An Approach for Combining Multimodal Fusion and Neural Architecture Search Applied to Knowledge Tracing
This addresses the challenge of building more accurate adaptive learning systems for students by integrating multiple data modalities and automating architecture design, representing an incremental improvement over existing deep learning methods.
The paper tackles the problem of knowledge tracing by combining multimodal fusion and neural architecture search into a single framework, achieving superior performance on two public datasets with statistically significant results.
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.