Abstract
Estimating students’ knowledge is a fundamental and important task for student modeling in intelligent tutoring systems. Since the concept of knowledge tracing was proposed, there have been many studies focusing on estimating students’ mastery of specific knowledge components, yet few studies paid attention to the analysis and prediction on a student’s overall learning trend in the learning process. Therefore, we propose a method to analyze a student’s learning trend in the learning process and predict students’ performance in future learning. Firstly, we estimate the probability that the student has mastered the knowledge components with the model of Bayesian Knowledge Tracing, and then model students’ learning curves in the overall learning process and predict students’ future performance with Regression Analysis. Experimental results show that this method can be used to fit students’ learning trends well and can provide prediction with reference value for students’ performances in the future learning.
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This work is supported by the National Natural Science Foundation of China (Project Nos. 61370137), the International Corporation Project of Beijing Institute of Technology (No. 3070012221404) and the 111 Project of Beijing Institute of Technology.
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Cai, Y., Niu, Z., Wang, Y., Niu, K. (2015). Learning Trend Analysis and Prediction Based on Knowledge Tracing and Regression Analysis. In: Liu, A., Ishikawa, Y., Qian, T., Nutanong, S., Cheema, M. (eds) Database Systems for Advanced Applications. DASFAA 2015. Lecture Notes in Computer Science(), vol 9052. Springer, Cham. https://doi.org/10.1007/978-3-319-22324-7_3
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DOI: https://doi.org/10.1007/978-3-319-22324-7_3
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