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.
Deep Hierarchical Knowledge Tracing
Knowledge tracing is an essential and challenging task in intelligent tutoring systems, whose goal is to estimate students’ knowledge state based on their responses to questions. Although many models for knowledge tracing task are developed, most of them depend on either concepts or items as input and ignore the hierarchical structure of items, which provides valuable information for the prediction of student learning results. In this paper, we propose a novel deep hierarchical knowledge tracing (DHKT) model exploiting the hierarchical structure of items. In the proposed DHKT model, the hierarchical relations between concepts and items are modeled by the hinge loss on the inner product between the learned concept embeddings and item embeddings. Then the learned embeddings are fed into a neural network to model the learning process of students, which is used to make predictions. The prediction loss and the hinge loss are minimized simultaneously during training process.
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- Proceedings of the 12th International Conference on Educational Data Mining
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- National Science Foundation
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