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

In this work, we explore video lecture interaction in Massive Open Online Courses (MOOCs), which is central to student learning experience on these educational platforms. As a research contribution, we operationalize video lecture clickstreams of students into cognitively plausible higher level behaviors, and construct a quantitative information processing index, which can aid instructors to better understand MOOC hurdles and reason about unsatisfactory learning outcomes. Our results illustrate how such a metric inspired by cognitive psychology can help answer critical questions regarding students' engagement, their future click interactions and participation trajectories that lead to in-video & course dropouts. Implications for research and practice are discusse

Understanding in-video dropouts and interaction peaks in online lecture videos

Engineering and Applied Science

Videos in higher education: making the most of a good thing

There has been a noticeable rise in the use of, and research into, educational videos in tertiary education in the past decade. This is due in no small part to the reduction of expensive barriers to their production and storage, and an increase in access to streaming services that make videos playable anywhere, anytime. Research into educational videos broadly falls into three categories, namely improving video design; investigating platforms and technologies that increase student engagement with videos for a diverse range of learners; and developing pedagogical approaches that take full advantage of the affordances of videos. This article outlines the valuable contributions authors in this and other publications have made to each of these lines of enquiry, and highlights areas in need of further inquiry. Research into the improvement of theory and practice these three focus areas is needed to guide the development educational videos

How Do In-video Interactions Reflect Perceived Video Difficulty?

Lecture videos are the major components in MOOCs. It is common for MOOC analytics researchers to model video behaviors in order to identify at-risk students. Much of the work emphasized prediction. However, we have little empirical understanding about these video interactions, especially at the click-level. For example, what kind of video interactions may indicate a student has experienced difficulty? To what extent can video interactions tell us about perceived video difficulty? In this paper, we present a video interaction analysis to provide empirical evidence about this issue. We find out that speed decreases, frequent and long pauses, infrequent seeks with high amount of skipping and re-watching indicate higher level of video difficulty. MOOC practitioners and instructors may use the insights to provide students with proper support to enhance the learning experience

MOOC Video Interaction Patterns: What Do They Tell Us?

For MOOC learners, lecture video viewing is the central learning activity. This paper reports a large-scale analysis of in-video interactions. We categorize the video behaviors into patterns by employ- ing a clustering methodology, based on the available types of interactions, namely, pausing, forward and backward seeking and speed changing. We focus on how learners view MOOC videos with these interaction patterns, especially on exploring the relationship between video interaction and perceived video difficulty, video revisiting behaviors and student performance. Our findings provide insights for improving the MOOC learning experiences

Making Sense of Video Analytics: Lessons Learned from Clickstream Interactions, Attitudes, and Learning Outcome in a Video-Assisted Course

Online video lectures have been considered an instructional media for various pedagogic approaches, such as the flipped classroom and open online courses. In comparison to other instructional media, online video affords the opportunity for recording student clickstream patterns within a video lecture. Video analytics within lecture videos may provide insights into student learning performance and inform the improvement of video-assisted teaching tactics. Nevertheless, video analytics are not accessible to learning stakeholders, such as researchers and educators, mainly because online video platforms do not broadly share the interactions of the users with their systems. For this purpose, we have designed an open-access video analytics system for use in a video-assisted course. In this paper, we present a longitudinal study, which provides valuable insights through the lens of the collected video analytics. In particular, we found that there is a relationship between video navigation (repeated views) and the level of cognition/thinking required for a specific video segment. Our results indicated that learning performance progress was slightly improved and stabilized after the third week of the video-assisted course. We also found that attitudes regarding easiness, usability, usefulness, and acceptance of this type of course remained at the same levels throughout the course. Finally, we triangulate analytics from diverse sources, discuss them, and provide the lessons learned for further development and refinement of video-assisted courses and practices