KT-Bi-GRU: Student Performance Prediction with a Recurrent Knowledge
Tracing Neural Network
Abstract
The work in this paper is an extended research of our previous work: M.
Delianidi, K. Diamantaras, G. Chrysogonidis, and V.
Nikiforidis,“Student performance prediction using dynamic neural
models,” in Fourteenth International Conference on Educational Data
Mining (EDM 2021), 2021, pp. 46–54. In both works we study the task of
predicting a student’s performance in a series of questions based, at
each step, on the answers he/she has given on the previous questions. We
propose a recurrent neural network approach where the dynamic part of
the model is a Bidirectional GRU layer. In this work, we differentiate
the model architecture from the earlier paper by imposing that the
dynamic part is based exclusively on the history of previous
question/answers, not including the current question. Then, the
subsequent classification part is fed by the output of the dynamic part
and the current question. In this way, the first part estimates the
student’s knowledge state and represents it with a dynamically generated
vector considering only the student’s previous questions and responses.
We call this part the “Knowledge State Representation subnet’‘. Using
this representation, the following “Tracing subnet’‘ which is a static
multi-layer classifier can predict the correctness of the answer to any
following question. Therefore, this architecture is suitable not only
for described prediction task but also for recommendation tasks which
were not possible with the previous architecture. In addition, in this
work, we also study the effectiveness of our models against each other,
as well as against the previous state-of-the-art models on seven
datasets including a new multi-skills dataset derived from the NeurIPS
2020 education challenge. Both our models compare favorably against the
state-of-the-art methods in almost all datasets, while the newly
proposed architecture achieves very similar results compared to our
earlier model in most cases except for two cases, including the
multi-skill “NeurIPS-2020-small” dataset, where it achieves
considerably better results.