  Optimizing students' learning strategies is a crucial component in
intelligent tutoring systems. Previous research has demonstrated the
effectiveness of devising personalized learning strategies for students by
modelling their learning processes through partially observable Markov decision
process (POMDP). However, the research holds the assumption that the student
population adheres to a uniform cognitive pattern. While this assumption
simplifies the POMDP modelling process, it evidently deviates from a real-world
scenario, thus reducing the precision of inducing individual students' learning
strategies. In this article, we propose the homomorphic POMDP (H-POMDP) model
to accommodate multiple cognitive patterns and present the parameter learning
approach to automatically construct the H-POMDP model. Based on the H-POMDP
model, we are able to represent different cognitive patterns from the data and
induce more personalized learning strategies for individual students. We
conduct experiments to show that, in comparison to the general POMDP approach,
the H-POMDP model demonstrates better precision when modelling mixed data from
multiple cognitive patterns. Moreover, the learning strategies derived from
H-POMDPs exhibit better personalization in the performance evaluation.
