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ERIC Number: EJ1485120
Record Type: Journal
Publication Date: 2025
Pages: 30
Abstractor: As Provided
ISBN: N/A
ISSN: N/A
EISSN: EISSN-1946-6226
Available Date: 0000-00-00
Intervention for Students Repeating a CS1 Online Course: Analyzing Their Performance, Behavior, and Perceptions
ACM Transactions on Computing Education, v25 n3 Article 38 2025
Objectives: This study addresses the challenges faced by students repeating an introductory programming course (CS1) at an online university, where dropout and failure rates remain high. While programming education has been widely studied, targeted interventions for repeating students remain scarce. Our research aims to analyze the experiences of these students and develop a tailored intervention to improve support mechanisms. Participants: The study focuses on students retaking CS1, which were categorized into two groups: those who previously attempted but failed the course, and those who dropped out early with almost no follow-up of the course. Method: An observational approach was used to analyze students' performance, behavior, and perceptions, combining data from institutional databases and questionnaires. Based on these insights, an intervention was designed, incorporating individualized feedback, structured engagement activities, and encouragement for students to reflect on their past experiences in order to foster a more supportive learning environment. Findings: Initial results indicate that the intervention increased student engagement, particularly in the early weeks of the semester. However, some students continued to struggle with course content, suggesting the need for additional instructional support. While the intervention was effective in fostering engagement, its impact on overall success rates was limited, highlighting the necessity of more personalized approaches. Conclusions: The study provides valuable insights for educators seeking to improve support for repeating students in programming courses. Key lessons include the importance of proactive engagement, tailored feedback, and structured interventions. Future research should focus on developing personalized interventions, validating a tailored questionnaire for deeper insights, and exploring adaptive learning technologies and longitudinal studies to enhance academic outcomes while reducing instructor workload.
Association for Computing Machinery. 1601 Broadway 10th Floor, New York, NY 10119. Tel: 800-342-6626; Tel: 212-626-0500; Fax: 212-944-1318; e-mail: acmhelp@acm.org; Web site: http://toce.acm.org/
Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: Spain
Grant or Contract Numbers: N/A
Author Affiliations: N/A