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

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 ob-tained from 47 graduate students. Four learning improvement profiles were derived from a sta-tistics 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 Skipping-Backward, 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 signifi-cance. 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.