Learning Ability Community for Personalized Knowledge Tracing

Abstract

Knowledge tracing is an essential task that estimates students’ knowledge state as they engage in the online learning platform. Several models have been proposed to predict the state of students’ learning process to improve their learning efficiencies, such as Bayesian Knowledge Tracing, Deep Knowledge Tracing, and Dynamic Key-Value Memory Networks. However, these models fail to fully consider the influence of students’ current knowledge state on knowledge growth, and ignore the current knowledge state of students is affected by forgetting mechanisms. Moreover, these models are a unified model that does not consider the use of group learning behavior to guide individual learning. To tackle these problems, in this paper, we first propose a model named Knowledge Tracking based on Learning and Memory Process (LMKT) to solve the effect of students’ current knowledge state on knowledge growth and forgetting mechanisms. Then we propose the definition of learning capacity community and personalized knowledge tracking. Finally, we present a novel method called Learning Ability Community for Personalized Knowledge Tracing (LACPKT), which models students’ learning process according to group dynamics theory. Experimental results on public data sets show that the LMKT model and LACPKT model are effective. Besides, the LACPKT model can trace students’ knowledge state in a personalized way.