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"CAKT: Coupling contrastive learning with attention networks for interp" by Shuaishuai Zu, Li Li et al.

In intelligent systems, knowledge tracing (KT) plays a vital role in providing personalized education. Existing KT methods often rely on students' learning interactions to trace their knowledge states by predicting future performance on the given questions. While deep learning-based KT models have achieved improved predictive performance compared with traditional KT models, they often lack interpretability into the captured knowledge states. Furthermore, previous works generally neglect the multiple semantic information contained in knowledge states and sparse learning interactions. In this paper, we propose a novel model named CAKT that couples contrastive learning with attention networks for interpretable knowledge tracing. Specifically, we use three attention-based encoders to model three dynamic factors of the Item Response Theory (IRT) model, based on designed learning sequences. Then, we identify two key properties related to the knowledge states and learning interactions: c ....

Item Response Theory , Attention Networks , Contrastive Learning , Knowledge Tracing ,

Riiid Expands its NLP Capabilities, Presenting Innovative AI Models at the World's Leading NLP Conferences

/PRNewswire/ Riiid, a leading AI Education solutions company and a member of Born2global Centre, today announced two of its new AI research studies that. ....

Yunyoung Noh , Yoonseok Yang , Jay Shin , Global Centre , Ai Research Scientist At Riiid , Association For The Advancement Of Artificial Intelligence , Vision Fund , Ministry Of Science , Estate Tracking , Knowledge Tracing , Neural Information Processing Systems , Artificial Intelligence , Born2global Centre ,

Yohan Lee, Head of Strategy at Riiid Labs - Interview Series

Yohan Lee, Head of Strategy at Riiid Labs - Interview Series
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