From data to teaching: video lessons and learning analytics in blended university contexts - Pelizzari, Federica; Tassalini, Elena; Scott, Flavia Maria (Frontiers in Education, 2026)
This study investigates how students use video lessons within a blended master’s degree program (LM-93), addressing a persistent research gap: most studies investigate perceived usefulness or self-reported engagement, whereas far fewer analyze trace-based behavioral data in authentic higher education contexts. Understanding real usage patterns is crucial, as pedagogical effectiveness depends not only on content quality but also on how students interact with video-based materials. While many studies focus on self-reported satisfaction or outcomes, fewer examine fine-grained behavioral data in authentic blended university contexts. Drawing on evidence-based research on educational innovation, we analyzed 5,278 viewing sessions tracked through the Panopto platform across 14 courses over two academic years. A multi-step quantitative approach was applied, combining descriptive statistics, correlations, manual segmentation, and K-means clustering (Jain, 2010). Results show substantial heterogeneity in viewing behaviors: for example, only 18% of sessions fall into an “in-depth” profile, characterized by long viewing time, high completion, and frequent interactions. Four stable behavioral profiles emerged (in-depth, fast, partial, and discontinuous) highlighting diverse strategies in time management and engagement. These findings suggest that asynchronous video lessons can support engagement and self-regulation for a subset of students, but also risk reinforcing superficial or fragmented use for others. The study offers actionable implications for instructional design and inclusivity in blended learning: designing shorter and more navigable video lessons, integrating checkpoints and formative feedback, and using Learning Analytics dashboards to support data-informed and Universal Design for Learning–oriented practices. Limitations include the single-institution context, reliance on platform logs, and the absence of qualitative triangulation, which point to future research integrating behavioral, outcome, and qualitative data. The study also highlights the need for transparent and ethically informed uses of Learning Analytics when interpreting behavioral traces.