Understanding in-video dropouts and interaction peaks inonline lecture videos

By Juho Kim, Philip J. Guo, Daniel T. Seaton, Piotr Mitros, Krzysztof Z Gajos, Rob Miller. ACM Conference on Learning @ Scale, 2014

439 citations recorded by the source corpus.

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 re-watching 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.

Subfields: Computer Science Applications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/4a699c2d50e984b200603eec7127f1a3ef31fec8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Science Applications

1. Gregor E. Kennedy
2. Mary D. Ainley
3. Paula de Barba
4. Jane Yin-Kim Yau
5. Dirk Ifenthaler

In Computer Vision and Pattern Recognition

1. Mung Chiang
2. Mary D. Ainley

Citations in the corpus

• Behavior-Based Grade Prediction for MOOCs Via Time Series Neural Networks (2017, background): “on MOOC forums and [15], [16] studied interaction patterns in lecture videos.”
• The role of students' motivation and participation in predicting performance in a MOOC (2016, background): “Video hits could mean different behaviours, such as watching the entire video, dropping out after a certain time, or re-watching only certain parts (Kim et al., 2014).”