Unveiling the Relationship Across Students’ Feedback, Video Viewing Logs and Achievement in Video-Based Learning

The rapid adoption of Video-Based Learning (VBL) as an asynchronous form of learning has presented educators with the challenge of assessing student engagement and understanding in the absence of face-to-face interaction. This study aims to shed light on the “black box” of how students use this kind of resources and with what outcomes by examining the relationship between video interaction logs, quiz scores, and self-reported metrics on perceived difficulty, understanding, and prior knowledge. The study involved a diverse sample of 32 university students with backgrounds ranging from STEM to humanities, who watched a video on the Evoli platform about machine learning. Employing a K-Means cluster analysis approach, we identified three distinct learner profiles: Regular Viewers, Careful Viewers, and Superficial Viewers. These profiles were characterized by unique patterns of video interactions, such as playback rates, pausing, and rewatching behaviors. Enriching these profiles with additional metrics, such as quiz scores and self-reported data, revealed nuanced learning trajectories within each cluster, with Careful Viewers demonstrating the most significant improvement despite initial knowledge disadvantages. Our findings underscore the influence of prior knowledge on video engagement behaviors, with learners possessing a strong foundation exhibiting more streamlined viewing patterns. Furthermore, the study highlights the value of deep engagement, characterized by deliberate pausing and rewatching, in facilitating knowledge acquisition and understanding. Despite the limitations posed by the controlled laboratory setting and the sample size, the insights gleaned from this research hold promising implications for enhancing instructors’ awareness in VBL contexts.