Titlebook: Artificial Intelligence in Education; 20th International C Seiji Isotani,Eva Millán,Rose Luckin Conference proceedings 2019 Springer Nature

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https://doi.org/10.1007/978-3-7643-9900-9ey need to collaborate more effectively. These systems have focused on a single platform. However, recent technology-supported collaborative learning platforms allow students to collaborate in different contexts: computer-supported classroom environments, network based online learning environments,

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https://doi.org/10.1007/978-3-7643-9900-9 enhance the prediction process. In this paper, we proposed a bootstrapped resampling approach for predicting the academic performance of university students using probabilistic modeling taking into consideration the bias issue of educational datasets. We include in this investigation students’ data

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Conformally Covariant Families,out . during reflective dialogues that are initiated after students solve quantitative physics problems, and . during these discussions—that is, domain contingent scaffolding and instructional contingent scaffolding, respectively. The pilot study compared an experimental and control version of Rimac

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Paneitz Operator and Paneitz Curvature, Such automatic item generators are most useful if the generated item instances are of either equivalent or predictable difficulty. In this study, we analyzed student performance on over 300 item generators from four university-level STEM classes collected over a period of two years. In most cases,

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