Understanding in-video dropouts and interaction peaks inonline lecture videos

Abstract

With thousands of learners watching the same online lecture videos, analyzing video watching patterns provides a unique opportunity to understand how students learn with videos. This paper reports a large-scale analysis of in-video dropout and peaks in viewership and student activity, using second-by-second user interaction data from 862 videos in four Massive Open Online Courses (MOOCs) on edX. We find higher dropout rates in longer videos, re-watching sessions (vs first-time), and tutorials (vs lectures). Peaks in rewatching sessions and play events indicate points of interest and confusion. Results show that tutorials (vs lectures) and re-watching sessions (vs first-time) lead to more frequent and sharper peaks. In attempting to reason why peaks occur by sampling 80 videos, we observe that 61% of the peaks accompany visual transitions in the video, e.g., a slide view to a classroom view. Based on this observation, we identify five student activity patterns that can explain peaks: starting from the beginning of a new material, returning to missed content, following a tutorial step, replaying a brief segment, and repeating a non-visual explanation. Our analysis has design implications for video authoring, editing, and interface design, providing a richer understanding of video learning on MOOCs.

MOOC Video Interaction Patterns: What Do They Tell Us?

“…Research on MOOC video interactions are usually centered on analyzing lecture-to-lecture navigation strategies [5], predicting dropout [6,15] and in-video dropouts [7]. Among these work, only [15,7] contain click-level video analysis. [7] emphasizes on the video interaction peaks while ignoring other silent interactions such as speed changing.…”

“…Among these work, only [15,7] contain click-level video analysis. [7] emphasizes on the video interaction peaks while ignoring other silent interactions such as speed changing. [15] analyzed the video interactions sequences with n-gram analysis, which aimed at predicting dropouts.…”

“…Students may revisit an older video for checking concepts while watching new videos or doing homework. Kim et al find out [7] that first-watching sessions are more sequential while the revisiting sessions are more selective, i.e. the students selectively navigate the video into specific parts.…”

Your click decides your fate: Inferring Information Processing and Attrition Behavior from MOOC Video Clickstream Interactions

“…Specific to the context of video lectures, prior work has cut teeth on a)how video production style (slides, code, classroom, khan academy style etc) relates to students' engagement , b)what features of the video lecture and instruction delivery, such as slide transitions (change in visual content), instructor changing topic (topic modeling and ngram analysis) or variations in instructor's acoustic stream (volume, pitch, speaking rate), lead to peaks in viewership activity (Kim et al, 2014b). There has been increasing focus on unveiling numerous facets of complexity of raw click-level interactions resulting from student activities within individual MOOC videos (Kim et al, 2014a;Sinha et al, 2014). However, to the best of our knowledge, we present the first study that describes usage of such detailed clickstream information to form cognitive video watching states that summarize student clickstream.…”

The Myth of the Six-Minute Rule: Student Engagement with Online Videos

“…This analysis revealed five peak types, from which the authors provide recommendations for content authoring and interface design. 3 Other studies have focused on how to use these data to inform student and instructor behavior, focusing on feeding back data through a dashboard, 2 multimedia exercises, 5 and three-dimensional video cubes.…”