  In educational applications, Knowledge Tracing (KT), the problem of
accurately predicting students' responses to future questions by summarizing
their knowledge states, has been widely studied for decades as it is considered
a fundamental task towards adaptive online learning. Among all the proposed KT
methods, Deep Knowledge Tracing (DKT) and its variants are by far the most
effective ones due to the high flexibility of the neural network. However, DKT
often ignores the inherent differences between students (e.g. memory skills,
reasoning skills, ...), averaging the performances of all students, leading to
the lack of personalization, and therefore was considered insufficient for
adaptive learning. To alleviate this problem, in this paper, we proposed
Leveled Attentive KNowledge TrAcing (LANA), which firstly uses a novel
student-related features extractor (SRFE) to distill students' unique inherent
properties from their respective interactive sequences. Secondly, the pivot
module was utilized to dynamically reconstruct the decoder of the neural
network on attention of the extracted features, successfully distinguishing the
performance between students over time. Moreover, inspired by Item Response
Theory (IRT), the interpretable Rasch model was used to cluster students by
their ability levels, and thereby utilizing leveled learning to assign
different encoders to different groups of students. With pivot module
reconstructed the decoder for individual students and leveled learning
specialized encoders for groups, personalized DKT was achieved. Extensive
experiments conducted on two real-world large-scale datasets demonstrated that
our proposed LANA improves the AUC score by at least 1.00% (i.e. EdNet 1.46%
and RAIEd2020 1.00%), substantially surpassing the other State-Of-The-Art KT
methods.
