Exploring the Potential of Immersive Virtual Environments for Learning American Sign Language

Wang, Jindi; Ivrissimtzis, Ioannis; Li, Zhaoxing; Zhou, Yunzhan; Shi, Lei

doi:10.1007/978-3-031-42682-7_31

Cited by 2 publications

References 21 publications

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LBKT: A LSTM BERT-Based Knowledge Tracing Model for Long-Sequence Data

Li,

Yang,

Wang

et al. 2024

Lecture Notes in Computer Science

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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 development 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 issues 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 characteristic in students' actions. LBKT achieves the best performance on most benchmark datasets on the metrics of ACC and AUC.

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LBKT: A LSTM BERT-Based Knowledge Tracing Model for Long-Sequence Data

Li,

Yang,

Wang

et al. 2024

Lecture Notes in Computer Science

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Comparative Efficacy of 2D and 3D Virtual Reality Games in American Sign Language Learning

Wang,

Ivrissimtzis,

et al. 2024

2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)

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With ongoing advancements in virtual reality and gamification, researchers have started exploring their application in sign language learning, aiming at enhancing communication between hearing individuals and the deaf community. This study compares the impact of 2D and 3D games on American Sign Language (ASL) learning, using questionnaires to assess user experience. Results show that 3D games enhance engagement, attractiveness, usability, and efficiency, although user performance remains similar in both environments. The findings suggest the potential of 3D game-based approaches to improve ASL learning experiences while also identifying areas for enhancing their dependability and clarity.

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Exploring the Potential of Immersive Virtual Environments for Learning American Sign Language

Cited by 2 publications

References 21 publications

LBKT: A LSTM BERT-Based Knowledge Tracing Model for Long-Sequence Data

LBKT: A LSTM BERT-Based Knowledge Tracing Model for Long-Sequence Data

Comparative Efficacy of 2D and 3D Virtual Reality Games in American Sign Language Learning

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