Research exploring the connection between students' learning and their psychological factors (e.g., emotions, attitudes, and beliefs) is often grounded in models and theories from literature related to psychology and learning sciences. These theories provide insights into how psychological factors influence students' learning, motivation, and academic performance. To deepen our understanding of the interplay between these factors and students' learning and performance, this paper provides findings from a systematic literature review (SLR) of research studies about theories and methods used to understand the emotions and self-efficacy of undergraduate computing and engineering students. We examined thirty studies published between 2005 and 2023 in top-tier academic venues for computing and engineering education research. These studies leverage diverse methodologies, including validated surveys, physiological data, and grounded theory approaches, to explore the nuances of students' emotions and self-efficacy in computing and engineering education. We discuss how these factors are defined in the literature, the methods applied to measure and analyze them, and the implications for future research and educational practice. This SLR could assist computing and engineering education researchers in designing rigorous research studies focused on exploring these factors in students' learning. Furthermore, this may provide educators with a reference for devising effective teaching strategies to improve students' perceptions of computing, thereby enhancing their academic achievement.

This study uses a multi-modal approach to investigate novice students' cognitive and emotional experiences during a programming task. We collected multi-modal data from twenty-eight students taking an introductory programming course for the first time. These data include eye-tracking and electrodermal activity (EDA) to assess cognitive engagement and emotional arousal in near real-time, followed by a retrospective think-aloud interview. Eye-tracking data were analyzed to identify students' focus within the programming environment, while peaks in the EDA data were used as a proxy for emotional arousal. By triangulating these data, we identified key programming events, such as debugging failure and syntax errors, influencing students' cognitive engagement and emotional responses. Preliminary findings suggest that students' prolonged focus and frequent transition between coding pane, terminal, and task description areas, particularly during debugging, align with peaks in emotional arousal. This may emphasize the role of task challenges in shaping students' experiences. Insights from this study could inform the need to integrate emotional and cognitive support into educational tools to personalize learning and provide near real-time support when students encounter difficulties. Having such tools could make programming courses more engaging and improve both student retention and learning outcomes.

In introductory computer programming courses, students experience a range of emotions. Students often experience anxiety and frustration when they encounter difficulties in writing programs. Continued frustration can discourage students from pursuing engineering and computing careers. Although prior research has shown how emotions affect students’ motivation and learning, little is known about students’ emotions in programming courses. In this qualitative study of first-year engineering students taking an introductory programming course, we examined the emotions that these students experienced during programming tasks and the reasons for experiencing those emotions. Our study was grounded in the control-value theory of achievement emotions. Each research participant came to two laboratory sessions: a programming session and a retrospective think-aloud interview session. In the programming session, each participant worked individually on programming problems. We collected screen capture, biometrics, and survey responses. In the interview session, each participant watched a video of their actions during the programming session. After every 2 minutes of viewing, the participants reported the emotions that they had experienced during this 2-minute period. We performed a thematic analysis of the interview data. Our results indicate that the participants experienced frustration most frequently. Sometimes they experienced multiple emotions. For example, one participant felt annoyed because she had made a mistake, but she felt joy and pride when she fixed the mistake. To promote student learning, educators should take students’ emotions into account in the design of curriculum and pedagogy for introductory programming courses.

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.

Learning computer programming is hard for novices and may induce both positive and negative emotions. This dissertation research employs a novel multi-modal research design in an attempt to understand emotions that novices experience while learning programming in an introductory undergraduate programming course. I want to participate in the doctoral consortium to get feedback on my research, specifically on how to improve my data analysis strategy and to make sense of the data.