Denis Zhidkikh | Searcharxiv

arXiv2025

Navigating through CS1: The Role of Self-Regulation and Supervision in Student Progress

The need for students' self-regulation for fluent transitioning to university studies is known. Our aim was to integrate study-supportive activities with course supervision activities within CS1. We educated TAs to pay attention to students' study ability and self-regulation. An interview study ($N=14$) was undertaken to investigate this approach. A thematic analysis yielded rather mixed results in light of our aims. Self-regulation was underpinned by the influences external to our setting, including labor market-related needs, earlier crises in study habits, and personal characteristics such as passion, grit, creativity, and valuation of utility. Safety in one-to-one supervision was considered essential, while shyness, fear, and even altruism caused self-handicapping during the course. Students were aware of their learning styles and need for self-regulation, while did not always know how to self-regulate or preferred to externalize it. The results highlight that supporting self-regulation should be integrated with students' personal histories and experiences, and thereby calls attention to transformative learning pedagogies. The thematization can help to understand CS1 students' self-regulation processes and improve CS1 support practices.

cs.CY↗

arXiv2025

Understanding Self-Regulated Learning Behavior Among High and Low Dropout Risk Students During CS1: Combining Trace Logs, Dropout Prediction and Self-Reports

The introductory programming course (CS1) at the university level is often perceived as particularly challenging, contributing to high dropout rates among Computer Science students. Identifying when and how students encounter difficulties in this course is critical for providing targeted support. This study explores the behavioral patterns of CS1 students at varying dropout risks using self-regulated learning (SRL) as the theoretical framework. Using learning analytics, we analyzed trace logs and task performance data from a virtual learning environment to map resource usage patterns and used student dropout prediction to distinguish between low and high dropout risk behaviors. Data from 47 consenting students were used to carry out the analysis. Additionally, self-report questionnaires from 29 participants enriched the interpretation of observed patterns. The findings reveal distinct weekly learning strategy types and categorize course behavior. Among low dropout risk students, three learning strategies were identified that differed in how students prioritized completing tasks and reading course materials. High dropout risk students exhibited nine different strategies, some representing temporary unsuccessful strategies that can be recovered from, while others indicated behaviors of students on the verge of dropping out. This study highlights the value of combining student behavior profiling with predictive learning analytics to explain dropout predictions and devise targeted interventions. Practical findings of the study can in turn be used to help teachers, teaching assistants and other practitioners to better recognize and address students at the verge of dropping out.

cs.CY↗