Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Park, Jaekwon"'
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