Do Gender and Race Matter? Supporting Help-Seeking with Fair Peer Recommenders in an Online Algebra Learning Platform

Abstract

Discussion forums are important for students’ knowledge inquiry in online contexts, with help-seeking being an essential learning strategy in discussion forums. This study aimed to explore innovative methods to build a peer recommender that can provide fair and accurate intelligence to support help-seeking in online learning. Specifically, we have examined existing network embedding models, Node2Vec and FairWalk, to benchmark with the proposed fair network embedding (Fair-NE). A dataset of 187,450 post-reply pairs by 10,182 Algebra I students from 2015 to 2020 was sampled from Algebra Nation, an online algebra learning platform. The dataset was used to train and evaluate the engines of peer recommenders. We evaluated models with representation fairness, predictive accuracy, and predictive fairness. Our findings suggest that constructing fairness-aware models in learning analytics (LA) is crucial to tackling the potential bias in data and to creating trustworthy LA systems.