Exploring the impact of psychosocial learning environments and instructional modalities on academic achievement in blended computer science education

The goal of the present study was to explore the relations among perceived psychosocial learning environments, instructional modality, motivation, self-regulated learning, and academic achievement in blended computer science education. The participants were 207 undergraduate students enrolled in a blended online and face-to-face design course. We employed exploratory structural equation modeling to analyze the data. Our findings indicated that within the perceived psychosocial learning environments, dimensions such as personal relevance, authentic learning, and active learning significantly predicted various motivational beliefs. Furthermore, active learning and expectancy were strong predictors of effective time management, while instructional modality and emotional cost were closely linked to academic procrastination. Notably, academic procrastination emerged as the sole significant predictor of academic achievement, measured by course grades. These results suggest that traditional curricula emphasizing textbook reading and code memorization may be ineffective in teaching computer science. Additionally, our study highlights a higher tendency for procrastination in online settings. We recommend a curriculum focused on personal relevance, authentic learning, and active learning to better motivate students and enhance their ability to manage their learning effectively. To improve academic achievement in computer science education, it is crucial to address maladaptive self-regulatory processes and motivational beliefs, which primarily arise from active learning and instructional modality. We will conclude with specific recommendations for designing learning environments that better support computer science education.