TSP: Truthful Grading-Based Strategyproof Peer Selection for MOOCs

Yufeng Wang, Hui Fang, Chonghu Cheng, Qun Jin

研究成果

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018
編集者Mark J.W. Lee, Sasha Nikolic, Gary K.W. Wong, Jun Shen, Montserrat Ros, Leon C. U. Lei, Neelakantam Venkatarayalu
出版社Institute of Electrical and Electronics Engineers Inc.
ページ679-684
ページ数6
ISBN(電子版)9781538665220
DOI
出版ステータスPublished - 2019 1 16
イベント2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018 - Wollongong, Australia
継続期間: 2018 12 42018 12 7

出版物シリーズ

名前Proceedings 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
国/地域Australia
CityWollongong
Period18/12/418/12/7

ASJC Scopus subject areas

  • 教育
  • 人工知能
  • コンピュータ ネットワークおよび通信
  • 工学(その他)

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