Intelligent Knowledge Tracing: More Like a Real Learning Process of a Student
share
Knowledge tracing (KT) refers to a machine learning technique to assess a student's level of understanding (so-called knowledge state) of a certain concept based on the student performance on problem solving. KT accepts a series of question-answer pairs as an input and iteratively updates the knowledge state of the student, eventually returning the probability of the student solving an unseen question. From the viewpoint of neuroeducation (the field of applying neuroscience, cognitive science, and psychology to education), however, KT leaves much room for improvement in terms of explaining the complex process of human learning. In this paper, we identify three problems of KT (namely non adaptive knowledge growth, neglected latent information, and unintended negative influence) and propose a memory-network-based technique named intelligent knowledge tracing (IKT) to address them, thus approaching one step closer to understanding the complex mechanism underlying human learning. In addition, we propose a new performance metric called correct update count (CUC) that can measure the degree of unintended negative influence, thus quantifying how closely a student model resembles the human learning process. The proposed CUC metric can complement the area under the curve (AUC) metric, allowing us to evaluate competing models more effectively. According to our experiments using a widely used public benchmark, IKT significantly (over two times) outperformed the existing KT approaches in terms of CUC, while preserving the correctness behavior measured in AUC.
READ FULL TEXT