TY - GEN
T1 - RankwithTA
T2 - 2017 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2017
AU - Fang, Hui
AU - Wang, Yufeng
AU - Jin, Qun
AU - Ma, Jianhua
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China under Grant 61171092, and the JiangSu Educational Bureau Project under Grant 14KJA510004.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - Massive Online Open Courses (MOOCs) have the potential to revolutionize higher education with their wide outreach and accessibility. One of key challenges in MOOCs is the student evaluation: The large number of students makes it infeasible for instructors or teaching assistants (TAs) to grade all assignments. Peer grading-having students assess each other-is a promising approach to tackling the problem of evaluation at scale. The user evaluations are then used directly, or aggregated into a consensus value. However, lacking an incentive scheme, users have no motive in making effort in completing the evaluations, providing inaccurate answers instead. To address the above issues, we propose and implement a peer grading scheme, RankwithTA. Specifically, considering that the quality of a student determines both her performance in the assignment and her grading ability, RankwithTA makes the grade each student received depend on both the quality of the solution they submitted, and on the quality of their review and grading work to incentivize students' correct grading, Furthermore, the ground truth is incorporated, which utilizes external calibration by having some students graded by instructors or TAs to provide a basis for accuracy. The simulation results illustrate that RankwithTA performs better than the existing schemes.
AB - Massive Online Open Courses (MOOCs) have the potential to revolutionize higher education with their wide outreach and accessibility. One of key challenges in MOOCs is the student evaluation: The large number of students makes it infeasible for instructors or teaching assistants (TAs) to grade all assignments. Peer grading-having students assess each other-is a promising approach to tackling the problem of evaluation at scale. The user evaluations are then used directly, or aggregated into a consensus value. However, lacking an incentive scheme, users have no motive in making effort in completing the evaluations, providing inaccurate answers instead. To address the above issues, we propose and implement a peer grading scheme, RankwithTA. Specifically, considering that the quality of a student determines both her performance in the assignment and her grading ability, RankwithTA makes the grade each student received depend on both the quality of the solution they submitted, and on the quality of their review and grading work to incentivize students' correct grading, Furthermore, the ground truth is incorporated, which utilizes external calibration by having some students graded by instructors or TAs to provide a basis for accuracy. The simulation results illustrate that RankwithTA performs better than the existing schemes.
KW - Accuracy
KW - Incentive
KW - Massive Online Open Courses (MOOCs)
KW - Peer grading
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UR - http://www.scopus.com/inward/citedby.url?scp=85047190398&partnerID=8YFLogxK
U2 - 10.1109/TALE.2017.8252331
DO - 10.1109/TALE.2017.8252331
M3 - Conference contribution
AN - SCOPUS:85047190398
T3 - Proceedings of 2017 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2017
SP - 497
EP - 502
BT - Proceedings of 2017 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 12 December 2017 through 14 December 2017
ER -