DEAba: A Paper-Based, Data-Driven Methodology for Deep Learning of Metabolism

Abstract Mastering metabolic concepts remains a persistent challenge for undergraduate students due to the abstract nature of the concepts, the intricate pathway interconnections, and the necessity of integrative reasoning. The common focus of prior educational stages on low-order cognitive assessment exacerbates this challenge, reinforcing surface-level learning and leading to widespread underpreparedness in incoming students. To address this systemic issue, we developed the Differential Expression Analysis-based activity (DEAba): a cooperative, paper-based yet data-intensive methodology that engages students in interpreting adapted real-world omics data sets. Grounded in established learning frameworks (Cognitive Load Theory, ICAP, and Bloom’s taxonomy), DEAba combines data-driven reasoning with analog collaboration to foster conceptual integration and address the high intrinsic cognitive load of metabolism. Across seven academic years (n = 1,212 students) in an undergraduate Biochemistry course, DEAba implementation was associated with significant improvements in final exam performance (p = 0.001), exam participation, and pass rates, while perception surveys revealed high levels of perceived usefulness, engagement, and conceptual clarity. These findings suggest that theoretically grounded, low-cost analog designs may support improved learning outcomes in challenging biochemical topics. DEAba offers instructors a scalable, evidence-based, and readily transferable tool for promoting active learning and deep, integrated understanding of metabolism without requiring technological infrastructure.