| |
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNISA996538665503316 |
|
|
Autore |
Wang Ning |
|
|
Titolo |
Artificial Intelligence in Education [[electronic resource] ] : 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings / / edited by Ning Wang, Genaro Rebolledo-Mendez, Noboru Matsuda, Olga C. Santos, Vania Dimitrova |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
|
|
|
|
|
|
|
ISBN |
|
|
|
|
|
|
Edizione |
[1st ed. 2023.] |
|
|
|
|
|
Descrizione fisica |
|
1 online resource (863 pages) |
|
|
|
|
|
|
Collana |
|
Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 13916 |
|
|
|
|
|
|
Altri autori (Persone) |
|
Rebolledo-MendezGenaro |
MatsudaNoboru |
SantosOlga C |
DimitrovaVania |
|
|
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Artificial intelligence |
Database management |
Data mining |
Application software |
User interfaces (Computer systems) |
Human-computer interaction |
Education—Data processing |
Artificial Intelligence |
Database Management |
Data Mining and Knowledge Discovery |
Computer and Information Systems Applications |
User Interfaces and Human Computer Interaction |
Computers and Education |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Nota di contenuto |
|
Intro -- Preface -- Organization -- International Artificial Intelligence in Education Society -- Contents -- Full Papers -- Machine-Generated Questions Attract Instructors When Acquainted with Learning Objectives -- 1 Introduction -- 2 Related Work -- 3 Overview of Quadl |
|
|
|
|
|
|
|
|
|
-- 4 Evaluation Study -- 4.1 Model Implementation -- 4.2 Survey Study -- 5 Results -- 5.1 Instructor Survey -- 5.2 Accuracy of the Answer Prediction Model -- 5.3 Qualitative Analysis of Questions Generated by Quadl -- 6 Discussion -- 7 Conclusion -- References -- SmartPhone: Exploring Keyword Mnemonic with Auto-generated Verbal and Visual Cues -- 1 Introduction -- 2 Methodology -- 2.1 Pipeline for Auto-generating Verbal and Visual Cues -- 3 Experimental Evaluation -- 3.1 Experimental Design -- 3.2 Experimental Conditions -- 3.3 Evaluation Metrics -- 3.4 Results and Discussion -- 4 Conclusions and Future Work -- References -- Implementing and Evaluating ASSISTments Online Math Homework Support At large Scale over Two Years: Findings and Lessons Learned -- 1 Introduction -- 2 Background -- 2.1 The ASSISTments Program -- 2.2 Theoretical Framework -- 2.3 Research Design -- 3 Implementation of ASSISTments at Scale -- 3.1 Recruitment -- 3.2 Understanding School Context -- 3.3 Training and Continuous Support -- 3.4 Specifying a Use Model and Expectation -- 3.5 Monitoring Dosage and Evaluating Quality of Implementation -- 4 Data Collection -- 5 Analysis and Results -- 6 Conclusion -- References -- The Development of Multivariable Causality Strategy: Instruction or Simulation First? -- 1 Introduction -- 2 Literature Review -- 2.1 Learning Multivariable Causality Strategy with Interactive Simulation -- 2.2 Problem Solving Prior to Instruction Approach to Learning -- 3 Method -- 3.1 Participants -- 3.2 Design and Procedure -- 3.3 Materials -- 3.4 Data Sources and Analysis -- 4 Results. |
5 Discussion -- 6 Conclusions, Limitations, and Future Work -- References -- Content Matters: A Computational Investigation into the Effectiveness of Retrieval Practice and Worked Examples -- 1 Introduction -- 2 A Computational Model of Human Learning -- 3 Simulation Studies -- 3.1 Data -- 3.2 Method -- 4 Results -- 4.1 Pretest -- 4.2 Learning Gain -- 4.3 Error Type -- 5 General Discussion -- 6 Future Work -- 7 Conclusions -- References -- Investigating the Utility of Self-explanation Through Translation Activities with a Code-Tracing Tutor -- 1 Introduction -- 1.1 Code Tracing: Related Work -- 2 Current Study -- 2.1 Translation Tutor vs. Standard Tutor -- 2.2 Participants -- 2.3 Materials -- 2.4 Experimental Design and Procedure -- 3 Results -- 4 Discussion and Future Work -- References -- Reducing the Cost: Cross-Prompt Pre-finetuning for Short Answer Scoring -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Task Definition -- 3.2 Scoring Model -- 4 Method -- 5 Experiment -- 5.1 Dataset -- 5.2 Setting -- 5.3 Results -- 5.4 Analysis: What Does the SAS Model Learn from Pre-finetuning on Cross Prompt Data? -- 6 Conclusion -- References -- Go with the Flow: Personalized Task Sequencing Improves Online Language Learning -- 1 Introduction -- 2 Related Work -- 2.1 Adaptive Item Sequencing -- 2.2 Individual Adjustment of Difficulty Levels in Language Learning -- 3 Methodology -- 3.1 Online-Controlled Experiment -- 4 Results -- 4.1 H1 - Incorrect Answers -- 4.2 H2 - Dropout -- 4.3 H3 - User Competency -- 5 Discussion -- 6 Conclusion -- References -- Automated Hand-Raising Detection in Classroom Videos: A View-Invariant and Occlusion-Robust Machine Learning Approach -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Data -- 3.2 Skeleton-Based Hand-Raising Detection -- 3.3 Automated Hand-Raising Annotation -- 4 Results. |
4.1 Relation Between Hand-Raising and Self-reported Learning Activities -- 4.2 Hand-Raising Classification -- 4.3 Automated Hand-Raising Annotation -- 5 Discussion -- 6 Conclusion -- References -- Robust Educational Dialogue Act Classifiers with Low-Resource and Imbalanced Datasets -- 1 Introduction -- 2 Background -- 2.1 |
|
|
|
|
|
|
|
Educational Dialogue Act Classification -- 2.2 AUC Maximization on Imbalanced Data Distribution -- 3 Methods -- 3.1 Dataset -- 3.2 Scheme for Educational Dialogue Act -- 3.3 Approaches for Model Optimization -- 3.4 Model Architecture by AUC Maximization -- 3.5 Study Setup -- 4 Results -- 4.1 AUC Maximization Under Low-Resource Scenario -- 4.2 AUC Maximization Under Imbalanced Scenario -- 5 Discussion and Conclusion -- References -- What and How You Explain Matters: Inquisitive Teachable Agent Scaffolds Knowledge-Building for Tutor Learning -- 1 Introduction -- 2 SimStudent: The Teachable Agent -- 3 Constructive Tutee Inquiry -- 3.1 Motivation -- 3.2 Response Classifier -- 3.3 Dialog Manager -- 4 Method -- 5 Results -- 5.1 RQ1: Can we Identify Knowledge-Building and Knowledge-Telling from Tutor Responses to Drive CTI? -- 5.2 RQ2: Does CTI Facilitate Tutor Learning? -- 5.3 RQ3: Does CTI Help Tutors Learn to Engage in Knowledge-Building? -- 6 Discussion -- 7 Conclusion -- References -- Help Seekers vs. Help Accepters: Understanding Student Engagement with a Mentor Agent -- 1 Introduction -- 2 Mr. Davis and Betty's Brain -- 3 Methods -- 3.1 Participants -- 3.2 Interaction Log Data -- 3.3 In-situ Interviews -- 3.4 Learning and Anxiety Measures -- 4 Results -- 4.1 Help Acceptance -- 4.2 Help Seeking -- 4.3 Learning Outcomes -- 4.4 Insights from Qualitative Interviews -- 5 Conclusions -- References -- Adoption of Artificial Intelligence in Schools: Unveiling Factors Influencing Teachers' Engagement -- 1 Introduction. |
2 Context and the Adaptive Learning Platform Studied -- 3 Methodology -- 4 Results -- 4.1 Teachers' Responses to the Items -- 4.2 Predicting Teachers' Engagement with the Adaptive Learning Platform -- 5 Discussion -- 6 Conclusion -- Appendix -- References -- The Road Not Taken: Preempting Dropout in MOOCs -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Dataset -- 3.2 Modeling Student Engagement by HMM -- 3.3 Study Setup -- 4 Results -- 5 Discussion and Conclusion -- References -- Does Informativeness Matter? Active Learning for Educational Dialogue Act Classification -- 1 Introduction -- 2 Related Work -- 2.1 Educational Dialogue Act Classification -- 2.2 Sample Informativeness -- 2.3 Statistical Active Learning -- 3 Methods -- 3.1 Dataset -- 3.2 Educational Dialogue Act Scheme and Annotation -- 3.3 Identifying Sample Informativeness via Data Maps -- 3.4 Active Learning Selection Strategies -- 3.5 Study Setup -- 4 Results -- 4.1 Estimation of Sample Informativeness -- 4.2 Efficacy of Statistical Active Learning Methods -- 5 Conclusion -- References -- Can Virtual Agents Scale Up Mentoring?: Insights from College Students' Experiences Using the CareerFair.ai Platform at an American Hispanic-Serving Institution -- 1 Introduction -- 2 CareerFair.ai Design -- 3 Research Design -- 4 Results -- 5 Discussion -- 6 Conclusions and Future Directions -- References -- Real-Time AI-Driven Assessment and Scaffolding that Improves Students' Mathematical Modeling during Science Investigations -- 1 Introduction -- 1.1 Related Work -- 2 Methods -- 2.1 Participants and Materials -- 2.2 Inq-ITS Virtual Lab Activities with Mathematical Modeling -- 2.3 Approach to Automated Assessment and Scaffolding of Science Practices -- 2.4 Measures and Analyses -- 3 Results -- 4 Discussion -- References. |
Improving Automated Evaluation of Student Text Responses Using GPT-3.5 for Text Data Augmentation -- 1 Introduction -- 2 Background and Research Questions -- 3 Methods -- 3.1 Data Sets -- 3.2 Augmentation Approach -- 3.3 Model Classification -- 3.4 Baseline Evaluation -- 4 Results -- 5 Discussion -- 6 Conclusion -- 7 Future Work -- References -- The Automated Model of Comprehension |
|
|
|
|
|
|
|
|
|
Version 3.0: Paying Attention to Context -- 1 Introduction -- 2 Method -- 2.1 Processing Flow -- 2.2 Features Derived from AMoC -- 2.3 Experimental Setup -- 2.4 Comparison Between AMoC Versions -- 3 Results -- 3.1 Use Case -- 3.2 Correlations to the Landscape Model -- 3.3 Diffentiating Between High-Low Cohesion Texts -- 4 Conclusions and Future Work -- References -- Analysing Verbal Communication in Embodied Team Learning Using Multimodal Data and Ordered Network Analysis -- 1 Introduction -- 2 Methods -- 3 Results -- 3.1 Primary Tasks -- 3.2 Secondary Tasks -- 4 Discussion -- References -- Improving Adaptive Learning Models Using Prosodic Speech Features -- 1 Introduction -- 2 Methods -- 2.1 Participants -- 2.2 Design and Procedure -- 2.3 Materials -- 2.4 Speech Feature Extraction -- 2.5 Data and Statistical Analyses -- 3 Results -- 3.1 The Association Between Speech Prosody and Memory Retrieval Performance -- 3.2 Improving Predictions of Future Performance Using Speech Prosody -- 4 Discussion -- References -- Neural Automated Essay Scoring Considering Logical Structure -- 1 Introduction -- 2 Conventional Neural AES Model Using BERT -- 3 Argument Mining -- 4 Proposed Method -- 4.1 DNN Model for Processing Logical Structure -- 4.2 Neural AES Model Considering Logical Structure -- 5 Experiment -- 5.1 Setup -- 5.2 Experimental Results -- 5.3 Analysis -- 6 Conclusion -- References. |
"Why My Essay Received a 4?": A Natural Language Processing Based Argumentative Essay Structure Analysis. |
|
|
|
|
|
|
Sommario/riassunto |
|
This book constitutes the refereed proceedings of the 24th International Conference on Artificial Intelligence in Education, AIED 2023, held in Tokyo, Japan, during July 3-7, 2023. This event took place in hybrid mode. The 53 full papers and 26 short papers presented in this book were carefully reviewed and selected from 311 submissions. The papers present result in high-quality research on intelligent systems and the cognitive sciences for the improvement and advancement of education. The conference was hosted by the prestigious International Artificial Intelligence in Education Society, a global association of researchers and academics specializing in the many fields that comprise AIED, including, but not limited to, computer science, learning sciences, and education. |
|
|
|
|
|
|
|
| |