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ERIC Number: ED560589
Record Type: Non-Journal
Publication Date: 2015-Jun
Pages: 8
Abstractor: As Provided
Reference Count: 27
YouEDU: Addressing Confusion in MOOC Discussion Forums by Recommending Instructional Video Clips
Agrawal, Akshay; Venkatraman, Jagadish; Leonard, Shane; Paepcke, Andreas
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015)
In Massive Open Online Courses (MOOCs), struggling learners often seek help by posting questions in discussion forums. Unfortunately, given the large volume of discussion in MOOCs, instructors may overlook these learners' posts, detrimentally impacting the learning process and exacerbating attrition. In this paper, we present YouEDU, an instructional aid that automatically detects and addresses confusion in forum posts. Leveraging our Stanford MOOC-Posts corpus, we train a set of classifiers to classify forum posts across multiple dimensions. In particular, classifiers that target sentiment, urgency, and other descriptive variables inform a single classifier that detects confusion. We then employ information retrieval techniques to map confused posts to minute-resolution clips from course videos; the ranking over these clips accounts for textual similarity between posts and closed captions. We measure the performance of our classification model in multiple educational contexts, exploring the nature of confusion within each; we also evaluate the relevancy of materials returned by our ranking algorithm. Experimental results demonstrate that YouEDU achieves both its goals, paving the way for intelligent intervention systems in MOOC discussion forums. [For complete proceedings, see ED560503.]
International Educational Data Mining Society. e-mail:; Web site:
Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: International Educational Data Mining Society