2026-09-18T22:18:29Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1048322022-01-31T21:15:59Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Kalyan Veeramachaneni.
Boyer, Sebastien (Sebastien Arcario)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Engineering Systems Division
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Technology and Policy Program
2016-10-14T15:54:48Z
2016-10-14T15:54:48Z
2016
2016
http://hdl.handle.net/1721.1/104832
959240507
Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, Technology and Policy Program, 2016.
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 85-87).
Predictive models are crucial in enabling the personalization of student experiences in Massive Open Online Courses. For successful real-time interventions, these models must be transferable - that is, they must perform well on a new course from a different discipline, a different context, or even a different MOOC platform. In this thesis, we first investigate whether predictive models "transfer" well to new courses. We then create a framework to evaluate the "transferability" of predictive models. We present methods for overcoming the biases introduced by specific courses into the models by leveraging a multi-course ensemble of models. Using 5 courses from edX, we show a predictive model that, when tested on a new course, achieved up to a 6% increase in AUCROC across 90 different prediction problems. We then tested this model on 10 courses from Coursera (a different platform) and demonstrate that this model achieves an AUCROC of 0.8 across these courses for the problem of predicting dropout one week in advance. Thus, the model "transfers" very well.
by Sebastien Boyer.
S.M. in Technology and Policy
S.M.
87 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Institute for Data, Systems, and Society.
Engineering Systems Division.
Technology and Policy Program.
Electrical Engineering and Computer Science.
Transfer learning for predictive models in MOOCs
Transfer learning for predictive models in Massive Open Online Courses
Thesis
application/pdf
Publication
unknown
unknown
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<Abstract>Predictive models are crucial in enabling the personalization of student experiences in Massive Open Online Courses. For successful real-time interventions, these models must be transferable - that is, they must perform well on a new course from a different discipline, a different context, or even a different MOOC platform. In this thesis, we first investigate whether predictive models "transfer" well to new courses. We then create a framework to evaluate the "transferability" of predictive models. We present methods for overcoming the biases introduced by specific courses into the models by leveraging a multi-course ensemble of models. Using 5 courses from edX, we show a predictive model that, when tested on a new course, achieved up to a 6% increase in AUCROC across 90 different prediction problems. We then tested this model on 10 courses from Coursera (a different platform) and demonstrate that this model achieves an AUCROC of 0.8 across these courses for the problem of predicting dropout one week in advance. Thus, the model "transfers" very well.</Abstract>
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