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In educational applications, Knowledge Tracing (KT), the problem ofaccurately predicting students' responses to future questions by summarizingtheir knowledge states, has been widely studied for decades as it is considereda fundamental task towards adaptive online learning. Among all the proposed KTmethods, Deep Knowledge Tracing (DKT) and its variants are by far the mosteffective ones due to the high flexibility of the neural network. However, DKToften ignores the inherent differences between students (e.g. memory skills,reasoning skills, ...), averaging the performances of all students, leading tothe lack of personalization, and therefore was considered insufficient foradaptive learning. To alleviate this problem, in this paper, we proposedLeveled Attentive KNowledge TrAcing (LANA), which firstly uses a novelstudent-related features extractor (SRFE) to distill students' unique inherentproperties from their respective interactive sequences. Secondly, the pivotmodule was utilized to dynamically reconstruct the decoder of the neuralnetwork on attention of the extracted features, successfully distinguishing theperformance between students over time. Moreover, inspired by Item ResponseTheory (IRT), the interpretable Rasch model was used to cluster students bytheir ability levels, and thereby utilizing leveled learning to assigndifferent encoders to different groups of students. With pivot modulereconstructed the decoder for individual students and leveled learningspecialized encoders for groups, personalized DKT was achieved. Extensiveexperiments conducted on two real-world large-scale datasets demonstrated thatour 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 KTmethods.

LANA: Towards Personalized Deep Knowledge Tracing Through Distinguishable Interactive Sequences