Examining Students' Achievement Goals, Metacognitive Monitoring Behaviors, and Achievement Using Person-Centered and Data-Mining Approaches

• In Event: 44.081 - Data-Driven Techniques That Complement Theoretical Study of Cognition and Learning With Technology

Abstract

Purpose. Students’ achievement goals are theorized to influence their cognitive and metacognitive processes and, ultimately, their achievement. This study employs person-centered analysis of surveyed goals and data mining of logs of learning management system (LMS) events to examine these relations.
Perspectives. The engine of the self-regulated learning (SRL) process is the "enactment phase" when learners metacognitively monitor the success of their past efforts and select subsequent cognitive strategies based on these judgments (Winne & Hadwin, 2008). These SRL processes can be observed in environments that both provide and record use of monitoring tools. Logs provide rich records of learning events and allow researchers to examine theoretical assumptions including those that propose ways that students’ motivations influence monitoring processes. For example, students’ achievement goals (Elliot, 2005) – their purposes for engaging in learning tasks, which include a desire for understanding, a desire to perform well, and a desire to avoid performing poorly – are thought to influence students’ cognitive engagement, monitoring, and strategy selection. Students with strong mastery goals are theorized to engage in deeper cognitive and metacognitive learning strategies (including monitoring), while those with performance avoidance goals are theorized to tend towards shallower strategies, and performance-approach oriented students’ tendencies are theorized to mix strategies.
Methods. We observed 440 students’ (75% female, 40% from underrepresented minority groups, 58% first generation college students) use of tools provided by their instructor on the LMS course site for their face-to-face biology course. Tools were designed to support behaviors including 1) monitoring of learning via ungraded practice quizzes, 2) monitoring progress towards learning goals via self-scorable lists of learning objectives per chapter and 3) use of “My Grades” to monitor performance. Students were surveyed initially to assess their achievement goals (Elliot & Murayama, 2008; Achievement Goals Questionnaire-Revised).
Data. Descriptive, inferential, and data mining analyses explored how students' achievement goals influenced use of monitoring tools and whether patterns aligned to behaviors common to high (i.e., earn an A or B) and low achievers (i.e., C, D, or F). A preliminary k-means cluster analyses yielded three groups with goal complexes representing 1) high mastery goals compared to performance-approach and performance-avoidance goals, 2) high mastery and performance-approach endorsement, and 3) high endorsement of all goals.
Results. Mastery-oriented students monitored their learning earlier and to a greater extent than approach- or goal-oriented students (Figure 1). Mastery-oriented students more frequently monitored mastery of learning objectives prior to exams (Figure 2). Approach-oriented students were more apt to seek feedback on their performance after exams (Figure 3). Behavior patterns of mastery-oriented learners matched behaviors of the highest achievers, confirming the benefits of this orientation toward learning in this course.
Significance. Results of analyses provide fine-grained evidence of implications of students' goals for the self-regulated learning process – a theoretical question that is seldom studied with rich process data and data-driven methods. Results also provide clues to the periods when such materials are most productively used. This can inform how and when instructors should provide content to students to prompt timely behaviors and promote learning.

Authors

• Wonjoon Hong, University of Nevada - Las Vegas

• Matthew L. Bernacki, University of Nevada - Las Vegas