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Shimmei, Machi; Matsuda, Noboru – International Educational Data Mining Society, 2023
We propose an innovative, effective, and data-agnostic method to train a deep-neural network model with an extremely small training dataset, called VELR (Voting-based Ensemble Learning with Rejection). In educational research and practice, providing valid labels for a sufficient amount of data to be used for supervised learning can be very costly…
Descriptors: Artificial Intelligence, Training, Natural Language Processing, Educational Research
Cunningham, Jim – International Educational Data Mining Society, 2015
In this paper, I describe preliminary work on a new research project in learning analytics at Arizona State University. In conjunction with an innovative remedial mathematics course using Khan Academy and student coaches, this study seeks to measure the effectiveness of visualized data in assisting student coaches as they help remedial math…
Descriptors: Research Projects, Remedial Mathematics, Coaching (Performance), Visual Aids
Géryk, Jan – International Educational Data Mining Society, 2015
The efficacy of animated data visualizations in comparison with static data visualizations is still inconclusive. Some researches resulted that the failure to find out the benefits of animations may relate to the way how they are constructed and perceived. In this paper, we present visual analytics (VA) tool which makes use of enhanced animated…
Descriptors: Animation, Visualization, Visual Stimuli, Program Effectiveness
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Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis