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.

For novice students, learning programming is hard, hence, inducing a variety of emotions. According to literature, two of the most commonly occurring emotions that students experience while learning programming are frustration and confusion. Although these emotions are momentary, they may have long-term effects on students' motivation, performance, and retention in computing. In this study, we aim to discuss challenges that students report when they feel frustration and confusion while working on the rainfall problem. This problem has been used extensively in literature to understand students' problem-solving skills. However, little is understood about how students react emotionally when they work on this problem. We recruited twenty-eight students who took CS1 during Fall 2022. They worked on the problem for twenty minutes while we collected their biometrics, clickstream, and keystroke data. A retrospective think-aloud interview was conducted soon after the task, where participants elaborated on their emotional experiences while watching the video replay of their programming task. We analyzed interview data using qualitative content analysis and triangulated these findings with biometric, clickstream, and keystroke data. Some of the challenges that trigger confusion and frustration are getting unexpected output, inability to resolve compilation and logical issues, forgetting syntax, and conceptual misunderstanding. Moreover, we found an alignment between the different data sources to provide a near real-time view of emotional experiences. Instructors may use the findings of this study to design interventions that support problem-solving, such as problem-based teaching, using interactive programming tools, and tracing for debugging.

This overarching study aims to establish the validity of the Programming Emotions Questionnaire (PEQ). The PEQ is an instrument that could be used for assessing students' emotions in a computer programming class, lab, or test. It is derived from the achievement emotions questionnaire (AEQ) that is grounded in the control-value theory of achievement emotions. Since AEQ has numerous psychometric challenges (e.g., some items are not atomic), we make significant changes to revise it, hence, we will be validating the questionnaire from scratch. In this poster we discuss the preliminary findings of our validation study, specifically, findings from the content validity phase. For content validity, we had discussions with psychometric experts and computer science experts. We also present findings of a pilot study of the target population, that is, CS1 students. The result of content validity suggests that there is a need to develop the PEQ that significantly modifies the AEQ. The immediate next steps include conducting steps to ensure construct and criterion validity, and its reliability.