Knowledge Tracing Model with Learning and Forgetting Behavior

The Knowledge Tracing (KT) task aims to trace the changes of students' knowledge state in real time according to students' historical learning behavior, and predict students' future learning performance. The modern KT models have two problems. One is that these KT models can't reflect students' actual knowledge level. Most KT models only judge students' knowledge state based on their performance in exercises, and poor performance will lead to a decline in knowledge state. However, the essence of students' learning process is the process of acquiring knowledge, which is also a manifestation of learning behavior. Even if they answer the exercises incorrectly, they will still gain knowledge. The other problem is that many KT models don't pay enough attention to the impact of students' forgetting behavior on the knowledge state in the learning process. In fact, learning and forgetting behavior run through students' learning process, and their effects on students' knowledge state shouldn't be ignored. In this paper, based on educational psychology theory, we propose a knowledge tracing model with learning and forgetting behavior (LFBKT). LFBKT comprehensively considers the factors that affect learning and forgetting behavior to build the knowledge acquisition layer, knowledge absorption layer and knowledge forgetting layer. In addition, LFBKT introduces difficulty information to enrich the information of the exercise itself, while taking into account other answering performances besides the answer. Experimental results on two public datasets show that LFBKT can better trace students' knowledge state and outperforms existing models in terms of ACC and AUC.