Design and implementation of question recommendation system based on deep knowledge tracing

Online education has developed rapidly since 2020, and the completion of after-school exercises is a part of online education, which plays an important role in improving students’ knowledge. However, the existing question recommendation systems mainly have two problems: (1) The question recommendation is completely based on the parametric theoretical model. The parametric theoretical model parameterizes the questions and the students’ ability to answer the questions, so it cannot provide a personalized question recommendation strategy. (2) The question recommendation strategy depends on the teacher’s formulation, and the efficiency is not high. In order to solve the above two problems, this paper is based on deep knowledge tracing and uses a strategy for recommending questions for students’ weak knowledge points. This method first uses the deep knowledge tracing model to model students’ personal knowledge level, and then finds out students’ weak knowledge points. Recommend questions for students’ weak knowledge points. Under the real experimental data set, this method can recommend personalized questions for students without the participation of experts.