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Lookup NU author(s): Dr Lei ShiORCiD
This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by Institute of Intelligent Systems, 2024.
For re-use rights please refer to the publisher's terms and conditions.
The field of Knowledge Tracing (KT) aims to understand how students learn and master knowledge over time by analyzing their historical behaviour data. To achieve this goal, many researchers have proposed KT models that use data from Intelligent Tutoring Systems (ITS) to predict students’ subsequent actions. However, with the devel- opment of ITS, large-scale datasets containing long-sequence data began to emerge. Recent deep learning based KT models face obstacles such as low efficiency, low accuracy, and low interpretability when dealing with large-scale datasets containing long-sequence data. To address these is- sues and promote the sustainable development of ITS, we propose a LSTM BERT-based Knowledge Tracing model for long sequence data processing, namely LBKT, which uses a BERT-based architecture with a Rasch model-based embeddings block to deal with different difficulty levels information and an LSTM block to process the sequential char- acteristic in students’ actions. LBKT achieves the best performance on most benchmark datasets on the metrics of ACC and AUC.
Author(s): Li Z, Yang J, Wang J, Shi L, Feng J, Stein S
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 20th International Conference on Intelligent Tutoring Systems (ITS 2024)
Year of Conference: 2024
Online publication date: 13/06/2024
Acceptance date: 05/04/2024
Date deposited: 11/04/2024
Publisher: Institute of Intelligent Systems
URL: https://iis-international.org/its2024-generative-intelligence-and-its/
ePrints DOI: 10.57711/baer-g845
