, the authors cite Pintrich and draw a parallel between the thermostat and the ability of a student to regulate the process of education. Isaacson and Fujita note that there are three major components that are to be found in self-regulation, namely the following ones:

  1. The identification of goals and finding the ways to achieve them, making a strategic plan;
  2. Control. This component also includes self-discipline and self-monitoring;
  3. Refection. This aspect involves understanding the difference between the goal that was set at the beginning and the material that was learned. The student should evaluate the success or sometimes a failure of the process and draw conclusions for future learning (Isaacson & Fujita, 2006).

The ability to monitor the learning process and make reflection is a fundamental element that influences the process of education significantly. The authors aim to investigate the relationship between monitoring and academic success. It should be noted that the researchers reach the goal and provide a detailed analysis of the relationship between monitoring and learning performance.

The purpose of this study is to see whether monitoring accuracy is associated with exam performance. The study predicts that people who have high monitoring accuracy perform better on exams. The validation of the hypothesis would imply that it is possible to enhance individual performance through evaluating and monitoring metacognitive processes.



The study targeted 23 female respondents who were students from Zayed University in Abu Dhabi Campus. The research targeted the respondents majoring in psychology and human services. The average age of the respondents was capped between 19 and 29 years. However, most of the participants were 21 years old. The majority of the respondents were drawn from students in their third year of study who major in psychology. The respondents were enrolled in PSY 375 (Health Psychology).

Research design and procedure

The researcher used a correlation design. Response accuracy and confidence rating were the dependent variables.

The participants filled questionnaires in the 50-minutes in-class test. The participants were expected to answer the closed-ended questions in an accurate manner. The exam was based on areas of study that the respondents had already covered during normal lessons. The 50-minute test was carried out in classes of the respondents. The respondents consented to a voluntary test. From the previously targeted 23 respondents, only one respondent turned down the request to participate in the study. Before the commencement of the examination, each respondent was asked to make a prediction of her expected grade on a scale of 1 to 10, with ten being the highest score and one being the least score. Upon completion of every answer, each respondent was asked to rate the answers on a continuum that starts with ‘really confident’ and ends with ‘really not confident. Finally, each respondent was asked to estimate her grade on the exam and the studying hours dedicated for the exam.

During data collection, it was necessary to observe certain steps that guarantee the protection of the privacy of respondents by issuing an informed consent letter requesting permission and assuring the participants of their privacy. The informed consent form highlighted the scope of the respondent’s participation, freedom to respond or not to respond to the questions, rights, and responsibilities, and general permission to participate.

Analytical tool

In relation to the proposed study, the scope of the research methodology was to examine the relationship between people’s evaluations of their memory (or learning) and their performance of the 23 participants. The instrument that was used to address this scope is guided test and questionnaire. The questionnaire enabled the targeted respondents to express different opinions on the validity of their answers. The design of the questionnaires was done in simple language to ensure that the targeted respondents are fully integrated into the study.

After data collection, data analysis was carried out for the data collected through the questionnaire. The collected data from the exam was examined via Excel to generate cross-tabulation for carrying out comparative and correlational analysis.


Monitoring accuracy is used to measure the extent to which the participants’ evaluation of their memory and learning capabilities match with their actual performance. Upon conducting the experiment, it is necessary to assess whether such a match exists and what is the extent of it. Within the framework of such assessment, we will conduct a few correlations.

Exam Performance and Confidence Ratings

The overall exam performance summed up to 0.85 )SD=0.117) (proportion of correct responses). To convert the performance into the language of grades, such performance can be counted for a good B. Although the average does not give an account for results specifically, it is possible to say that the students have reached a high-performance rate in the given test. Only one student has a proportion of correct answers that is lower than 0.7 (SD= 0.117); three students in the sample have given 100% correct responses. Overall, the performance rate was high for all participants regardless of their confidence rating.

Monitoring Accuracy

Monitoring accuracy indicates how accurate a participant was in predicting their performance. In order to examine the accuracy of the participant predictions, several correlations have been conducted. First, I conducted a correlation between confidence rating and exam performance. The average correlation (r) between the participants’ predictions of their performance – metacognitive judgment – and grade-point was 0.248 (SD = 0.254).,and it was reliably different from zero, t (18)=1.19, p < 0.05. These results suggest that participants are aware of the questions that they know and are able to answer and the questions that they do not know. This means that the participant can monitor their performance. Although the results indicate that the participant performed better on questions that received higher confidence ratings, their metacognitive judgments are not perfect.

Another correlation has been done to investigate how accurate the participant was in their rating. I have conducted a correlation between the rating given at the end of the exam and the student’s actual grade. The average correlation between the participants’ confidence rating at the end of the exam and their actual post-test grade outcome was 0a.33 (SD= 0.117), p < 0.05. Such result indicates a weak association between the students’ confidence ratings and their exam grades. The results indicate that some participants were able to give a prediction that was very close to their final grade, but not the same. This means that not all the students were able to give an accurate prediction of their performance in the exam. Participant predictions were not perfect.

The relationship between monitoring accuracy and exam performance

Further, we have to test the hypothesis on the presence of an association between those with high learning monitoring accuracy who monitor their performance well and those with low learning monitoring accuracy who do not monitor their performance very well and their learning outcomes. For that purpose, we have separated the participants into two groups based on how well did their metacognitive judgment (cr) correlate with their exam performance. The participant with correlation below the mean (0.248) was characterized as a low monitoring accuracy group, and participants with a correlation above the mean were characterized as a high monitoring accuracy group. Three participants who had 100% correct test results do not meet the inclusion criteria, which is why they were excluded from the data analysis.

Based on the correlation score, the high monitoring accuracy group consisted of 11 participants, and the low monitoring efficacy group amounted to eight participants. The mean for the high monitoring accuracy group and the low monitoring accuracy group was 0.43 and -0.00, respectively. The reason why is that the mean is negative is that half of the participants in the low monitoring accuracy group had negative correlation scores. To illustrate, they gave a high confidence rating for responses that were incorrect and a low confidence rating for responses that were correct. The average proportion correct for these two groups was 0.836 (SD= 0.07) and 0.812 (SD= 0.14), respectively. A t-test was conducted to determine if the groups’ performance was significantly different from each other. The result of the t-test for both groups was t (17) = 0.46, p = 0.64. The results of the t-test do not demonstrate any significant difference in the exam performance between students who monitor their learning well and those with low learning monitoring accuracy. Thus, contrary to the initial hypothesis, the absence of the difference indicates that higher or lower accuracy monitoring does not determine the performance rate. Hence, the results suggest that the presence of a high monitoring accuracy is not an indicator of good academic performance.

Mean proportions correct across students with high MA (1) and low MA (2).
Figure 1. Mean proportions correct across students with high MA (1) and low MA (2).

MA= monitoring accuracy.


Explanation of results

It is essential to note that stated above results prove that the relation between monitoring the learning process and academic success does not occur. According to the results, the exam performance was not related to confidence rating, and the vast majority of students showed good results on the test. Only one student showed a low result, whereas three students managed to answer all questions correctly. Such outcomes can be explained through a positive metacognitive development of the students and, in addition, a high self-regulation level. Isaacson and Fujita are sure that a high self-regulation level contributed to better results, and the experiment proved it.

The next section of the results deals with the monitoring accuracy. It is of paramount importance to make an accent that monitoring accuracy is considered to be an essential component for the improvement of academic performance. The described experiment shows that students answer those questions better than they know; however, in the majority of cases, they are unable to make metacognitive judgments. According to the findings conducted by Kruger, Dunning, Frumos, and others, the monitoring process influences the process of education in a significant way and, furthermore, contributes to better control over the learning outcomes. The academic performance corresponds to the confidence and skillfulness of the student.

There is an interesting relation between the actual level of knowledge and the expected rate. The experiment showed that the correlation between expectations and actual grades is rather weak. The students show a lack of skills that are directed to the evaluation of competence. The majority of students were unable to determine the level of their competence and results of the test, although it should be noted that some students were very close. Thus, it seems essential to point out the fact that the vast majority of students cannot predict their results and are not sure regarding the realistic level of their competence. Accurate predictions are another weak point of the students. Kruger and Dunning are sure that students with lower academic performance have more confidence and are likely to overestimate their level of knowledge, whereas people who are skillful usually underestimate their knowledge and believe that other students have better performance.

Is there a relation between monitoring and performance? Do students who monitor their learning process show better academic results than those who do not? These types of questions are essential to answer and thus, receive a primary concern in this paper. The participants were divided into two groups based on their metacognitive judgments; the first group contained the students who have a higher level of self-monitoring, and the second deals with those who do not. With the consideration of the fact, those three students who answered all the questions correctly were excluded from the groups; eleven students are believed to have high monitoring accuracy, whereas eight members have low monitoring accuracy. The results of the experiment showed that the hypothesis that students with high monitoring accuracy are more successful at academic learning than the ones with low are not correct. The performance rate is considered to be independent of the level of monitoring accuracy. The way the participants performed on the test revealed a possibility that the connection between monitoring and performance is rather weak.

References to previous research

It is worth highlighting that the results of the study contradict the findings that were revealed by Kruger, Dunning, Isaacson, Fujita, and Frumos. The stated researchers were sure that the monitoring accuracy is related to academic performance in a significant way. The scientists stated that monitoring allows improving the control over the learning process and evaluating the outcomes. Moreover, self-regulated education combined with monitoring gives fruitful results and is sure to address the needs of the students (Isaacson & Fujita, 2006). The experts claimed that monitoring provides a student with a possibility to set goals and objectives, learn the material more efficiently, and analyze the result with the goal that was set in the beginning (Isaacson & Fujita, 2006). Thus, such students have a more realistic level of confidence and are able to draw appropriate conclusions, make the right choices, and metacognitive judgments. The experiment conducted between twenty-three participants showed contradicting results. According to the experiment, there is no connection between expectation and confidence rate, monitoring, and academic performance. The issue demands further investigations and research as they provide results that contradict the theories proposed by the following scientists, namely Kruger, Dunning, Isaacson, Fujita, and Frumos.

Research Limitations

The possible methodological limitations of the study that targeted only 23 female respondents include the sample size of the target group. In order to properly analyze the relationships between the performance of respondents and the evaluation of their memory, it would have been beneficial to include 23 male respondents with the same major in psychology and human services to extend the sample size and add variation to the quality of the sample.

The second methodological limitation of the study is linked to the self-reported data. The responses for the test questions provided in the questionnaires were taken at face value since they could not be verified. For example, the estimations of the grade on the scale from 1 to 10 could not be verified since respondents could just say any number that came to mind without taking into account any actual implications that will influence the results.

The last methodological limitation is connected to the measure that was used to collect the data for the research. The collection of data through the use of questionnaires may have inhibited the ability of the researcher to present thorough results of the analysis. For instance, the researcher forgot to include the summarizing question in the survey that could have been beneficial for addressing an issue of how well students remembered the previously learned material and what strategy they have used to study and prepare for this exam.

Conclusion and Recommendations

The research did not find evidence that control and monitoring of students’ metacognition have a significant enhancing effect on the individual exams. Instead, students appear prone to incorrect judgments and overconfidence.

However, an in-depth look at the results and their comparison to other research in the field shows that this may not be the definite result. Ultimately, the discovered lack of correlation between individual exam results and monitoring accuracy can be interpreted in two significant ways. The first interpretation suggests that students are often unable to evaluate their level of knowledge in the tests and that new study methods should be sought to improve their degree of comprehension, knowledge retention, and self-evaluation. The second one, instead, questions the validity of the results and positions that that more control over the conditions of testing would be required before final conclusions can be made. These results also suggest that critical self-evaluation by students before taking the test considerably improves monitoring accuracy..

The critical issue with the current study may well be the students’ inability to evaluate their own knowledge effectively during the preparation for exams. This means that future research into this matter would need to focus on laying down guidelines for students to learn how to evaluate their comprehension of the material in general, as well as smaller parts of it.

Further steps concerning the research should involve the benefits and negative aspects of metacognition. As mentioned by Efklides (2005), metacognition can have a variety of characteristics ranging from helpful to debilitating (p. 11). In addition, it is important to investigate the connections between the increased knowledge about a particular subject and metacognition; however, it is also crucial to take factors like personality into account. The ability of an individual to make differentiations between what is already known and what still needs to be learned is a skill that will contribute to particular variations of self-regulation and self-monitoring. Thus, the research on the personalities of students that can be considered accurate knowledge monitors is needed in order to assess the ways they regulate their academic behavior.

In addition, further research to determine connections between the monitoring of the knowledge as well as motivation, is necessary to get insight into how these aspects affect students’ ability to learn strategically. In this respect, the identification of students with accurate knowledge monitoring skills but the lack of academic motivation would be highly beneficial. It is expected that the students with high levels of academic motivation are less likely to achieve effectiveness unless they are equipped with the skills related to knowledge monitoring.


Efklides, A. (2006). Metacognition and Affect: What Can Metacognitive Experiences Tell Us About the Learning Process? Educational Research Review, 1, 3-14.

Frumos, F. (2015). Metacognitive Monitoring Accuracy and Academic Performance at University Students. Journal of Innovation in Psychology, Education and Didactics, 19(2), 307-314.

Isaacson, R., & Fujita, F. (2006). Metacognitive Knowledge Monitoring and Self-Regulated Learning: Academic Success and Reflections on Learning. Journal of the Scholarship of Teaching and Learning, 6(1), 39-55.

Kruger, J., & Dunning, D. (2009). Unskilled and unaware of it: How difficulties in recognizing one’s own incompetence lead to inflated self-assessments. Journal of Personality and Social Psychology, 1(1), 20-46.

Rae, D. (2015). Entrepreneurial learning: New perspectives in research, education and practice. New York, NY: Taylor and Francis.

Thiede, K. W., Griffin, T. D., Wiley, J., & Anderson, M. C. (2010). Poor metacomprehension accuracy as a result of inappropriate cue use. Discourse Processes, 47(4), 331-362.

Young, A., & Fry, J. (2008). Metacognitive awareness and academic achievement in college students. Journal of the Scholarship of Teaching and Learning, 8(2), 1-10. Web.

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