[1O3-J-12] Human interface, education aid: education and evaluation

[1O3-J-12-01] Hidden Markov IRT model as a generalization of Bayesian Knowledge Tracing

〇Emiko Tsutsumi¹, Shuhei Shionoya¹, Masaki Uto¹, Maomi Ueno¹ (1. The University of Electro-Communications)

Keywords:Bayesian Knowledge Tracing, Item response theory, Hidden Markov model

To develop learner's ability, a teacher should grasp the learner's knowledge state accurately in a learning process.For this purpose, Bayesian Knowledge Tracing (BKT) has been proposed to infer learner's knowledge state.Although conventional BKT models learner's knowledge state as a discrete value, the learner's knowledge state must be contentious. Based on this idea, we propose a Hidden Markov IRT model as a generalization of Bayesian Knowledge Tracing. In the proposed model, learner's knowledge state takes a continuous value and change according to a Hidden Markov process in a learning process. The proposed model estimates the optimal value of the degree of learner's mastering knowledge from learning data.From some numerical experiments, we demonstrate that the proposed model improves the estimation accuracy of the learner's knowledge state.