Re-watch occurs when a learner plays a segment of a video that they have already seen, whether by replaying from the beginning, seeking back to a specific timestamp, or looping a clip in a player that supports it. In the xAPI Video Profile the seeked verb captures backward seeks with before and after timestamps, allowing an LRS (Learning Record Store) to identify exactly which segment was revisited and how many times across a cohort. As an engagement signal re-watch is ambiguous and must be interpreted in context: a cluster of re-watches concentrated on the same ten-second span of an explanation diagram suggests a comprehension bottleneck that the instructor should address. By contrast, re-watches distributed across an entire case-study walkthrough are more likely to reflect deliberate note-taking rather than confusion. Aggregated across a cohort, re-watch density layered onto the engagement heatmap produces the richest picture of where a video holds attention versus where it produces confusion. Some re-watch is desirable — it indicates the video has lasting reference value and learners are self-remediating. However, a spike correlating with a low score on the associated assessment quiz is a reliable signal that the explanation failed and the segment should be refilmed or supplemented with a worked example.
The handbook · 7 volumes
Fora Soft Video Engineering Handbook
Re-watch data shows where learners got confused, but only if seeks are tracked cleanly and the player rebuffers fast after a jump. The seven volumes cover the whole video stack; for re-watch analytics start with Volume 3 on players, trick play, and QoE monitoring, with keyframe spacing in Volume 1 and clear speech in Volume 4. Written by Nikolay Sapunov, CEO at Fora Soft.
Using re-watch data to find the confusing parts of your course videos?
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