Forgetting Punished Recommendations for MOOC

  • Yanxia PangEmail author
  • Liping Li
  • Wenan Tan
  • Yuanyuan Jin
  • Ying Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11280)


Prerequisite inadequacy tends to cause more drop-out of MOOC. Recommendation is an effective method of learning intervene. Existing recommendation for MOOC is mainly for subsequent learning objects that have not been learned before. This paper proposes a solution called Forgetting-punished MOOC Recommendation (FMR). FMR combines the forgetting effect on learning score as a main feature for recommendation. It provides Prerequisite Recommendation (PR) for the unqualified learning objects and Subsequent Recommendation (SR) for the qualified objects. Experiments verify the accuracy improvement of PR and SR.


MOOC Recommendation Prerequisite Subsequent Location 



The work is funded by computer science and technology subject of Shanghai Polytechnic University with No. xxkzd1604.


  1. 1.
    Breslow, L., Pritchard, D.E., De Boer, J., Stump, G.S., Ho, A.D., Seaton, D.T.: Studying learning in the worldwide classroom: research into edX’s first MOOC. Res. Pract. Assess. 8, 13–25 (2013)Google Scholar
  2. 2.
    Kizilcec, R.F., Piech, C., Schneider, E.: Deconstructing disengagement: analyzing learner subpopulations in massive open online courses. In: Proceedings of the Third International Conference on Learning Analytics and Knowledge, pp. 170–179. ACM (2013)Google Scholar
  3. 3.
    Pappano, L.: The year of the MOOC. New York Times, New York (2012)Google Scholar
  4. 4.
    Polyzou, A., Karypis, G.: Grade prediction with course and student specific models. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J.Z., Wang, R. (eds.) PAKDD 2016. LNCS (LNAI), vol. 9651, pp. 89–101. Springer, Cham (2016). Scholar
  5. 5.
    Yang, Y., Liu, H., Carbonell, J., Ma, W.: Concept graph learning from educational data. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, pp. 159–168. ACM (2015)Google Scholar
  6. 6.
    Liu, J., Jiang, L., Wu, Z., Zheng, Q., Qian, Y.: Mining learning dependency between knowledge units from text. VLDB J. Int. J. Very Large Data Bases 20(3), 335–345 (2011)CrossRefGoogle Scholar
  7. 7.
    Huang, X., Yang, K., Lawrence, V.B.: An efficient data mining approach to concept map generation for adaptive learning. In: Perner, P. (ed.) ICDM 2015. LNCS (LNAI), vol. 9165, pp. 247–260. Springer, Cham (2015). Scholar
  8. 8.
    Scheines, R., Silver, E., Goldin, I.M.: Discovering prerequisite relationships among knowledge components. In: EDM, pp. 355–356 (2014)Google Scholar
  9. 9.
    Vuong, A., Nixon, T., Towle, B.: A method for finding prerequisites within a curriculum. In: EDM, pp. 211–216 (2011)Google Scholar
  10. 10.
    Liang, C., Wu, Z., Huang, W., Giles, C.L.: Measuring prerequisite relations among concepts. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1668–1674 (2015)Google Scholar
  11. 11.
    Talukdar, P.P., Cohen, W.W.: Crowd sourced comprehension: predicting prerequisite structure in Wikipedia. In: Proceedings of the Seventh Workshop on Building Educational Applications Using NLP, pp. 307–315. Association for Computational Linguistics (2012)Google Scholar
  12. 12.
    Wang, S., et al.: Using prerequisites to extract concept maps from textbooks. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 317–326. ACM (2016)Google Scholar
  13. 13.
    Agrawal, R., Golshan, B., Terzi, E.: Grouping students in educational settings. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1017–1026. ACM (2014)Google Scholar
  14. 14.
    Lu, Z., Pan, S.J., Li, Y., Jiang, J., Yang, Q.: Collaborative evolution for user profiling in recommender systems. In: IJCAI, pp. 3804–3810 (2016)Google Scholar
  15. 15.
    Sun, Y., Han, J., Yan, X., Yu, P.S., Wu, T.: Pathsim: meta path-based top-k similarity search in heterogeneous information networks. Proc. VLDB Endow. 4(11), 992–1003 (2011)Google Scholar
  16. 16.
    Chen, Y., Zhao, X., Gan, J., Ren, J., Hu, Y.: Content-based top-N recommendation using heterogeneous relations. In: Cheema, M.A., Zhang, W., Chang, L. (eds.) ADC 2016. LNCS, vol. 9877, pp. 308–320. Springer, Cham (2016). Scholar
  17. 17.
    Yu, H., O’Riedl, M.: A sequential recommendation approach for interactive personalized story generation. In: Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems, vol. 1, pp. 71–78. International Foundation for Autonomous Agents and Multiagent Systems (2012)Google Scholar
  18. 18.
    Huang, Y.-M., Huang, T.-C., Wang, K.-T., Hwang, W.-Y.: A Markov-based recommendation model for exploring the transfer of learning on the web. J. Educ. Technol. Soc. 12(2), 144 (2009)Google Scholar
  19. 19.
    Mi, F., Faltings, B.: Adaptive sequential recommendation using context trees. In: IJCAI, pp. 4018–4019 (2016)Google Scholar
  20. 20.
    Lee, Y., Cho, J.: An intelligent course recommendation system. SmartCR 1(1), 69–84 (2011)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Education Growth Advisors: Learning to adapt: understanding the adaptive learning supplier landscape. PLN/Bill and Melinda Gates Foundation (2013)Google Scholar
  22. 22.
    Ebbinghaus, H.: Memory: a contribution to experimental psychology. Ann. Neurosci. 20(4), 155 (2013)CrossRefGoogle Scholar
  23. 23.
    Schacter, D.L.: The seven sins of memory: insights from psychology and cognitive neuroscience. Am. Psychol. 54(3), 182 (1999)CrossRefGoogle Scholar
  24. 24.
    Averell, L., Heathcote, A.: The form of the forgetting curve and the fate of memories. J. Math. Psychol. 55(1), 25–35 (2011)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 43–52. Morgan Kaufmann Publishers Inc. (1998)Google Scholar
  26. 26.
    Clark, D.: Adaptive MOOCs. CogBooks adaptive learning. Copyright CogBooks (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yanxia Pang
    • 1
    • 2
    Email author
  • Liping Li
    • 2
  • Wenan Tan
    • 2
  • Yuanyuan Jin
    • 1
  • Ying Zhang
    • 1
  1. 1.East China Normal UniversityShanghaiChina
  2. 2.Shanghai Polytechnic UniversityShanghaiChina

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