TY - GEN
T1 - TSP
T2 - 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018
AU - Wang, Yufeng
AU - Fang, Hui
AU - Cheng, Chonghu
AU - Jin, Qun
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (PPZY2015A0 4), JiangSu Educational Bureau Project under Grant 14JK A5140 , and State eK y Laboratory of Novel Software Technology under grant FK TK 20 7B14.
Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/16
Y1 - 2019/1/16
N2 - 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.
AB - 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.
KW - massive online open course (MOOC)
KW - peer selection
KW - strategyproof
KW - truthful grading
UR - http://www.scopus.com/inward/record.url?scp=85062085811&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062085811&partnerID=8YFLogxK
U2 - 10.1109/TALE.2018.8615340
DO - 10.1109/TALE.2018.8615340
M3 - Conference contribution
AN - SCOPUS:85062085811
T3 - Proceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018
SP - 679
EP - 684
BT - Proceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018
A2 - Lee, Mark J.W.
A2 - Nikolic, Sasha
A2 - Wong, Gary K.W.
A2 - Shen, Jun
A2 - Ros, Montserrat
A2 - Lei, Leon C. U.
A2 - Venkatarayalu, Neelakantam
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 December 2018 through 7 December 2018
ER -