In the realm of intelligent education, knowledge tracking is a critical study topic. Deep learning-based knowledge tracking models have better predictive performance compared to traditional knowledge tracking models, but the models are less interpretable and also often ignore the intrinsic differences among students (e.g., learning capability, guessing capability, etc.), resulting in a lack of personalization of predictive results. To further reflect the personalized differences among students and enhance the interpretability of the model at the same time, a Deep Knowledge Tracking model integrating Learning Capability and Item Response Theory (DKT-LCIRT) is proposed. The model dynamically calculates students’ learning capability by each time interval and allocates each student to groups with similar learning capabilities to increase the predictive performance of the model. Furthermore, the model introduces item response theory to enhance the interpretability of the model. Substantial experiments on four real datasets were carried out, and the experimental results showed that the DKT-LCIRT model improved the AUC by 3% and the ACC by 2% compared to other models. The results confirmed that the DKT-LCIRT model outperformed other classical models in terms of predictive performance, fully reflecting students’ individualization and adding a more meaningful interpretation to the model.
One of the most critical functions of modern intelligent teaching technology is cognitive diagnostics. Traditional cognitive diagnostic models (CDMs) usually use designed functions to deal with the linear interaction between students and exercises, but it is difficult to adequately deal with the complex relationship of non-linear interaction between students and exercises; moreover, existing cognitive diagnostic models often lack the integrated consideration of multiple features of exercises. To address these issues, this paper proposes a neural network cognitive diagnosis model (NeuralNCD) that incorporates multiple features. The model obtains more accurate diagnostic results by using neural networks to handle the nonlinear interaction between students and exercises. First, the student vector and the exercise vector are obtained through the Q-matrix; second, the multi-dimensional features of the exercises (e.g., difficulty, discrimination, guess and slip) are obtained using the neural network; finally, item response theory and a neural network are employed to characterize the interaction between the student and the exercise in order to determine the student’s cognitive state. At the same time, monotonicity assumptions and data preprocessing mechanisms are introduced into the neural network to improve the accuracy of the diagnostic results. Extensive experimental results on real world datasets present the effectiveness of NeuralNCD with regard to both accuracy and interpretability for diagnosing students’ cognitive states. The prediction accuracy (ACC), root mean square error (RMSE), and area under the curve (AUC) were 0.734, 0.425, and 0.776, respectively, which were about 2–10% higher than the related works in these evaluation metrics.
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