ES-KT-24: A Multimodal Knowledge Tracing Benchmark Dataset with Educational Game Playing Video and Synthetic Text Generation
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910605302038528 |
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| author | Kim, Dohee Lee, Unggi Lee, Sookbun Bae, Jiyeong Ahn, Taekyung Park, Jaekwon Lee, Gunho Kim, Hyeoncheol |
| author_facet | Kim, Dohee Lee, Unggi Lee, Sookbun Bae, Jiyeong Ahn, Taekyung Park, Jaekwon Lee, Gunho Kim, Hyeoncheol |
| contents | This paper introduces ES-KT-24, a novel multimodal Knowledge Tracing (KT) dataset for intelligent tutoring systems in educational game contexts. Although KT is crucial in adaptive learning, existing datasets often lack game-based and multimodal elements. ES-KT-24 addresses these limitations by incorporating educational game-playing videos, synthetically generated question text, and detailed game logs. The dataset covers Mathematics, English, Indonesian, and Malaysian subjects, emphasizing diversity and including non-English content. The synthetic text component, generated using a large language model, encompasses 28 distinct knowledge concepts and 182 questions, featuring 15,032 users and 7,782,928 interactions. Our benchmark experiments demonstrate the dataset's utility for KT research by comparing Deep learning-based KT models with Language Model-based Knowledge Tracing (LKT) approaches. Notably, LKT models showed slightly higher performance than traditional DKT models, highlighting the potential of language model-based approaches in this field. Furthermore, ES-KT-24 has the potential to significantly advance research in multimodal KT models and learning analytics. By integrating game-playing videos and detailed game logs, this dataset offers a unique approach to dissecting student learning patterns through advanced data analysis and machine-learning techniques. It has the potential to unearth new insights into the learning process and inspire further exploration in the field. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_10244 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ES-KT-24: A Multimodal Knowledge Tracing Benchmark Dataset with Educational Game Playing Video and Synthetic Text Generation Kim, Dohee Lee, Unggi Lee, Sookbun Bae, Jiyeong Ahn, Taekyung Park, Jaekwon Lee, Gunho Kim, Hyeoncheol Social and Information Networks This paper introduces ES-KT-24, a novel multimodal Knowledge Tracing (KT) dataset for intelligent tutoring systems in educational game contexts. Although KT is crucial in adaptive learning, existing datasets often lack game-based and multimodal elements. ES-KT-24 addresses these limitations by incorporating educational game-playing videos, synthetically generated question text, and detailed game logs. The dataset covers Mathematics, English, Indonesian, and Malaysian subjects, emphasizing diversity and including non-English content. The synthetic text component, generated using a large language model, encompasses 28 distinct knowledge concepts and 182 questions, featuring 15,032 users and 7,782,928 interactions. Our benchmark experiments demonstrate the dataset's utility for KT research by comparing Deep learning-based KT models with Language Model-based Knowledge Tracing (LKT) approaches. Notably, LKT models showed slightly higher performance than traditional DKT models, highlighting the potential of language model-based approaches in this field. Furthermore, ES-KT-24 has the potential to significantly advance research in multimodal KT models and learning analytics. By integrating game-playing videos and detailed game logs, this dataset offers a unique approach to dissecting student learning patterns through advanced data analysis and machine-learning techniques. It has the potential to unearth new insights into the learning process and inspire further exploration in the field. |
| title | ES-KT-24: A Multimodal Knowledge Tracing Benchmark Dataset with Educational Game Playing Video and Synthetic Text Generation |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2409.10244 |