T-BMIRT: Estimating representations of student knowledge and educational components in online education

摘要

A large amount of data generated by students in online education can be used to improve the quality of education. The important task of online education is to estimate the student proficiency and the characteristics of educational components. We developed the T-BMIRT model: a temporal, multidimensional, IRT-based method for estimating the above parameters. The model added learning video parameters and modeled the student proficiencies over time as a random process, accounting for the student learning and forgetting process. And it was extended to multidimensional to estimate the educational components which contain multiple skills. So the model can describe the student learning trajectories in an online education system. In addition, we evaluated this model by predicting student next response to assessment, and found it is better than the IRT and temporal IRT models on each dataset we used, especially when the dataset contains learning videos interactions.