Abstract

Online discussion forums are highly valued by instructors due to their affordance for understanding class activities and learning. However, a discussion forum with a great number of posts requires a large amount of time to view, and help requests are easily overlooked. Various machine-learning–based tools have been developed to help instructors monitor or identify posts that require immediate responses. However, the black-box nature of deep learning cannot explain why and how decisions are achieved, raising trust and reliability issues. To address the gap, this work developed an explainable text classifier framework based on a model originally designed for legal services. We used the Stanford MOOCPost dataset to identify posts of confusion. Our results showed that the framework can not only identify discussion forum posts with confusion of different levels, but also provide explanation in terms of words from the identified posts.

摘要

在线讨论论坛因其理解课堂活动和学习的能力而受到教师的高度重视。然而，一个拥有大量帖子的论坛需要大量的时间来查看，而帮助请求很容易被忽视。已经开发了各种基于机器学习的工具来帮助教师监控或识别需要立即响应的帖子。然而，深度学习的黑盒性质无法解释为什么以及如何做出决策，从而引发信任和可靠性问题。为了弥补这一差距，这项工作基于最初为法律服务设计的模型开发了一个可解释的文本分类器框架。我们使用 Stanford MOOCPost 数据集来识别混淆帖子。