Knowledge Tracing via Attention Enhanced Encoder-Decoder

Abstract

The knowledge tracing model takes students’ learning behaviours data as input to determine their current knowledge status and predict their future answers. The learning behaviours data describes three main types of learning behaviours: learning process, learning end, and learning interval. The classical knowledge tracing models only use the data of the learning end, which contains limited information and the models cannot accurately describe constraint in the same learning behaviour in the time series. Subsequent models add other types of learning behaviours data but do not integrate different types of learning behaviours, and the models cannot accurately describe collaboration in different learning behaviours. To address these issues, knowledge tracing via attention-enhanced encoder-decoder is proposed to synthesize and analyse the three types of learning behaviours mentioned above and firstly adopts the multiheaded attention mechanism to describe constraint in the same learning behaviours; secondly adopts the channel attention mechanism modelling collaboration in the three types of learning behaviours. In the experiments, various comparisons are made with related models on several real data sets, and the results show that our model achieves certain advantages in terms of performance and knowledge state representation. In terms of practical application, an intelligent learning platform based on the model has been implemented, which predicts the future answer of students in the teaching process of two offline courses: computer and English and has achieved better performance than other knowledge tracing models.