Click-level MOOC Video Analysis
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.
Note:
The video features were generated by the MOOCAnalysis tool (on the development page) I developed for Coursera data. The tool was also used in another research work, with side publications. The second paper has participated the machine learning contest for predicting MOOC dropouts, and we won the first place!!!!
Sinha, T., Jermann, P., Li, N., & Dillenbourg, P. (2014). Your click decides your fate: Inferring Information Processing and Attrition Behavior from MOOC Video Clickstream Interactions. EMNLP 2014, 3.
Sinha, T., Li, N., Jermann, P., & Dillenbourg, P. (2014). Capturing “attrition intensifying” structural traits from didactic interaction sequences of MOOC learners. EMNLP 2014, 42.
Main Publications:
Li, N., Kidzinski, L., Jermann, P., & Dillenbourg, P. (2015). How Do In-video Interactions Reflect Perceived Video Difficulty?. In Proceedings of the European MOOCs Stakeholder Summit 2015 (No. EPFL-CONF-207968, pp. 112-121). PAU Education.
Note:
The video features were generated by the MOOCAnalysis tool (on the development page) I developed for Coursera data. The tool was also used in another research work, with side publications. The second paper has participated the machine learning contest for predicting MOOC dropouts, and we won the first place!!!!
Sinha, T., Jermann, P., Li, N., & Dillenbourg, P. (2014). Your click decides your fate: Inferring Information Processing and Attrition Behavior from MOOC Video Clickstream Interactions. EMNLP 2014, 3.
Sinha, T., Li, N., Jermann, P., & Dillenbourg, P. (2014). Capturing “attrition intensifying” structural traits from didactic interaction sequences of MOOC learners. EMNLP 2014, 42.
Main Publications:
Li, N., Kidzinski, L., Jermann, P., & Dillenbourg, P. (2015). How Do In-video Interactions Reflect Perceived Video Difficulty?. In Proceedings of the European MOOCs Stakeholder Summit 2015 (No. EPFL-CONF-207968, pp. 112-121). PAU Education.
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