  Knowledge Tracing is the process of tracking mastery level of different
skills of students for a given learning domain. It is one of the key components
for building adaptive learning systems and has been investigated for decades.
In parallel with the success of deep neural networks in other fields, we have
seen researchers take similar approaches in the learning science community.
However, most existing deep learning based knowledge tracing models either: (1)
only use the correct/incorrect response (ignoring useful information from other
modalities) or (2) design their network architectures through domain expertise
via trial and error. In this paper, we propose a sequential model based
optimization approach that combines multimodal fusion and neural architecture
search within one framework. The commonly used neural architecture search
technique could be considered as a special case of our proposed approach when
there is only one modality involved. We further propose to use a new metric
called time-weighted Area Under the Curve (weighted AUC) to measure how a
sequence model performs with time. We evaluate our methods on two public real
datasets showing the discovered model is able to achieve superior performance.
Unlike most existing works, we conduct McNemar's test on the model predictions
and the results are statistically significant.
