Student modeling is a key ingredient for intelligent interaction with students, and understanding how students learn composite concepts can help us build models of higher quality. An interesting question is how machines can help us infer about students' learning processes of composite concepts from students' external performance. Since Bayesian networks have been used in student modeling in many research projects, we employ them for simulating students' performance in taking tests, and seek methods for learning the original networks based on the simulated students' performance. The problem is not easy because the data we can observe have only indirect and uncertain relationship with the variables for mastery levels, and we would like to know the relationships among these hidden variables. We applied mutual information and artificial neural networks for this learning problem, and we achieved 75% in accuracy even when the item responses have really uncertain relationships with the actual mastery levels.
['Chao-Lin Liu']
Learning Students' Learning Patterns with Neural Computing