Deep knowledge tracing with learning curves

Abstract

Knowledge tracing (KT) models students' mastery level of knowledge concepts based on their responses to the questions in the past and predicts the probability that they correctly answer subsequent questions in the future. Recent KT models are mostly developed with deep neural networks and have demonstrated superior performance over traditional approaches. However, they ignore the explicit modeling of the learning curve theory, which generally says that more practices on the same knowledge concept enhance one's mastery level of the concept. Based on this theory, we propose a Convolution-Augmented Knowledge Tracing (CAKT) model and a Capsule-Enhanced CAKT (CECAKT) model to enable learning curve modeling. In particular, when predicting a student's response to the next question associated with a specific knowledge concept, CAKT uses a module built with three-dimensional convolutional neural networks to learn the student's recent experience on that concept, and CECAKT improves CAKT by replacing the global average pooling layer with capsule networks to prevent information loss. Moreover, the two models employ LSTM networks to learn the overall knowledge state, which is fused with the feature learned by the convolutional/capsule module. As such, the two models can learn the student's overall knowledge state as well as the knowledge state of the concept in the next question. Experimental results on four real-life datasets show that CAKT and CECAKT both achieve better performance compared to existing deep KT models.

Psychological factors enhanced heterogeneous learning interactive graph knowledge tracing for understanding the learning process

“…Within the dynamic realm of online education, the imperative lies in optimizing learning modes to align with the evolving needs of students. For instance, subject-specific education modes can be enhanced by tailoring them to accommodate the Ebbinghaus forgetting curve, thus facilitating the development of distinct learning modes tailored to different subjects (Su et al, 2023). Moreover, recognizing the inherent diversity in students' comprehension levels for various types of knowledge, coupled with disparate learning methods and varying degrees of practice, underscores the need for online education platforms to prioritize personalized student development (Wang et al, 2023b).…”