  We address the problem of predicting the correctness of the student's
response on the next exam question based on their previous interactions in the
course of their learning and evaluation process. We model the student
performance as a dynamic problem and compare the two major classes of dynamic
neural architectures for its solution, namely the finite-memory Time Delay
Neural Networks (TDNN) and the potentially infinite-memory Recurrent Neural
Networks (RNN). Since the next response is a function of the knowledge state of
the student and this, in turn, is a function of their previous responses and
the skills associated with the previous questions, we propose a two-part
network architecture. The first part employs a dynamic neural network (either
TDNN or RNN) to trace the student knowledge state. The second part applies on
top of the dynamic part and it is a multi-layer feed-forward network which
completes the classification task of predicting the student response based on
our estimate of the student knowledge state. Both input skills and previous
responses are encoded using different embeddings. Regarding the skill
embeddings we tried two different initialization schemes using (a) random
vectors and (b) pretrained vectors matching the textual descriptions of the
skills. Our experiments show that the performance of the RNN approach is better
compared to the TDNN approach in all datasets that we have used. Also, we show
that our RNN architecture outperforms the state-of-the-art models in four out
of five datasets. It is worth noting that the TDNN approach also outperforms
the state of the art models in four out of five datasets, although it is
slightly worse than our proposed RNN approach. Finally, contrary to our
expectations, we find that the initialization of skill embeddings using
pretrained vectors offers practically no advantage over random initialization.
