  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.
