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ERIC Number: ED560563
Record Type: Non-Journal
Publication Date: 2015-Jun
Pages: 8
Abstractor: As Provided
Reference Count: 17
Beyond Prediction: First Steps toward Automatic Intervention in MOOC Student Stopout
Whitehill, Jacob; Williams, Joseph; Lopez, Glenn; Coleman, Cody; Reich, Justin
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015)
High attrition rates in massive open online courses (MOOCs) have motivated growing interest in the automatic detection of student "stopout". Stopout classifiers can be used to orchestrate an intervention before students quit, and to survey students dynamically about why they ceased participation. In this paper we expand on existing stop-out detection research by (1) exploring important elements of classifier design such as generalizability to new courses; (2) developing a novel framework inspired by control theory for how to use a classifier's outputs to make intelligent decisions; and (3) presenting results from a "dynamic survey intervention" conducted on 2 HarvardX MOOCs, containing over 40000 students, in early 2015. Our results suggest that surveying students based on an automatic stopout classifier achieves higher response rates compared to traditional post-course surveys, and may boost students' propensity to "come back" into the course. [For complete proceedings, see ED560503.]
International Educational Data Mining Society. e-mail:; Web site:
Publication Type: Reports - Research; Speeches/Meeting Papers
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: International Educational Data Mining Society