Learner Affect Through the Looking Glass: Characterization and Detection of Confusion in Online Courses

Ziheng Zeng, Snigdha Chaturvedi, Suma Bhat
2017 Educational Data Mining  
Characterizing the nature of students' affective and emotional states and detecting them is of fundamental importance in online course platforms. In this paper, we study this problem by using discussion forum posts derived from large open online courses. We find that posts identified as encoding confusion are actually manifestations of different learner affects pertaining to their informational needsprimarily seeking factual answers. We quantitatively demonstrate that the use of content-related
more » ... linguistic features and communityrelated features derived from a post serve as reliable detectors of confusion while widely outperforming currently available algorithms of confusion detection. We also point out that several prediction tasks in this domain (e.g., confusion and urgency detection) can be correlated, and that a model trained for one task can effectively be used for making predictions on the other task without requiring labeled examples. Finally, we highlight a very significant problem of adapting the classifier to unseen courses.
dblp:conf/edm/ZengCB17 fatcat:4bv6b4k3dfcbjge4ph6mv6bn6y