Toward building a fair peer recommender to support help-seeking in online learning
Chenglu Li, Wanli Xing, Walter L. Leite
Abstract
Help-seeking is a valuable practice in online discussion forums. However, the asynchronicity and information overload of online discussion forums have made it challenging for help seekers and providers to connect effectively. This study formulated a new method to provide fair and accurate insights toward building a peer recommender to support help-seeking in online learning. Specifically, we developed the fair network embedding (Fair-NE) model and compared it with existing popular models. We trained and evaluated the models with a large dataset consisting of 187,450 discussion post-reply pairs by 10,182 Algebra I online learners from 2015 to 2020. Finally, we examined models with representation fairness, predictive accuracy, and predictive fairness. The results showed that the Fair-NE can achieve superior fairness in genders and races while retaining competitive predictive accuracy. This study marks a paradigm change from previous investigation and evaluation of fair artificial intelligence to proactively build fair artificial intelligence in education.