Titlebook: Artificial Intelligence in Education; 19th International C Carolyn Penstein Rosé,Roberto Martínez-Maldonado,B Conference proceedings 2018 S

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Ella Me Ayudó (She Helped Me): Supporting Hispanic and English Language Learners in a Math ITSarning companion (LC) avatars that matches their personality and will regard a LC as a substitute self-construction. Results indicate that ELL students find LCs more useful and helpful than do Caucasian students and ELL students purposely design LCs that look more like themselves than do the non-ELL students.

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Learner Behavioral Feature Refinement and Augmentation Using GANshile also generating a set of additional, complementary features that better summarize the raw data. Through experimental validation on a real-world dataset that we collected from an online course, we demonstrate that our method leads to features that are both predictive of learner quiz scores and closely related to human-crafted features.

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Characterizing Students Based on Their Participation in the Classules questions, internal test, and tournament questions. Applying K-Means, four different groups of students have been identified. The results revealed that students who were more active and participated more in activities achieved better scores on their final exam.

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0302-9743 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..978-3-319-93845-5978-3-319-93846-2Series ISSN 0302-9743 Series E-ISSN 1611-3349

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0302-9743 Education, AIED 2018, held in London, UK, in June 2018..The 45 full papers presented in this book together with 76 poster papers, 11 young researchers tracks, 14 industry papers and 10 workshop papers were carefully reviewed and selected from 192 submissions. The conference provides opportunities 

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Laura Mazzoli Smith,Jim Campbelllitate massive open online learning. We explore social network analysis and unsupervised learning approaches (such as probabilistic graphical models) on these type of Intelligent Tutoring Systems to examine the potential of the embedding representations on students learning.

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Sheryl O. Hughes,Thomas G. Powertep hints and more abstract high-level hints) on students’ behavior in a college-level logic proof tutor, Deep Thought. The results suggest that hint types can affect student behavior, including hint usage, rule applications, and time in-tutor.

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How SNAP Reduces Food Insecurity, perseverance and recovery from failure. We use rare Association Rule Mining to explore how students’ grit may be associated with students’ behavior within MathSpring, an intelligent tutoring system, as a first step.