RAOCSL: A BERT-Based Strategy for Identifying Learner Confusion under Class Imbalance

Tongyu Zhao, Jiaying Gao, Yu Feng, Yatong Zu, Adriano Tavares, Tiago Gomes, Sandro Pinto, Hao Xu

Abstract: Understanding and identifying the nature of learner confusion is important for online learning platforms. In this study, we address this problem by analyzing forum posts from large-scale online courses. However, due to the large volume of comments and frequent interactions, confusion posts are often overlooked. Existing methods and models, while capable of detecting confusion, typically rely on linguistic features of posts and community factors (e.g. votes, views) but ignore personalized contexts, such as the specific causes and types of confusion. To address this problem, we create the first deep learning dataset focused on confusion types and develop a BERT-based network to model personalized features and identify confusion types. Considering the highly imbalanced distribution of different types of confusion, we further design a novel loss function that adaptively optimizes the training weights for each type. Our method’s effectiveness is confirmed through extensive experimentation.

External IDs:dblp:conf/icassp/ZhaoGFZTG0025