Improving the Performance of Knowledge Tracing Models using Quantized Correctness Embeddings

Yoonjin Im, Jaewan Moon, Eunseong Choi, Jongwuk Lee

http://doi.org/10.5626/JOK.2023.50.4.329

Knowledge tracing is a task of monitoring the proficiency of knowledge based on learners" interaction records. Despite the flexible usage of deep neural network-based models for this task, the existing methods disregard the difficulty of each question and result in poor performance for learners who get the easy question wrong or the hard question correct. In this paper, we propose quantizing the learners’ response information based on the question difficulty so that the knowledge tracing models can learn both the response and the difficulty of the question in order to improve the performance. We design a method that can effectively discriminate between negative samples with a high percentage of correct answer rate and positive samples with a low percentage of correct answer rate. Toward this end, we use sinusoidal positional encoding (SPE) that can maximize the distance difference between embedding representations in the latent space. Experiments show that the AUC value is improved to a maximum of 17.89% in the target section compared to the existing method.