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MAN: Memory-augmented Attentive Networks for Deep Learning-based Knowledge Tracing

Published: 18 August 2023 Publication History

Abstract

Knowledge Tracing (KT) is the task of modeling a learner’s knowledge state to predict future performance in e-learning systems based on past performance. Deep learning-based methods, such as recurrent neural networks, memory-augmented neural networks, and attention-based neural networks, have recently been used in KT. Such methods have demonstrated excellent performance in capturing the latent dependencies of a learner’s knowledge state on recent exercises. However, these methods have limitations when it comes to dealing with the so-called Skill Switching Phenomenon (SSP), i.e., when learners respond to exercises in an e-learning system, the latent skills in the exercises typically switch irregularly. SSP will deteriorate the performance of deep learning-based approaches for simulating the learner’s knowledge state during skill switching, particularly when the association between the switching skills and the previously learned skills is weak. To address this problem, we propose the Memory-augmented Attentive Network (MAN), which combines the advantages of memory-augmented neural networks and attention-based neural networks. Specifically, in MAN, memory-augmented neural networks are used to model learners’ longer term memory knowledge, while attention-based neural networks are used to model learners’ recent term knowledge. In addition, we design a context-aware attention mechanism that automatically weighs the tradeoff between these two types of knowledge. With extensive experiments on several e-learning datasets, we show that MAN effectively improve predictive accuracies of existing state-of-the-art DLKT methods.

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  • (2024)Customized adversarial training enhances the performance of knowledge tracing tasks2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA)10.1109/ISPA63168.2024.00108(808-815)Online publication date: 30-Oct-2024
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Published In

cover image ACM Transactions on Information Systems
ACM Transactions on Information Systems  Volume 42, Issue 1
January 2024
924 pages
EISSN:1558-2868
DOI:10.1145/3613513
Issue’s Table of Contents

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 August 2023
Accepted: 12 March 2023
Revised: 26 January 2023
Received: 30 August 2022
Published in TOIS Volume 42, Issue 1

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Author Tags

  1. E-learning
  2. knowledge tracing
  3. deep learning
  4. multi-head attention mechanism
  5. memory-augmented neural network

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  • Research-article

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  • National Key Research and Development Project of China
  • National Natural Science Foundation of China

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  • (2024)Transfer Learning-Driven Cattle Instance Segmentation Using Deep Learning ModelssAgriculture10.3390/agriculture1412228214:12(2282)Online publication date: 12-Dec-2024
  • (2024)ELAKT: Enhancing Locality for Attentive Knowledge TracingACM Transactions on Information Systems10.1145/365260142:4(1-27)Online publication date: 26-Apr-2024
  • (2024)Customized adversarial training enhances the performance of knowledge tracing tasks2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA)10.1109/ISPA63168.2024.00108(808-815)Online publication date: 30-Oct-2024
  • (2024)Deep Knowledge Tracking Model Integrating Multiple Feature Personalization Factors2024 IEEE Cyber Science and Technology Congress (CyberSciTech)10.1109/CyberSciTech64112.2024.00068(394-399)Online publication date: 5-Nov-2024
  • (2024)Knowledge ontology enhanced model for explainable knowledge tracingJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2024.10206536:5Online publication date: 24-Jul-2024

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