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pro vyhledávání: '"Stratton, Damji"'
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, 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
Publikováno v:
The Proceedings of the 19th Annual National Symposium on Student Retention, 2023
This study utilizes data from the Baccalaureate and Beyond Longitudinal Study to explore factors associated with the likelihood of students' employment in STEM fields one year after graduation. We examined various factors related to students' individ
Externí odkaz:
http://arxiv.org/abs/2311.14142
Autor:
Stratton, Damji Heo *
Publikováno v:
In International Journal of Educational Research Open 2021 2
