TY - GEN
T1 - A Crowd–AI Collaborative Approach to Address Demographic Bias for Student Performance Prediction in Online Education
AU - Zong, Ruohan
AU - Zhang, Yang
AU - Stinar, Frank
AU - Shang, Lanyu
AU - Zeng, Huimin
AU - Bosch, Nigel
AU - Wang, Dong
N1 - This research is supported in part by the National Science Foundation under Grant No. IIS-2202481, CHE-2105005, IIS-2008228, CNS-1845639, CNS-1831669. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the offcial policies, either expressed or implied, of the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
PY - 2023
Y1 - 2023
N2 - Recent advances in artifcial intelligence (AI) and crowdsourcing have shown success in enhancing learning experiences and outcomes in online education. This paper studies a student performance prediction problem where the objective is to predict students’ outcomes in online courses based on their behavioral data. In particular, we focus on addressing the limitation of current student performance prediction solutions that often make inaccurate predictions for students from underrepresented demographic groups due to the lack of training data and differences in behavioral patterns across groups. We develop DebiasEdu, a crowd–AI collaborative debias framework that melds the AI and crowd intelligence through 1) a novel gradient-based bias identifcation mechanism and 2) a bias-aware crowdsourcing interface and bias calibration design to achieve an accurate and fair student performance prediction. Evaluation results on two online courses demonstrate that DebiasEdu consistently outperforms stateof-the-art AI, fair AI, and crowd–AI baselines by achieving an optimized student performance prediction in terms of both accuracy and fairness.
AB - Recent advances in artifcial intelligence (AI) and crowdsourcing have shown success in enhancing learning experiences and outcomes in online education. This paper studies a student performance prediction problem where the objective is to predict students’ outcomes in online courses based on their behavioral data. In particular, we focus on addressing the limitation of current student performance prediction solutions that often make inaccurate predictions for students from underrepresented demographic groups due to the lack of training data and differences in behavioral patterns across groups. We develop DebiasEdu, a crowd–AI collaborative debias framework that melds the AI and crowd intelligence through 1) a novel gradient-based bias identifcation mechanism and 2) a bias-aware crowdsourcing interface and bias calibration design to achieve an accurate and fair student performance prediction. Evaluation results on two online courses demonstrate that DebiasEdu consistently outperforms stateof-the-art AI, fair AI, and crowd–AI baselines by achieving an optimized student performance prediction in terms of both accuracy and fairness.
UR - http://www.scopus.com/inward/record.url?scp=85205105346&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85205105346&partnerID=8YFLogxK
U2 - 10.1609/hcomp.v11i1.27560
DO - 10.1609/hcomp.v11i1.27560
M3 - Conference contribution
AN - SCOPUS:85205105346
SN - 9781577358848
T3 - Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, HCOMP
SP - 198
EP - 210
BT - HCOMP 2023 - Proceedings of the 11th AAAI Conference on Human Computation and Crowdsourcing
A2 - Bernstein, M.
A2 - Bozzon, A.
PB - Association for the Advancement of Artificial Intelligence
T2 - 11th AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2023
Y2 - 6 November 2023 through 9 November 2023
ER -