From data to teaching
Video lessons and learning analytics in blended university contexts
Bibliographic Data
| ID | 22166269 |
|---|---|
| Authors | Federica Pelizzari (0000-0003-2223-7212, University of the Sacred Heart), Elena Tassalini (University of the Sacred Heart), Flavia Maria Scott (University of the Sacred Heart) |
| Year | 2026 |
| Volume | 11 |
| Publication date | 2026-02-02 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Education (JOURNAL) |
| Journal identifiers | ISSN: 2504-284X • E-ISSN: 2504-284X |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/feduc.2026.1677494 |
| OpenAlex | W7127046024 |
| Language | EN |
| References cited | 46 |
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
Analytics · Asynchronous communication · Blended Learning · Descriptive statistics · Formative assessment · Higher education · Learning analytics · Learning Management · Innovative Teaching and Learning Methods · Intelligent Tutoring Systems and Adaptive Learning · Online Learning and Analytics
Multimedia Learning
Cluster Analysis
E‐Learning and the Science of Instruction
Effects of different video lecture types on sustained attention, emotion, cognitive load, and learning performance
Learning analytics should not promote one size fits all
Critical Inquiry in a Text-Based Environment
Students as co‐creators of teaching approaches, course design, and curricula
Data clustering
Effective Educational Videos
Nine Ways to Reduce Cognitive Load in Multimedia Learning
Using learning analytics to scale the provision of personalised feedback
Why Minimal Guidance During Instruction Does Not Work
A Review of Self-regulated Learning
Focus on Formative Feedback
School Engagement
Cognitive Load During Problem Solving
Universal Design for Learning and Instruction
Transforming higher education
Understanding computational thinking in the gameplay of the African Songo board game
Improving teaching practices via virtual reality‐supported simulation‐based learning
An Instructional Design Framework for Fostering Student Engagement in Online Learning Environments
Learning Analytics
Becoming a Self-Regulated Learner
| Citation velocity | historical |
|---|---|
| Highly cited | No |