Abstract
AbstractThe knowledge tracing (KT) model is an effective means to realize the personalization of online education using artificial intelligence methods. It can accurately evaluate the learning state of students and conduct personalized instruction according to the characteristics of different students. However, the current knowledge tracing models still have problems of inaccurate prediction results and poor features utilization. The study applies XGBoost algorithm to knowledge tracing model to improve the prediction performance. In addition, the model also effectively handles the multi-skill problem in the knowledge tracing model by adding the features of problem and knowledge skills. Experimental results show that the best AUC value of the XGBoost-based knowledge tracing model can reach 0.9855 using multiple features. Furthermore, compared with previous knowledge tracing models used deep learning, the model saves more training time.
Funder
Science and Technology Project of Gansu
the Talent Innovation and Entrepreneurship Fund of Lanzhou
Key Laboratory of Media convergence Technology and Communication
Publisher
Springer Science and Business Media LLC
Subject
Computational Mathematics,General Computer Science
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