  Knowledge Tracing (KT) aims to trace changes in students' knowledge states
throughout their entire learning process by analyzing their historical learning
data and predicting their future learning performance. Existing forgetting
curve theory based knowledge tracing models only consider the general
forgetting caused by time intervals, ignoring the individualization of students
and the causal relationship of the forgetting process. To address these
problems, we propose a Concept-driven Personalized Forgetting knowledge tracing
model (CPF) which integrates hierarchical relationships between knowledge
concepts and incorporates students' personalized cognitive abilities. First, we
integrate the students' personalized capabilities into both the learning and
forgetting processes to explicitly distinguish students' individual learning
gains and forgetting rates according to their cognitive abilities. Second, we
take into account the hierarchical relationships between knowledge points and
design a precursor-successor knowledge concept matrix to simulate the causal
relationship in the forgetting process, while also integrating the potential
impact of forgetting prior knowledge points on subsequent ones. The proposed
personalized forgetting mechanism can not only be applied to the learning of
specifc knowledge concepts but also the life-long learning process. Extensive
experimental results on three public datasets show that our CPF outperforms
current forgetting curve theory based methods in predicting student
performance, demonstrating CPF can better simulate changes in students'
knowledge status through the personalized forgetting mechanism.
