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Title: Deep Knowledge Tracing using Temporal Convolutional Networks
The knowledge tracing (KT) task consists of predicting students’ future performance on instructional activities given their past performance. Recently, deep learning models used to solve this task yielded relative excellent prediction results relative to prior approaches. Despite this success, the majority of these models ignore relevant information that can be used to enhance the knowledge tracing performance. To overcome these limitations, we propose a generic framework that also accounts for the engagement level of students, the difficulty level of the instructional activities, and the natural language processing embeddings of the text of each concept. Furthermore, to capture the fact that students’ knowledge states evolve over time we employ a LSTM-based model. Then, we pass such sequences of knowledge states to a Temporal Convolutional Network to predict future performance. Several empirical experiments have been conducted to evaluate the effectiveness of our proposed framework for KT using Cognitive Tutor datasets. Experimental results showed the superior performance of our proposed model over many existing deep KT models. And AUC of 96.57% has been achieved on the Algebra 2006-2007 dataset.
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Proceedings of the Workshop Artificial Intelligence for Education (IJCAI 2021))
Sponsoring Org:
National Science Foundation
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