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Titlebook: Artificial Intelligence in Education; 17th International C Cristina Conati,Neil Heffernan,M. Felisa Verdejo Conference proceedings 2015 Spr

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期刊全称Artificial Intelligence in Education
期刊简称17th International C
影响因子2023Cristina Conati,Neil Heffernan,M. Felisa Verdejo
视频video
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Artificial Intelligence in Education; 17th International C Cristina Conati,Neil Heffernan,M. Felisa Verdejo Conference proceedings 2015 Spr
影响因子.This book constitutes the refereed proceedings of the 17th International Conference on Artificial Intelligence in Education, AIED 2015, held in Madrid, Spain, in June 2015..The 50 revised full papers presented together with 3 keynotes, 79 poster presentations, 13 doctoral consortium papers, 16 workshop abstracts, and 8 interactive event papers were carefully reviewed and selected from numerous submissions. The conference provides opportunities for the cross-fertilization of approaches, techniques and ideas from the many fields that comprise AIED, including computer science, cognitive and learning sciences, education, game design, psychology, sociology, linguistics, as well as many domain-specific areas..
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Family Patterns — Convergence or Divergencelogical-based affect detection. Videos of students playing an educational physics game were collected in a noisy computer-enabled classroom environment where students conversed with each other, moved around, and gestured. Trained observers provided real-time annotations of learning-centered affectiv
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The European Family — Made in the OECD? gaming experience. This paper examines game-based learning for a population of middle school learners in the US, where female students tend to have less gaming experience than male students. In a pilot study with an early version of E., a game-based learning environment for middle school computer s
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https://doi.org/10.1057/9780230583146specially the case for complex simulations such as the DC Circuit Construction Kit used in this work. To deal with this complexity, we evaluate alternative representations that capture different levels of detail in student interactions. Our results show that these representations can be effectively
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Jeremy Staff,Nayan Ramirez,Mike Vuoloey learn to coherently link information, while organically constructing a solid knowledge base, is crucial to students’ development, but requires regular assessment and progress tracking. To this end, our aim is to provide an automated solution for analyzing and predicting students’ comprehension le
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