Improving Metacognition in a College Classroom: Does Enough Practice Work?.

Metacognition is often described as knowledge and control over one’s cognitive processes. Models of metacognition often include knowledge monitoring as the foundation of metacognitive skills. The current study was designed to determine whether the ability to accurately assess one’s knowledge can increase throughout a semester long course, when students are provided knowledge monitoring practice. Undergraduates’ enrolled in an educational psychology course were administered 13 exams during the course of a semester and provided a number of opportunities to practice knowledge monitoring. Prior to each exam students were required to predict their exam scores. Calibration (the difference between predicted scores and actual performance) improved over the course of the semester. However, the data also revealed improved calibration might have been an artifact of the data. Put differently, calibration was poor at the beginning of the semester as students were on average overconfident. By the end of the semester, students predicted scores had not changed, but exam scores increased thus improving calibration. Knowledge monitoring is a basic metacognitive process essential to learning. Imagine a student preparing for an upcoming examination in her educational psychology course. To prepare efficiently and to be well prepared for the exam, the student must be able to identify those concepts she has already mastered and those concepts that will require more effort and study time. This ability to monitor one’s own knowledge is a key to metacognitive and self-regulation processes during learning. Indeed, Tobias and Everson (2009) proposed a hierarchy of metacognitive processes with monitoring knowledge as the foundation. In the Tobias and Everson (2009) model, higher-level metacognitive processes, such as selecting strategies, evaluating learning, and planning are dependent upon accurate knowledge monitoring. Tobias and colleagues have demonstrated that the ability to accurately judge one’s knowledge (knowledge monitoring accuracy) is predictive of math achievement, reading achievement and even GPA scores (see Tobias & Everson, 2009 for a review). Other theories of metacognition also propose that effective knowledge monitoring leads to better regulation during studying (e.g., Metcalfe, 2009; Nelson & Narens, 1990). One goal of the current investigation was to determine if individual differences in knowledge monitoring accuracy are related to academic success within a classroom setting. A second goal was to determine if training improves students’ knowledge monitoring accuracy as measured by calibration. The Knowledge Monitoring Assessment As noted by Serra and Metcalfe (2009) metacognition is not flawless and poor metacognition can have a negative impact on studying and performance. Previous research has demonstrated that college students who are better at knowledge monitoring, as measured by predicting scores on an exam (i.e., calibration) are also likely to outperform those students who are not accurate knowledge monitors (Hacker, Bol, Hogan & Rakow, 2000; Isaacson & Fujita, 2001). Hartwig, Was, Isaacson, & Dunlosky, (2012) demonstrated a clear connection between knowledge monitoring accuracy and academic performance. In their investigation Hartwig, et al. (2012) developed a knowledge monitoring assessment based on the method presented by Tobias and Everson (2002; 2009). The knowledge monitoring assessment used by Hartwig, et al. (2012) required participants to judge whether they knew the definition of a word or not. Participants made a yes (known) or no (not known) judgment for each of 50 vocabulary words, and were then required to complete a multiple choice test in which they were presented with each of the 50 vocabulary items and five possible synonyms. Four of the possible synonyms were distractors and a fifth was an actual synonym of the vocabulary items. The knowledge monitoring assessment generated the following possible outcomes. Students indicate the word is: 1) known and correctly responded to the item on the vocabulary test [hits]; 2) known but responded to incorrectly on the test [false alarms]; 3) unknown but the correct response was given on the test [misses]; and 4) unknown and responded to incorrectly on the test [correct rejections]. Hits and correct rejections represent accurate knowledge assessment, whereas false alarms and misses represent inaccurate knowledge assessments. Hartwig et al. (2012) administered this knowledge monitoring assessment in the first two weeks of the semester of an undergraduate course in educational psychology and found that accuracy on the knowledge monitoring assessment was correlated to final exams scores. Although the correlation between the knowledge monitoring assessment and final exam score was moderate (r = .39) it represented a substantial amount of variance in final exam scores when one considers the knowledge monitoring assessment was completed at the start of the semester and the final exam was administered at the end of the semester. Furthermore, the number of variables that might influence final exam performance is quite large. The finding that the knowledge monitoring assessment accounted for variance in final exam scores is therefore notable. Indeed, Hartwig et al. (2012) split the participants into quartiles based on the knowledge monitoring assessment scores and found the quartiles differed in exam performance such that students who monitored more accurately also earned higher grades, on average, on the final exam. Improving Monitoring Accuracy The results of Hartwig, et al. (2012) and others (e.g., Hacker, et al., 2000; Isaacson & Fujita, 2001) provide evidence that knowledge monitoring accuracy is related to performance on exams. Although these findings are important, it is even more important to know if knowledge monitoring accuracy can be improved. If successful knowledge monitoring leads to positive academic outcomes, it follows that teaching students to be better knowledge monitors would make them better and more successful students. In an attempt to determine if students’ knowledge monitoring accuracy could be improved through pedagogical practices, Isaacson and Was (2010a) designed a classroom study in which they measured the knowledge monitoring accuracy at the beginning and the end of the semester of 106 undergraduates enrolled in an educational psychology class. Throughout the semester the students were required to frequently make monitoring judgments about their knowledge. Several opportunities were provided to the students to practice knowledge monitoring (cf. Isaacson & Was, 2010b). The most important of which was a weekly variable-weight and variable-difficulty exam (the exam format is described in detail in the methods section). Isaacson and Was (2010a) used the same stimuli in both administrations of the knowledge monitoring assessment. It was found that the knowledge monitoring assessment completed at the beginning of the semester and the one completed at the end of the semester were both correlated to the score on the final exam in the course. This finding supports the conclusions of Hartwig, et al (2012). More importantly, Isaacson and Was (2010a) found a significant increase in students’ knowledge monitoring accuracy from the knowledge monitoring assessment scores at the beginning of the semester to scores at the end of the semester. Isaacson and Was (2010a) proposed that the weekly monitoring practice provided throughout the semester increased students’ general knowledge monitoring ability. In an attempt to replicate the findings of increased knowledge monitoring accuracy over the course of the semester, Was, Issacson, Beziat, and Dippel (2011) conducted a study using the same methodology. Again, a significant increase in knowledge monitoring accuracy was found. However, Was et al. (2011) discovered that although there was a significant increase in the number of hits and a significant decrease in the number of misses, the rate of false alarms did not change. Therefore, the increase in gamma may reflect an artifact of the data. Put differently, if students are overconfident in their knowledge assessments the increase in hits at the end of the semester may reflect an increase in knowledge (i.e. items answered correctly), not an increase in accurately identifying known items. This may indicate that students have difficulty changing an optimistic bias or overconfidence (Hacker et al., 2000). The lack of change in the rate of false alarms and the increase in hits raised two important questions.