Interpretable knowledge tracing based on the feature relevance of interaction sequence
In order to improve the interpretability of the knowledge tracing(KT)model,the Shapley Value and ISP algorithms suit-able for the post-hoc interpretability of KT and the metrics harmony for the interpretability evaluation were proposed.Taking DKT,a classic deep learning model in the field of KT,as an example,the correlation score between history interaction and prediction results were calculated,explaining the prediction results of DKT.The Shapley Value algorithm calculated the contribution of each interac-tion to the prediction result,and regarded the contribution as a correlation score;the ISP algorithm constructed pseudo-labels based on the original sequence and the inferring ability of the model,realized the perturbation of the original sequence,and calculated the correlation score;based on correlation scores calculated by different explanation methods,harmony was proposed to evaluate the in-terpretability of each method.At the experimental aspect,the experimental results on five public datasets showed that,compared with the optimal baseline method,the method proposed in this study achieved a significant improvement in interpretability;at the specific application aspect,interpretability was used to mine the partial order relationship between skills,so as to explore a more rea-sonable learning sequence for students.
万方数据
维普
