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Online Knowledge Level Tracking with Data-Driven Student Models and Collaborative Filtering
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DOI：10.1109/TKDE.2019.2912367.png)
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摘要
En 中文
Intelligent Tutoring Systems are promising tools for delivering optimal and personalized learning experiences to students. A key component for their personalization is the student model, which infers the knowledge level of the students to balance the difficulty of the exercises. While important advances have been achieved, several challenges remain. In particular, the models should be able to track in real-time the evolution of the students' knowledge levels. These evolutions are likely to follow different profiles for each student, while measuring the exact knowledge level remains difficult given the limited and noisy information provided by the interactions. This paper introduces a novel model that addresses these challenges with three contributions: 1) the model relies on Gaussian Processes to track online the evolution of the student's knowledge level over time, 2) it uses collaborative filtering to rapidly provide long-term predictions by leveraging the information from previous users, and 3) it automatically generates abstract representations of knowledge components via automatic relevance determination of covariance matrices. The model is evaluated on three datasets, including real users. The results demonstrate that the model converges to accurate predictions in average four times faster than the compared methods.
Keyword:
Hidden Markov models
Predictive models
Data models
Task analysis
Adaptation models
Knowledge engineering
Production facilities
Student model
knowledge level estimation
machine learning
Gaussian processes
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10.4
论文数:
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被引数:
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