EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction

7 Jun 2019 Qi Liu Zhenya Huang Yu Yin Enhong Chen Hui Xiong Yu Su Guoping Hu

For offering proactive services to students in intelligent education, one of the fundamental tasks is predicting their performance (e.g., scores) on future exercises, where it is necessary to track each student's knowledge acquisition during her exercising activities. However, existing approaches can only exploit the exercising records of students, and the problem of extracting rich information existed in the exercise's materials (e.g., knowledge concepts, exercise content) to achieve both precise predictions of student performance and interpretable analysis of knowledge acquisition remains underexplored... (read more)

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METHOD TYPE
Interpretability
Image Models
Sigmoid Activation
Activation Functions
Tanh Activation
Activation Functions
Memory Network
Working Memory Models
LSTM
Recurrent Neural Networks