Intelligent Tutoring Systems (ITS) enhance personalized learning by predicting student answers to provide immediate and customized instruction. However, recent research has primarily focused on the correctness of the answer rather than the student's performance on specific answer choices, limiting insights into students' thought processes and potential misconceptions. To address this gap, we present MCQStudentBert, an answer forecasting model that leverages the capabilities of Large Language Models (LLMs) to integrate contextual understanding of students' answering history along with the text of the questions and answers. By predicting the specific answer choices students are likely to make, practitioners can easily extend the model to new answer choices or remove answer choices for the same multiple-choice question (MCQ) without retraining the model. In particular, we compare MLP, LSTM, BERT, and Mistral 7B architectures to generate embeddings from students' past interactions, which are then incorporated into a finetuned BERT's answer-forecasting mechanism. We apply our pipeline to a dataset of language learning MCQ, gathered from an ITS with over 10,000 students to explore the predictive accuracy of MCQStudentBert, which incorporates student interaction patterns, in comparison to correct answer prediction and traditional mastery-learning feature-based approaches. This work opens the door to more personalized content, modularization, and granular support.

翻译：智能辅导系统（ITS）通过预测学生答案来提供即时个性化指导，从而增强个性化学习。然而，现有研究主要关注答案的正确性，而非学生在具体选项上的表现，这限制了对学生思维过程及潜在误解的深入洞察。为填补这一空白，我们提出了MCQStudentBert——一种答案预测模型，该模型利用大语言模型（LLMs）的能力，整合对学生答题历史的上下文理解以及问题与选项的文本信息。通过预测学生可能选择的特定答案选项，实践者能够轻松地将模型扩展至新的选项，或为同一多项选择题（MCQ）移除某些选项，而无需重新训练模型。具体而言，我们比较了MLP、LSTM、BERT和Mistral 7B架构，以从学生历史交互中生成嵌入表示，并将其整合到经过微调的BERT答案预测机制中。我们将该流程应用于一个语言学习MCQ数据集（采集自拥有超过10,000名学生的ITS），以探究融合了学生交互模式的MCQStudentBert的预测准确性，并与正确答案预测及传统的基于掌握学习特征的方法进行比较。此项工作为更个性化的内容、模块化设计以及精细化支持开辟了新的途径。