Artificial Intelligence in Education: 17th International by Cristina Conati, Neil Heffernan, Antonija Mitrovic, M.

By Cristina Conati, Neil Heffernan, Antonija Mitrovic, M. Felisa Verdejo

This publication constitutes the refereed complaints of the seventeenth overseas convention on synthetic Intelligence in schooling, AIED 2015, held in Madrid, Spain, in June 2015.

The 50 revised complete papers awarded including three keynotes, seventy nine poster shows, thirteen doctoral consortium papers, sixteen workshop abstracts, and eight interactive occasion papers have been rigorously reviewed and chosen from quite a few submissions. The convention presents possibilities for the cross-fertilization of ways, innovations and ideas from the numerous fields that include AIED, together with computing device technology, cognitive and studying sciences, schooling, online game layout, psychology, sociology, linguistics, in addition to many domain-specific areas.

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Additional info for Artificial Intelligence in Education: 17th International Conference, AIED 2015, Madrid, Spain, June 22-26, 2015. Proceedings

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Kim Kelly and Neil Heffernan Building Compiler-Student Friendship. . . . . . . . . . . . . . Zhongxiu Liu and Tiffany Barnes Toward Combining Individual and Collaborative Learning Within an Intelligent Tutoring System. . . . . . . . . . . . . . Jennifer K. Olsen, Vincent Aleven, and Nikol Rummel Motivating Learning in the Age of the Adaptive Tutor . . . . . . . . Korinn Ostrow 840 844 848 852 Creating Data-Driven Feedback for Novices in Goal-Driven Programming Projects .

We compared this rule-driven version of the tutor with a non-rule-driven control in high school physics classes. Although students learned from both versions of the system, the experimental group outperformed the control group. A particularly interesting finding is that the experimental version was especially beneficial for female students. g. [5], [8], [16]). , whether an exchange addresses a problem-solving goal or its sub-goals) predict how much students learn from tutoring. Instead, they point to what content is addressed and how it is addressed in a particular context ([6], [14]) as key features of tutorial dialogue.

Whitney O. Baer, Zhiqiang Cai, Qinyu Cheng, Qiong Yu, D. Patrick Hays, and Arthur C. Graesser The Collaborative Logical Framework: Approach and Improvements Based on Instructors’ and Learners’ Needs . . . . . . . . . . . . Mario Chacón-Rivas, Olga C. Santos, and Jesus G. Boticario ReaderBench: The Learning Companion . . . . . . . . . . . . . Mihai Dascalu, Larise L. Stavarache, Philippe Dessus, Stefan Trausan-Matu, Danielle S. McNamara, and Maryse Bianco Talk, Tutor, Explore, Learn: Intelligent Tutoring and Exploration for Robust Learning .

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