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" Artificial Intelligence in Education : "
edited by Seiji Isotani, Eva Millán, Amy Ogan, Peter Hastings, Bruce McLaren, Rose Luckin.
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BL
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| Record Number
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862492
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| Title & Author
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Artificial Intelligence in Education : : 20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019, Proceedings, Part I /\ edited by Seiji Isotani, Eva Millán, Amy Ogan, Peter Hastings, Bruce McLaren, Rose Luckin.
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| Edition Statement
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1st ed. 2019.
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| Publication Statement
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Cham :: Springer International Publishing :: Imprint :: Springer,, 2019.
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| Series Statement
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Lecture Notes in Artificial Intelligence ;; 11625
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| Page. NO
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1 online resource (XXXIV, 561 pages 123 illustrations, 99 illustrations in color.) :: online resource
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| ISBN
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3030232042
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: 3030232050
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: 9783030232047
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: 9783030232054
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3030232034
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3030232069
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9783030232030
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9783030232047
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9783030232061
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| Contents
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Intro; Preface; Organization; Abstracts of Keynotes; Learning to Learn Differently; Human Development and Augmented Intelligence; Duolingo: Free Language Education for the World; Contents -- Part I; Contents -- Part II; Towards the Identification of Propaedeutic Relations in Textbooks; 1 Introduction; 2 Background and Related Work; 3 Proposed Method; 4 Experimental Evaluation; 4.1 Results and Discussion; 5 Conclusion and Limits; References; Investigating Help-Giving Behavior in a Cross-Platform Learning Environment; 1 Introduction; 2 System Description; 3 Method; 3.1 Participants; 3.2 Procedure
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3 Experiments3.1 Experimental Setup; 3.2 Results and Analysis; 4 Conclusion and Future Work; References; Effect of Discrete and Continuous Parameter Variation on Difficulty in Automatic Item Generation; 1 Introduction; 2 Experimental Data; 2.1 PrairieLearn and the Computer-Based Testing Facility; 2.2 Detailed Data Specification; 3 Discrete Parameters; 3.1 Analysis Method for Discrete Parameters; 3.2 Results for Discrete Parameters; 3.3 Manual Analysis of Outliers for Discrete Parameters; 4 Continuous Parameters; 4.1 Analysis Method for Continuous Parameters
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3 Testing the System3.1 Conditions; 3.2 Participants; 3.3 Materials; 3.4 Protocol; 3.5 Results; 4 Discussion and Future Work; Acknowledgments; References; Item Ordering Biases in Educational Data; 1 Introduction; 2 Background; 2.1 Used Data; 2.2 Simulations; 3 Item Ordering Bias; 4 Understanding Data; 5 Algorithms for Dynamic Item Ordering; 5.1 Algorithms; 5.2 Experiments; 6 Discussion; References; A Comparative Study on Question-Worthy Sentence Selection Strategies for Educational Question Generation; 1 Introduction; 2 Methodology; 2.1 Sentence Selection Strategies; 2.2 Evaluation Method
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3.3 Measures4 Results; 5 Discussion and Conclusion; References; Predicting Academic Performance: A Bootstrapping Approach for Learning Dynamic Bayesian Networks; 1 Introduction; 2 Related Work; 3 Method; 3.1 Datasets; 3.2 Resampling with Bootstrapping; 3.3 Bayesian Structure Learning; 3.4 Bayesian Parameter Learning; 4 Experimental Results; 5 Confidence Interval Results; 6 Conclusion and Future Work; References; The Impact of Student Model Updates on Contingent Scaffolding in a Natural-Language Tutoring System; Abstract; 1 Introduction; 2 Rimac: An Adaptive Natural-Language Tutoring System
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4.2 Results for Continuous Parameters4.3 Manual Analysis of Outliers for Continuous Parameters; 5 Limitations; 6 Conclusion; References; Automated Summarization Evaluation (ASE) Using Natural Language Processing Tools; Abstract; 1 Introduction; 1.1 Summarization; 1.2 Automated Summarization Evaluation (ASE); 1.3 Current Study; 2 Method; 2.1 Data; 2.2 Summary Rating; 2.3 Linguistic Features; 2.4 Statistical Analysis; 3 Results; 4 Discussion; 5 Conclusion; Acknowledgments; References; The Importance of Automated Real-Time Performance Feedback in Virtual Reality Temporal Bone Surgery Training
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| Abstract
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This two-volume set LNCS 11625 and 11626 constitutes the refereed proceedings of the 20th International Conference on Artificial Intelligence in Education, AIED 2019, held in Chicago, IL, USA, in June 2019. The 45 full papers presented together with 41 short, 10 doctoral consortium, 6 industry, and 10 workshop papers were carefully reviewed and selected from 177 submissions. AIED 2019 solicits empirical and theoretical papers particularly in the following lines of research and application: Intelligent and interactive technologies in an educational context; Modelling and representation; Models of teaching and learning; Learning contexts and informal learning; Evaluation; Innovative applications; Intelligent techniques to support disadvantaged schools and students, inequity and inequality in education.
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| Subject
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Artificial intelligence.
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Computer science.
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Data mining.
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Education.
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Social sciences-- Data processing.
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Software engineering.
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Artificial intelligence-- Educational applications.
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| Dewey Classification
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006.3
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| LC Classification
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LB1028.43.A78 2019eb
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| Added Entry
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Hastings, Peter
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Isotani, Seiji.
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Luckin, Rose.
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McLaren, Bruce
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Millán, Eva.
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Ogan, Amy.
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