Identifying Learners’ Confusion in a MOOC Forum Across Domains Using Explainable Deep Transfer Learning - Rahaf Alsuhaimi; Omaima Almatrafi (Information)
Massive Open Online Courses (MOOCs) offer highly specialized online courses and have attracted nearly 10 million learners worldwide to participate in various educational programs. These platforms provide discussion forums that allow learners to engage with both their peers and instructors, facilitating idea exchange and seeking assistance, respectively. However, due to the substantial participant-to-instructor ratio, certain posts may go unanswered. Addressing learners’ confusion is crucial. This emotional state, often experienced during the learning journey, necessitates prompt support to prevent potential dropouts. This paper proposes the application of a deep transfer learning method to automate the classification of online discussion posts based on indicators of confusion utilizing the Stanford MOOCPost dataset. The approach involves creating an explainable and adaptable deep learning model through network-based transfer learning across multiple educational domains. This model outperforms baseline methods, achieving an average accuracy of 91%. Additionally, employing data augmentation techniques enhances the model’s generalizability, resulting in an 11% improvement in the F1 score. To mitigate the inherent opacity of the implemented models, Local Interpretable Model-Agnostic Explanation and Shapley Additive Explanation techniques are integrated. These explanations assess the reliability of features and provide supplementary insights into the confusion detection. By pinpointing confused posts, this work assists instructors in delivering timely responses, resolving learner confusion, providing accurate visualization of key contributing words, and reducing the dropout rate. This proactive approach ensures a smoother continuation of the learning process, consequently enhancing learner satisfaction with the educational experience.