Providing effective and personalized support for students solving Math Word Problems (MWPs) in Algebra Intelligent Tutoring Systems (ITS) remains a significant challenge. Current help systems often rely on rule-based approaches limiting their adaptability to individual student needs and hindering proactive error detection. This work proposes a novel approach leveraging Hidden Markov Models (HMMs) to model user action sequences during MWP solving. We trained HMMs on a dataset of student interaction logs from an algebra ITS, where each action represents an observation and latent states represent the level of understanding. The HMM can effectively capture the dynamics of student’s understanding level by probabilistically modelling the relationship between actions. The trained HMM models were evaluated on their ability to predict student actions and identify potential errors compared to the system’s existing rule-based help and a Hidden Markov model. Results demonstrate that the HMM-based approach achieves significantly higher accuracy in predicting subsequent actions.