Post-secondary institutions have developed several interventions to address what Chamblis’ (2014) calls the arithmetic of classroom engagement. Large lecture courses limit the potential for student/instructor interaction. Instead, large lecture courses have historically relied on an industrialized model of information delivery. Very little is known about how students develop their strategies for completing their course-work in this context. The aim of this study was to outline a conceptual framework describing how undergraduates become engaged in their course-work in large science lecture courses. Course-work engagement refers to the set of practices that are part of students’ efforts to successfully complete a course. Course-work engagement is goal oriented behavior, shaped by the beliefs that individual holds about their self and the course. In the framework, I propose that students’ initial beliefs states catalyze their behavioral engagement in the course which is conditioned through feedback from working with peers, from performance assessments, and through interactions with the instructor. This study was conducted in a large (n=551) undergraduate introductory physics course. The course was composed of three lecture sections, each taught by a different instructor. Based on a review of the literature, I posed the following research questions:1.What are the relationships among students’ peer interactions, their digital instructional technology use, and their performance on assessments in a physics lecture course?2.How does the instructional system shape students’ engagement in peer interactions and their use of digital instructional technologies in a course?In this study, I employed three methods of data collection. First, I observed instruction in all three sections throughout the semester to characterize similarities and differences among the three lecture sections. Second, I administered two surveys to collect information about students’ goals for the course, their expectations for success, their beliefs about the social and academic community in the course, and the names of peers in the course who the student collaborated with in out-of-class study groups. Surveys were administered before the first and final exam in the course. Third, I used learning analytics data from a practice problem website to characterize students’ usage of the tool for study preparation before and after the first exam. Through the stochastic actor based modeling, I identified three salient factors on students’ likelihood of participating in out-of-class study groups. First, being underrepresented in the course may have shaped students’ opportunities to participate in out-of-class study groups. Women and international students both attempted to participate at higher rates than men and domestic students, respectively. However, women and international students were unlikely to have their relationships reciprocated over the semester. Second, when study tools are incorporated into out-of-class study groups, social influence appears to play a significant role in the formation of course-work engagement. For example, students who were non-users of the practice problem website tended to adopt the use behavior of their higher intensity peers. Third, changes in students’ beliefs about the course were significantly related to changes in their course grade.In terms of performance, students who experienced changes to their course beliefs, or what attempted to form new out of class study groups in the lead up to the third exam, were likely to experience academic difficulty. This study highlights the important role of time and the dynamic role of social interaction on the development of course-work engagement in large science lecture courses.
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Interaction and Mechanics: Understanding Course-work Engagementin Large Science Lectures
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