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MARC 21

Artificial intelligence in education. 21st international conference, AIED 2020, Ifrane, Morocco, July 6-10, 2020, proceedings. part II
Tag	Description
020	$a9783030522391
041	$aE
084	$aQA76.87 B58 2020
100	$aBittencourt, Ig Ibert.
245	$aArtificial intelligence in education.$b21st international conference, AIED 2020, Ifrane, Morocco, July 6-10, 2020, proceedings. part II$ht
260	$aSwitzerland$bSpringer$c2020.
300	$a436p.: col. ill. ; 23 cm.
307	$bBook
505	$aIntro -- Preface -- Organization -- International Artificial Intelligence in Education Society -- Contents -- Part II -- Contents -- Part I -- Short Papers -- Modelling Learners in Crowdsourcing Educational Systems -- 1 Introduction -- 2 Approach -- 3 Evaluation -- 4 Discussion and Conclusion -- References -- Interactive Pedagogical Agents for Learning Sequence Diagrams -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Overview and Elements of the Pedagogy Agent -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- A Socratic Tutor for Source Code Comprehension -- 1 Introduction 2 Research Questions -- 3 Method -- 3.1 Materials -- 3.2 Procedure -- 3.3 Assessment -- 4 Results -- 4.1 Quantitative Analysis -- 5 Conclusion -- References -- Scientific Modeling Using Large Scale Knowledge -- 1 Introduction -- 2 VERA: A Research Assistant for Ecological Modeling -- 3 Contextualization of Domain Knowledge -- 3.1 Illustrative Example of Inquiry-Based Modeling Using VERA -- 4 Lab Experiment -- 4.1 Results -- 5 Conclusion -- References -- Examining Students' Intrinsic Cognitive Load During Program Comprehension -- An Eye Tracking Approach -- Abstract -- 1 Introduction 2 Current Study -- 3 Method -- 4 Results -- 5 Conclusions -- References -- Sequence-to-Sequence Models for Automated Text Simplification -- Abstract -- 1 Introduction -- 2 Method -- 2.1 Corpora -- 2.2 Model Architectures -- 3 Results -- 4 Conclusions -- Acknowledgments -- References -- The Potential for the Use of Deep Neural Networks in e-Learning Student Evaluation with New Data Augmentation Method -- Abstract -- 1 Introduction -- 2 Comprehension and Data Preparation -- 3 Experimental Results -- 4 Conclusion -- References -- Investigating Transformers for Automatic Short Answer Grading 1 Introduction -- 2 Experiments -- 3 Results and Analysis -- 4 Conclusion and Future Work -- References -- Predicting Learners Need for Recommendation Using Dynamic Graph-Based Knowledge Tracing -- 1 Introduction -- 2 Proposed Approach -- 3 Experiment -- 3.1 Dataset -- 3.2 Results and Discussion -- 4 Conclusion and Future Work -- References -- BERT and Prerequisite Based Ontology for Predicting Learner's Confusion in MOOCs Discussion Forums -- 1 Introduction -- 2 Proposed Approach -- 3 Experiment Settings -- 4 Results and Evaluation -- 5 Conclusion -- References Identification of Students' Need Deficiency Through a Dialogue System -- 1 Introduction -- 2 Task-Oriented Dialogue System -- 3 Evaluation -- 4 Conclusion -- References -- The Double-Edged Sword of Automating Personalized Interventions in Makerspaces: An Exploratory Study of Potential Benefits and Drawbacks -- Abstract -- 1 Introduction -- 2 Literature Review -- 3 Overview -- 3.1 Course Overview --3.2 Research Questions -- 4 Methods -- 5 Results -- 5.1 RQ 1 -- Personalization Leads to Student Time Efficiency and Less Frustration in Learning 5.2 RQ 2 -- Personalization Leads to Unexpected Lowering of Community Spirit
650	$aArtificial intelligence.
700	$aMuldner, Kasia. Millan, Eva. Cukurova, Mutlu. Luckin, Rose.