Transfer Learning for Predictive Models in Massive Open Online Courses

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9112)

Abstract

Data recorded while learners are interacting with Massive Open Online Courses (MOOC) platforms provide a unique opportunity to build predictive models that can help anticipate future behaviors and develop interventions. But since most of the useful predictive problems are defined for a real-time framework, using knowledge drawn from the past courses becomes crucial. To address this challenge, we designed a set of processes that take advantage of knowledge from both previous courses and previous weeks of the same course to make real time predictions on learners behavior. In particular, we evaluate multiple transfer learning methods. In this article, we present our results for the stopout prediction problem (predicting which learners are likely to stop engaging in the course). We believe this paper is a first step towards addressing the need of transferring knowledge across courses.

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References

  1. 1.
    Balakrishnan, G., Coetzee, D.: Predicting student retention in massive open online courses using hidden markov models. In: Technical report No. UCB/EECS-2013-109. EECS, University of California, Berkeley (2013)Google Scholar
  2. 2.
    Caruana, R.: Multitask Learning. Springer (1998)Google Scholar
  3. 3.
    Chelba, C., Acero, A.: Adaptation of maximum entropy capitalizer: Little data can help a lot. Computer Speech & Language 20(4), 382–399 (2006)CrossRefGoogle Scholar
  4. 4.
    Halawa, S., Greene, D., Mitchell, J.: Dropout prediction in moocs using learner activity features. In: Proceedings of the European MOOC Summit, EMOOCs (2014)Google Scholar
  5. 5.
    Huang, J., Gretton, A., Borgwardt, K.M., Schölkopf, B., Smola, A.J.: Correcting sample selection bias by unlabeled data. In: Advances in neural information processing systems, pp. 601–608 (2006)Google Scholar
  6. 6.
    Lauría, E.J.M., Baron, J.D., Devireddy, M., Sundararaju, V., Jayaprakash, S.M.: Mining academic data to improve college student retention: an open source perspective. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 139–142. ACM (2012)Google Scholar
  7. 7.
    Lykourentzou, I., Giannoukos, I., Nikolopoulos, V., Mpardis, G., Loumos, V.: Dropout prediction in e-learning courses through the combination of machine learning techniques. Computers & Education 53(3), 950–965 (2009)CrossRefGoogle Scholar
  8. 8.
    Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22(10), 1345–1359 (2010)CrossRefGoogle Scholar
  9. 9.
    Street, H.D.: Factors influencing a learners’ decision to drop-out or persist in higher education distance learning. Online Journal of Distance Learning Administration 13(4) (2010)Google Scholar
  10. 10.
    Taylor, C., Veeramachaneni, K., O’Reilly, U.-M.: Likely to stop? predicting stopout in massive open online courses (2014). arXiv preprint arXiv:1408.3382
  11. 11.
    Tyler-Smith, K.: Early attrition among first time elearners: A review of factors that contribute to drop-out, withdrawal and non-completion rates of adult learners undertaking elearning programmes. Journal of Online learning and Teaching 2(2), 73–85 (2006)Google Scholar
  12. 12.
    Veeramachaneni, K., O’Reilly, U.-M., Taylor, C.: Towards feature engineering at scale for data from massive open online courses (2014). arXiv preprint arXiv:1407.5238
  13. 13.
    Yang, Q., Pan, S.J.: Transfer learning and applications. In: Intelligent Information Processing, p. 2 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Computer Science and Artificial Intelligence LaboratoryMassachusetts Institute of TechnologyCambridgeUSA

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