TSP: Truthful Grading-Based Strategyproof Peer Selection for MOOCs

Yufeng Wang, Hui Fang, Chonghu Cheng, Qun Jin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Considering that the challenges in using peer grading to select the best-k students in MOOCs (massive open online courses) are twofold: first is strategyproof, i.e., students should not benefit by untruthfully reporting valuations; second, instead of exerting (costly) efforts to evaluate, students may randomly provide (or just guess) the evaluations on other peers. This paper proposes a truthful grading-based strategyproof peer selection scheme for MOOCs, TSP. Specifically, all students are partitioned into same-size clusters, and each student only evaluates students in other clusters. Moreover, peer prediction mechanism was utilized to motivate each student to truthfully report their gradings through comparing their reports with other peer students who conduct same evaluation tasks. The theoretical analysis and simulation results show that TSP can both stimulate students to truthfully reporting their gradings, and meanwhile select best k-students in strategyproof way.

Original languageEnglish
Title of host publicationProceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018
EditorsMark J.W. Lee, Sasha Nikolic, Gary K.W. Wong, Jun Shen, Montserrat Ros, Leon C. U. Lei, Neelakantam Venkatarayalu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages679-684
Number of pages6
ISBN (Electronic)9781538665220
DOIs
Publication statusPublished - 2019 Jan 16
Event2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018 - Wollongong, Australia
Duration: 2018 Dec 42018 Dec 7

Publication series

NameProceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018

Conference

Conference2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018
CountryAustralia
CityWollongong
Period18/12/418/12/7

Keywords

  • massive online open course (MOOC)
  • peer selection
  • strategyproof
  • truthful grading

ASJC Scopus subject areas

  • Education
  • Artificial Intelligence
  • Computer Networks and Communications
  • Engineering (miscellaneous)

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  • Cite this

    Wang, Y., Fang, H., Cheng, C., & Jin, Q. (2019). TSP: Truthful Grading-Based Strategyproof Peer Selection for MOOCs. In M. J. W. Lee, S. Nikolic, G. K. W. Wong, J. Shen, M. Ros, L. C. U. Lei, & N. Venkatarayalu (Eds.), Proceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018 (pp. 679-684). [8615340] (Proceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TALE.2018.8615340