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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)

Abstract

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.

Keywords

MOOC Recommendation Prerequisite Subsequent Location 

Notes

Acknowledgment

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

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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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