Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Lee, Unggi"'
Autor:
Kim, Dohee, Lee, Unggi, Lee, Sookbun, Bae, Jiyeong, Ahn, Taekyung, Park, Jaekwon, Lee, Gunho, Kim, Hyeoncheol
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 eleme
Externí odkaz:
http://arxiv.org/abs/2409.10244
Autor:
Park, Jaekwon, Bae, Jiyoung, Lee, Unggi, Ahn, Taekyung, Lee, Sookbun, Kim, Dohee, Choi, Aram, Jeong, Yeil, Moon, Jewoong, Kim, Hyeoncheol
This study investigates the design, development, and evaluation of a Large Language Model (LLM)-based chatbot for teaching English conversations in an English as a Foreign Language (EFL) context. Employing the Design and Development Research (DDR), w
Externí odkaz:
http://arxiv.org/abs/2409.04987
Autor:
Lee, Unggi, Bae, Jiyeong, Jung, Yeonji, Kang, Minji, Byun, Gyuri, Lee, Yeonseo, Kim, Dohee, Lee, Sookbun, Park, Jaekwon, Ahn, Taekyung, Lee, Gunho, Kim, Hyeoncheol
Knowledge Tracing (KT) is a critical component in online learning, but traditional approaches face limitations in interpretability and cross-domain adaptability. This paper introduces Language Model-based Code Knowledge Tracing (CodeLKT), an innovati
Externí odkaz:
http://arxiv.org/abs/2409.00323
Autor:
Lee, Unggi, Bae, Jiyeong, Kim, Dohee, Lee, Sookbun, Park, Jaekwon, Ahn, Taekyung, Lee, Gunho, Stratton, Damji, Kim, Hyeoncheol
Knowledge Tracing (KT) is a critical task in online learning for modeling student knowledge over time. Despite the success of deep learning-based KT models, which rely on sequences of numbers as data, most existing approaches fail to leverage the ric
Externí odkaz:
http://arxiv.org/abs/2406.02893
Autor:
Lee, Unggi, Jeong, Yeil, Koh, Junbo, Byun, Gyuri, Lee, Yunseo, Lee, Hyunwoong, Eun, Seunmin, Moon, Jewoong, Lim, Cheolil, Kim, Hyeoncheol
This preliminary study explores the integration of GPT-4 Vision (GPT-4V) technology into teacher analytics, focusing on its applicability in observational assessment to enhance reflective teaching practice. This research is grounded in developing a V
Externí odkaz:
http://arxiv.org/abs/2405.18623
Autor:
Lee, Unggi, Jeon, Minji, Lee, Yunseo, Byun, Gyuri, Son, Yoorim, Shin, Jaeyoon, Ko, Hongkyu, Kim, Hyeoncheol
Despite the development of various AI systems to support learning in various domains, AI assistance for art appreciation education has not been extensively explored. Art appreciation, often perceived as an unfamiliar and challenging endeavor for most
Externí odkaz:
http://arxiv.org/abs/2402.06264
Autor:
Lee, Unggi, Yoon, Sungjun, Yun, Joon Seo, Park, Kyoungsoo, Jung, YoungHoon, Stratton, Damji, Kim, Hyeoncheol
This paper presents novel techniques for enhancing the performance of knowledge tracing (KT) models by focusing on the crucial factor of question and concept difficulty level. Despite the acknowledged significance of difficulty, previous KT research
Externí odkaz:
http://arxiv.org/abs/2312.11890
Autor:
Lee, Unggi, Jeong, Yeil, Koh, Junbo, Byun, Gyuri, Lee, Yunseo, Hwang, Youngsun, Kim, Hyeoncheol, Lim, Cheolil
Publikováno v:
Educational Technology & Society, 2024 Apr 01. 27(2), 321-346.
Externí odkaz:
https://www.jstor.org/stable/48766178
Knowledge tracing (KT) is a field of study that predicts the future performance of students based on prior performance datasets collected from educational applications such as intelligent tutoring systems, learning management systems, and online cour
Externí odkaz:
http://arxiv.org/abs/2208.12615
Autor:
Lee, Unggi, Jung, Haewon, Jeon, Younghoon, Sohn, Younghoon, Hwang, Wonhee, Moon, Jewoong, Kim, Hyeoncheol
Publikováno v:
Education & Information Technologies; 2024, Vol. 29 Issue 9, p11483-11515, 33p