Metacognition and Academic Achievement in College Students

Introduction

College professors today face classrooms filled with students who arrive with vastly different levels of knowledge about how they learn. Some students are active, self-directed learners who understand their own learning processes and can apply that understanding across numerous situations. Others may be average students who work hard and are aware of their learning strengths and weaknesses but do not sufficiently regulate their learning. Still others may be passive learners who have little awareness of how they learn or how to control that learning. In essence, university instructors are confronted with classrooms full of students who bring varying levels of metacognitive skills.

Metacognition is generally described as the activity of monitoring and controlling one's own cognition. It can further be defined as what we know about our cognitive processes and how we use those processes in order to learn and remember (Yanyan, 2010). Researchers conceptualize metacognition by breaking it into two subcomponents: metacognitive knowledge and metacognitive regulation. These two subcomponents have been theorized to be connected to one another (Zulkiply, 2008).

Metacognitive knowledge can be described as what we know about our own cognitive processes. Declarative, procedural, and conditional knowledge are all considered sub-components of metacognitive knowledge (Schraw, 2001). Declarative knowledge consists of what we know about how we understand and what influences the way we learn. Procedural knowledge refers to our knowledge of the various learning and memory methods and strategies that work best for us. Conditional knowledge is the knowledge people have about the conditions under which various cognitive strategies can be applied. Taken together, our knowledge of cognition encompasses what we know about how we learn, what procedures and strategies are most effective for us, and the circumstances under which various cognitive actions are most productive (Schraw & Hartley, 2006).

The purpose of this study was to examine the relationships among metacognition and its metacognitive components — experience, knowledge, and strategies — and university honors students' academic behaviors. The Inventory of College Level Study Skills (Young & Fry, 2008) and the Awareness of Independent Learning Inventory (Yanyan, 2010) were utilized to gather data from college students and assess the relationships between metacognition and its mechanisms of knowledge, experience, and approaches to academic behavior.

Current research studies relating to college-level metacognition cover remedial students, learning-disabled students, and average students in specific courses. However, there is limited research relating to the associations between metacognition and the metacognitive components of experience, knowledge, and strategies and honors students' academic behavior. This study extends the scope of university-level metacognitive research by examining those relationships as they pertain to academic achievement in college students.

This study contributes to the body of knowledge by providing information on the relationship between metacognition and its constituent components — knowledge, experience, and strategies — and academic achievement in college students. It is intended to serve as a catalyst for further research on academic achievement in college students and to help fill the gap pertaining to upper-range academic performers in postsecondary institutions, as identified by Sperling and Murphy (2002).

Educational psychologists have long emphasized the significance of metacognition for regulating and supporting student learning. More recently, the Partnership for 21st Century Skills identified self-directed learning as one of the essential career and life skills needed to prepare students for post-secondary education and the workforce. Nevertheless, educators may not always be familiar with methods for teaching and measuring metacognition, particularly among college students.

Constituent Elements of Metacognition

Procedural knowledge includes awareness and management of cognition, together with knowledge about strategies (Vrugt & Oort, 2008). Schraw (2006) also distinguishes conditional cognitive knowledge — knowledge of why and when to use a specific strategy. Scholars note that cognitive knowledge is "late-evolving," in the sense that children frequently display deficits in cognitive knowledge. Although the ability to explicitly express cognitive knowledge tends to improve with age, many adults struggle to articulate what they know about their own thinking. This observation suggests that cognitive knowledge may not need to be explicit in order for individuals to access and use it.

Metacognition researchers have presented somewhat different frameworks for categorizing cognitive knowledge. For instance, several researchers have used the concepts of declarative and procedural knowledge to distinguish between types of cognitive knowledge (Vrugt & Oort, 2008). Young and Fry (2008) describe declarative cognitive knowledge roughly as epistemological understanding — the student's understanding of thinking and knowing in general. Schraw (2006) depicts declarative cognitive knowledge as awareness about oneself as a learner and the factors that might influence one's performance. Schraw and Hartley (2006) describe the process of self-appraisal as reflecting on one's personal knowledge states to answer the question, "Do I know this?" Sperling and Murphy (2002) describe declarative cognitive knowledge specifically within the context of reading as awareness of the factors that might affect reading ability.

Researchers have examined metacognition and the way it connects to measures of academic achievement. In most of these studies, metacognitive skills are evaluated in terms of particular components, which are measured in various ways across the literature. Some researchers have used self-report inventories to evaluate metacognitive abilities and then connect them to achievement measures (Schraw & Hartley, 2006). Others have examined metacognitive judgments in the form of monitoring accuracy as a measure of metacognitive control on various tests (Everson & Tobias, 1998; Nietfeld et al., 2005; Schraw, 1994, as cited in Yanyan, 2010).

Monitoring accuracy is typically calculated in terms of what is called calibration of performance. Calibration conclusions are made at both the global and local levels. Local judgments are made immediately after each item on an exam. Local monitoring accuracy is determined by calculating the average difference between the actual answer to each test question and the student's judgment of how well they answered that question. Global judgments are made after the entire test is completed; students judge how well they think they performed on the test as a whole. Global monitoring accuracy is determined by calculating the difference between the overall test score and the student's judgment of their performance. Local monitoring accuracy is considered a measure of ongoing metacognitive regulation during testing, while global monitoring accuracy is considered a measure of cumulative metacognitive regulation (Vrugt & Oort, 2008).

Vrugt and Oort (2008) were concerned with knowledge monitoring accuracy, an ability believed to be connected to metacognitive regulation. They developed a measure to evaluate students' knowledge monitoring ability (KMA) by observing the difference between students' assessments of their knowledge in a given domain and their actual knowledge as demonstrated by performance on a standardized verbal test. They found the strongest connection to be between KMA and students' final course grades in Writing and English, then in the humanities, and then in overall academic performance.

Schraw (2001) examined the relationship between metacognitive understanding and metacognitive control. He measured metacognitive knowledge by having students rate how well they believed they could predict their accuracy on a series of multiple-choice reading tests. He measured metacognitive regulation at both the global and local levels by having students rate their accuracy for each question and then rate their overall accuracy after completing the tests. Based on his findings, Schraw suggested that adult students may differ less in their metacognitive knowledge abilities than in their metacognitive regulation abilities. He further argued that metacognitive knowledge can develop independently of metacognitive regulation. Schraw also found that actual test performance was significantly associated with pre-test judgments of exam performance, a measure of metacognitive knowledge, and that test performance was likewise connected to metacognitive regulation through correlations with both global and local judgments.

Duffy and Meloth (2009) investigated metacognitive regulation by measuring monitoring accuracy at the local and global level across a series of multiple-choice exams administered over the course of a semester. They found that monitoring accuracy remained stable throughout the semester. Students were more accurate in their global predictions than in their local predictions. They also found that student performance on tests was related to local monitoring accuracy.

Metacognitive Awareness Inventory

Schraw and Hartley (2006) developed the Metacognitive Awareness Inventory (MAI) to evaluate metacognitive understanding and metacognitive control — referred to as the knowledge of cognition factor and the regulation of cognition factor. The MAI consisted of 62 items loading onto these two components of metacognition. They found strong support for both the knowledge of cognition and regulation of cognition components and confirmed that these two components were linked, as had been proposed (Yanyan, 2010).

Schraw and Moshman (1995) also assessed the convergent validity of the MAI by comparing MAI scores with other measures believed to be associated with metacognition, such as pretest monitoring ability, actual test performance, and the ability to accurately monitor test performance. They did not find a significant relationship between monitoring accuracy and the MAI, nor between pretest judgments and monitoring accuracy. However, they found that the knowledge of cognition factor of the MAI was related to higher test performance, while the regulation of cognition factor was not. They also found that knowledge of cognition, as measured by pretest judgments, was related to the MAI, and that pretest judgments were positively related to test performance.

Yanyan (2010), using the MAI to define college student metacognitive awareness, found a significant association between the knowledge of cognition factor and the regulation of cognition factor. The study also examined whether MAI scores would be associated with other indicators of academic achievement such as SAT scores and high school GPA. No relationship was found between MAI scores and those measures of academic achievement. Notably, a negative relationship was found between SAT math scores and MAI results.

Overall, the findings reviewed above regarding the relationship of metacognition with academic achievement suggest that when regulation of cognition is measured by having students assess their performance at either a global or local level, it is associated with performance on tests, subject-specific GPA scores, and overall GPA scores (Schraw & Hartley, 2006).

It appears that when metacognition is measured through regulation-of-performance measures, there is support for a relationship between metacognitive abilities and measures of academic achievement. However, measuring monitoring ability and monitoring accuracy at the global and local level is a labor-intensive undertaking, particularly when students are assessed in actual college classes rather than in laboratory or controlled settings. Students monitoring their accuracy at global and local levels must take the time to respond to test questions and then indicate how confident they were in each answer — a time-consuming and potentially stressful task, especially when test scores count toward final course grades (Zulkiply, 2008). It is therefore important to measure students in a less disruptive manner in order to assess their metacognitive awareness and skill level. A less intrusive assessment such as a questionnaire allows instructors to quickly identify struggling students early and help them develop effective metacognitive skills.

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