KT-Bi-GRU: Student Performance Prediction with a Bi-Directional Recurrent Knowledge Tracing Neural Network

Student performance is affected by their knowledge which changes dynamically over time. Therefore,
employing recurrent neural networks (RNN), which are known to be very good in dynamic time series
prediction, can be a suitable approach for student performance prediction. We propose such a neural
network architecture containing two modules: (i) a dynamic sub-network including a recurrent Bi-GRU
layer used for knowledge state estimation, (ii) a non-dynamic, feed-forward sub-network for predicting
answer correctness based on the current question and current student knowledge state. The model
modifies our previously proposed architecture and is different from all other existing models because it
estimates the student’s knowledge state considering only their previous responses. Thus the dynamic
sub-network generates more stable knowledge state vector representations since they are independent of
the current question. We studied both single-skill and multi-skill question scenarios and employed embeddings
to represent questions and responses. In the multi-skill case the initialization of the question
embedding matrix with pretrained word-embeddings is found to improve model performance. The experimental
results showed that our current KT-Bi-GRU model and the previous one have similar performance
while both surpassed the performance of previous state-of-the-art knowledge tracing models for five out
of seven datasets where in some cases, the difference is quite noticeable.