Modelling Students' Algebraic Knowledge with Dynamic Bayesian Networks
Henrique M. Seffrin, Ig Ibert Bittencourt, Seiji Isotani, Patrícia A. Jaques · 2016
This paper presents a dynamic Bayesian network model for the assessment of students' algebraic knowledge in step-based intelligent tutoring systems. The proposed work assesses knowledge about concept, skills, and misconceptions of learners. Furthermore, the proposed model is independent of the problems provided by the system (i.e., equations), because it considers the algebraic operation used by the student to solve a step as evidence instead of the final solution provided by the student. Results of evaluations comparing student's performance on a posttest with the inference of the proposed model showed statistically significant similarities between them, indicating that the inference performed by the model was accurate.