Empowering Personalized Learning through a Conversation-based Tutoring System with Student Modeling
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arXiv
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| Format: | Preprint |
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2024
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| author | Park, Minju Kim, Sojung Lee, Seunghyun Kwon, Soonwoo Kim, Kyuseok |
| author_facet | Park, Minju Kim, Sojung Lee, Seunghyun Kwon, Soonwoo Kim, Kyuseok |
| contents | As the recent Large Language Models(LLM's) become increasingly competent in zero-shot and few-shot reasoning across various domains, educators are showing a growing interest in leveraging these LLM's in conversation-based tutoring systems. However, building a conversation-based personalized tutoring system poses considerable challenges in accurately assessing the student and strategically incorporating the assessment into teaching within the conversation. In this paper, we discuss design considerations for a personalized tutoring system that involves the following two key components: (1) a student modeling with diagnostic components, and (2) a conversation-based tutor utilizing LLM with prompt engineering that incorporates student assessment outcomes and various instructional strategies. Based on these design considerations, we created a proof-of-concept tutoring system focused on personalization and tested it with 20 participants. The results substantiate that our system's framework facilitates personalization, with particular emphasis on the elements constituting student modeling. A web demo of our system is available at http://rlearning-its.com. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_14071 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Empowering Personalized Learning through a Conversation-based Tutoring System with Student Modeling Park, Minju Kim, Sojung Lee, Seunghyun Kwon, Soonwoo Kim, Kyuseok Human-Computer Interaction As the recent Large Language Models(LLM's) become increasingly competent in zero-shot and few-shot reasoning across various domains, educators are showing a growing interest in leveraging these LLM's in conversation-based tutoring systems. However, building a conversation-based personalized tutoring system poses considerable challenges in accurately assessing the student and strategically incorporating the assessment into teaching within the conversation. In this paper, we discuss design considerations for a personalized tutoring system that involves the following two key components: (1) a student modeling with diagnostic components, and (2) a conversation-based tutor utilizing LLM with prompt engineering that incorporates student assessment outcomes and various instructional strategies. Based on these design considerations, we created a proof-of-concept tutoring system focused on personalization and tested it with 20 participants. The results substantiate that our system's framework facilitates personalization, with particular emphasis on the elements constituting student modeling. A web demo of our system is available at http://rlearning-its.com. |
| title | Empowering Personalized Learning through a Conversation-based Tutoring System with Student Modeling |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2403.14071 |