A Multi-Modal Investigation of Self-Regulation Strategies Adopted by First-Year Engineering Students During Programming Tasks

Abstract

This study aims to understand the self-regulation strategies first-year engineering students use to cope with emotions during programming tasks. We used Zimmerman s framework to identify the processes of self- regulated learning (SRL) as students worked on programming tasks [1, 2]. The SRL framework is a cyclical process that involves three main stages: forethought (preparation for the task), performance (engagement with the task), and self-reflection (reflection on their performance on the task). Most literature about SRL focuses on how students regulate their learning during the forethought and self-reflection stages [3, 4]. There is very little attention on students’ self-regulated learning experiences during the performance stage because it is hard to observe students while they work on the task. This study provides a unique opportunity to understand students’ self-regulation as they worked on programming problems. Seventeen first-year engineering students at a large midwestern university in the United States participated in this study during Spring 2018 [5]. As students worked on the programming task, multi-modal data were collected (video screen capture, eye-gaze data, facial expressions). Following the programming task, students reflected on their experience in a retrospective think-aloud interview. A key finding from this study showed students’ perseverance during the programming task. All students reported negative emotions while working on the task, especially while they encountered errors, or if they got stuck on a problem. First, some students reported pushing through the task, even though they experienced negative emotions. This group of students used negative emotions as fuel to persist through the adverse circumstances they experienced. These students gave up only when they could not find any solution to the problem. Second, some students gave up and moved to the next problem, as soon as they realized the problem was too hard, and they would not be able to complete the problem. Literature categorizes these two groups of students as “movers” and “stoppers” respectively [6, 7]. Students persistence through challenges indicated the positive role that negative emotions can play in students’ learning and motivation. According to the control-value theory, students experience frustration when they fail at a task [8]. In this case, most students experienced frustration because they failed at the task, but their reaction to frustration is different. The movers kept pushing through, despite experiencing frustration. This study also provides a unique opportunity to observe exemplars of near real-time biometric data of two students who participated in this study. Using these exemplars, we will discuss how different sources of data could be triangulated to provide a rich understanding of students’ self-regulated learning behaviors during programming tasks. The first exemplar is of a ‘mover’ who persisted through the task and completed it. The second exemplar is of a ‘stopper’ who struggled throughout the programming task. Understanding these persistence behaviors may help educators distinguish between students who endeavor to overcome their challenges and those who give up as soon as they encounter difficulty. These findings may be particularly useful to understand students’ long-term persistence in engineering and computing.