Online Computer Science Learning: A Comparison Between First-Generation and Continuing-Generation College Students

Abstract\u2014 Contribution: This study provides new insights into the online learning experiences of first-generation college students. By comparing their experiences with continuing-generation peers in an online computer science course, it challenges deficit-based narratives and supports an asset-based view of their success in remote learning environments.Background: The shift to online learning during the COVID-19 pandemic heightened concerns about equity in higher education. First-generation college students, often facing unique barriers, remain underexamined in online computer science contexts. Understanding their learning experiences is key to informing inclusive computer science education.Research Questions: How do first-generation and continuing-generation students differ in their perceptions of the learning environment, motivation, self-regulated learning, and objective academic performance in an online computer science course?Methodology: To address the research question of this study, a series of independent t-tests were employed. The Benjamini-Hochberg procedure was used to control for the false discovery rate for multiple tests of significance. Cohen\u2019s d was computed to gauge the magnitude of the differences.Findings: No statistically significant group differences in students\u2019 perceptions of the learning environment, motivation, self-regulated learning, or course performance. Effect sizes ranged from small to moderate, generally favoring first-generation college students. These findings challenge deficit-based assumptions and support an asset-based view of first-generation college students as capable, motivated, and resilient learners in the context of online computer science education.