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Walker, Andrew; Belland, Brian R.; Kim, Nam Ju; Lefler, Mason – AERA Online Paper Repository, 2017
Baeysian Network Meta-Analysis represents a rather unique challenge in assessing the quality of included studies. Prior efforts to synthesize computer based scaffolding are in need of a closer examination of research quality. This study examines two quality metrics for meta-analysis, study design, and risk of bias (Higgins et al., 2011). Lower…
Descriptors: Scaffolding (Teaching Technique), STEM Education, Research Design, Risk
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Mao, Ye; Zhi, Rui; Khoshnevisan, Farzaneh; Price, Thomas W.; Barnes, Tiffany; Chi, Min – International Educational Data Mining Society, 2019
Early prediction of student difficulty during long-duration learning activities allows a tutoring system to intervene by providing needed support, such as a hint, or by alerting an instructor. To be effective, these predictions must come early and be highly accurate, but such predictions are difficult for open-ended programming problems. In this…
Descriptors: Difficulty Level, Learning Activities, Prediction, Programming
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Valdés Aguirre, Benjamín; Ramírez Uresti, Jorge A.; du Boulay, Benedict – International Journal of Artificial Intelligence in Education, 2016
Sharing user information between systems is an area of interest for every field involving personalization. Recommender Systems are more advanced in this aspect than Intelligent Tutoring Systems (ITSs) and Intelligent Learning Environments (ILEs). A reason for this is that the user models of Intelligent Tutoring Systems and Intelligent Learning…
Descriptors: Intelligent Tutoring Systems, Models, Open Source Technology, Computers
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Sabourin, Jennifer L.; Rowe, Jonathan P.; Mott, Bradford W.; Lester, James C. – Journal of Educational Data Mining, 2013
Over the past decade, there has been growing interest in real-time assessment of student engagement and motivation during interactions with educational software. Detecting symptoms of disengagement, such as off-task behavior, has shown considerable promise for understanding students' motivational characteristics during learning. In this paper, we…
Descriptors: Student Behavior, Classification, Learner Engagement, Data Analysis
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Hu, Xiangen, Ed.; Barnes, Tiffany, Ed.; Hershkovitz, Arnon, Ed.; Paquette, Luc, Ed. – International Educational Data Mining Society, 2017
The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…
Descriptors: Data Analysis, Data Collection, Graphs, Data Use
Vos, Hans J. – 1994
As part of a project formulating optimal rules for decision making in computer assisted instructional systems in which the computer is used as a decision support tool, an approach that simultaneously optimizes classification of students into two treatments, each followed by a mastery decision, is presented using the framework of Bayesian decision…
Descriptors: Achievement Tests, Bayesian Statistics, Classification, Computer Managed Instruction
Barnes, Tiffany, Ed.; Desmarais, Michel, Ed.; Romero, Cristobal, Ed.; Ventura, Sebastian, Ed. – International Working Group on Educational Data Mining, 2009
The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented…
Descriptors: Data Analysis, Educational Research, Conferences (Gatherings), Foreign Countries