The main focus of research on Knowledge Tracing (KT) models is on model developments with the aim of improving predictive accuracy. Most of these models make the most incorrect predictions when students choose a distractor, leading to student errors going undetected. We present an approach to add new capabilities to KT models by capturing predictive uncertainty and demonstrate that a larger predictive uncertainty aligns with model incorrect predictions. We show that uncertainty in KT models is informative and that this signal would be pedagogically useful for application in an educational learning platform that can be used in a limited resource setting where understanding student ability is necessary.

翻译：知识追踪模型的研究主要聚焦于模型开发，旨在提升预测准确性。当学生选择干扰项时，这些模型大多会产生最不准确的预测，导致学生错误无法被识别。我们提出了一种通过捕捉预测不确定性来增强知识追踪模型能力的方法，并证明较大的预测不确定性与模型预测错误具有一致性。研究表明，知识追踪模型中的不确定性信息具有参考价值，该信号在教育学习平台的应用中具有教学实用性，尤其适用于需要理解学生能力且资源有限的环境。