Learning analytics on video-viewing engagement in a flipped statistics course: Relating external video-viewing patterns to internal motivational dynamics and performance

Abstract

This study attempts to advance the comprehension of clickstream data in video-based learning environments and interpret learning motivation from these large-volume and unstructured data. Three hundred and fifty video-learning records from 47,044 video viewing operations were obtained from 47 graduate students. Four learning improvement profiles were derived from a statistics course's pre- and post-test scores: the Advanced, the Diligent, the Indifferent, and the Persistent. The results indicated that the Diligent and the Persistent paused videos frequently. These students reflected the need with these self-paced breaks to take notes according to the retrospective interview. The Advanced demonstrated the highest SkippingBackward frequency with a small SkippingBackward time ratio, revealing confidence in searching the desired clips. In contrast, the Indifferent exhibited the least frequency but the largest time ratio for SkippingBackward, implying distraction problems. In addition, this study showed that the Pause time ratio was indirectly related to weekly quiz scores via autonomous motivation with marginal significance. Based on these results, we demonstrate that learning motivation can be revealed from dynamic clickstreams in video-based learning (i.e., the interaction between students and learning contexts), supporting its dynamic developmental mechanism. The authors suggest that instructors offer on-demand instructional materials and implement top-down video-viewing strategies or digital prompts to support and encourage autonomy in video-based learning.