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Yang Shi; Tiffany Barnes; Min Chi; Thomas Price – International Educational Data Mining Society, 2024
Knowledge tracing (KT) models have been a commonly used tool for tracking students' knowledge status. Recent advances in deep knowledge tracing (DKT) have demonstrated increased performance for knowledge tracing tasks in many datasets. However, interpreting students' states on single knowledge components (KCs) from DKT models could be challenging…
Descriptors: Algorithms, Artificial Intelligence, Models, Programming
Meng Cao; Philip I. Pavlik Jr.; Wei Chu; Liang Zhang – International Educational Data Mining Society, 2024
In category learning, a growing body of literature has increasingly focused on exploring the impacts of interleaving in contrast to blocking. The sequential attention hypothesis posits that interleaving draws attention to the differences between categories while blocking directs attention toward similarities within categories [4, 5]. Although a…
Descriptors: Attention, Algorithms, Artificial Intelligence, Classification
Ahmad Slim; Chaouki Abdallah; Elisha Allen; Michael Hickman; Ameer Slim – International Educational Data Mining Society, 2025
Curricular design in higher education significantly impacts student success and institutional performance. However, academic programs' complexity--shaped by pass rates, prerequisite dependencies, and course repeat policies--creates challenges for administrators. This paper presents a method for modeling curricular pathways including development of…
Descriptors: Curriculum Design, Integrated Curriculum, Data Analysis, Monte Carlo Methods
Maciej Pankiewicz; Yang Shi; Ryan S. Baker – International Educational Data Mining Society, 2025
Knowledge Tracing (KT) models predicting student performance in intelligent tutoring systems have been successfully deployed in several educational domains. However, their usage in open-ended programming problems poses multiple challenges due to the complexity of the programming code and a complex interplay between syntax and logic requirements…
Descriptors: Algorithms, Artificial Intelligence, Models, Intelligent Tutoring Systems
Verger, Mélina; Lallé, Sébastien; Bouchet, François; Luengo, Vanda – International Educational Data Mining Society, 2023
Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against some students and possible harmful long-term…
Descriptors: Prediction, Models, Student Behavior, Academic Achievement
David Joyner, Editor; Benjamin Paaßen, Editor; Carrie Demmans Epp, Editor – International Educational Data Mining Society, 2024
The Georgia Institute of Technology is proud to host the seventeenth International Conference on Educational Data Mining (EDM) in Atlanta, Georgia, July 14-July 17, 2024. EDM is the annual flagship conference of the International Educational Data Mining Society. This year's theme is "New tools, new prospects, new risks--educational data…
Descriptors: Data Analysis, Pattern Recognition, Technology Uses in Education, Artificial Intelligence
Feng, Mingyu, Ed.; Käser, Tanja, Ed.; Talukdar, Partha, Ed. – International Educational Data Mining Society, 2023
The Indian Institute of Science is proud to host the fully in-person sixteenth iteration of the International Conference on Educational Data Mining (EDM) during July 11-14, 2023. EDM is the annual flagship conference of the International Educational Data Mining Society. The theme of this year's conference is "Educational data mining for…
Descriptors: Information Retrieval, Data Analysis, Computer Assisted Testing, Cheating

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