Timed vs. Untimed Assessments in Computer Science Education: Mapping Student Engagement and Learning Progression Through Network Analysis

Assessment plays a critical role in shaping learning outcomes in computer science education. This study applies network analysis to examine how different assessment designs—timed and untimed—affect student engagement and learning progression. Using data from an introductory programming course delivered in both online and in-person formats, we construct performance correlation networks to analyze similarities among students over time. Our findings show that untimed assessments, delivered via an interactive textbook platform, enable more individualized engagement patterns and clearer differentiation in learning outcomes. In contrast, timed assessments produce more standardized performance clusters, potentially masking underlying learning variability. Despite differences in modality, we observe consistent patterns across online and in-person settings, underscoring the influence of instructional design over delivery format. This work contributes to computing education by integrating learning analytics and pedagogical analysis, demonstrating how network-based methods can enhance assessment strategy evaluation and support more adaptive educational environments.