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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
| 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 |
| Autore | Wang Ning |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (863 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
Rebolledo-MendezGenaro
MatsudaNoboru SantosOlga C DimitrovaVania |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
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 |
| ISBN | 3-031-36272-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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. |
| Record Nr. | UNINA-9910734892803321 |
Wang Ning
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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
| 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 |
| Autore | Wang Ning |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (863 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
Rebolledo-MendezGenaro
MatsudaNoboru SantosOlga C DimitrovaVania |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
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 |
| ISBN | 3-031-36272-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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. |
| Record Nr. | UNISA-996538665503316 |
|
Wang Ning
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky [[electronic resource] ] : 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings / / edited by Ning Wang, Genaro Rebolledo-Mendez, Vania Dimitrova, Noboru Matsuda, Olga C. Santos
| Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky [[electronic resource] ] : 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings / / edited by Ning Wang, Genaro Rebolledo-Mendez, Vania Dimitrova, Noboru Matsuda, Olga C. Santos |
| Autore | Wang Ning |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (855 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
Rebolledo-MendezGenaro
DimitrovaVania MatsudaNoboru SantosOlga C |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
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 |
| ISBN | 3-031-36336-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Our Interactions Between AI and Education: Broadening Our Perspective on What AI Can Offer Education -- Computational Models of Learning: Deepening Care and Carefulness in AI in Education -- Towards the Future of AI-augmented Human Tutoring in Math Learning -- Empowering Education with LLMs - the Next-Gen Interface and Content Generation -- Conducting Rapid Experimentation with an Open-source Adaptive Tutoring System -- Workshop on AI Education in K-12 -- Tutorial: Educational Recommender Systems -- Equity, Diversity, and Inclusion in Educational Technology Research and Development -- AI and Educational Policy: Bridging Research and Practice -- Automated assessment and guidance of project work -- How to Open Science: Promoting Principles and Reproducibility Practices within the Artificial Intelligence in Education Community -- AI and Education. A view through the lens of human rights, democracy and the rule of law. -- AI in Education. Coming of Age. The Community Voice -- TUTORIAL: Designing, Building and Evaluating Intelligent Psychomotor AIED systems (IPAIEDS@AIED2023) -- Intelligent Textbooks -- AI to Support Guided Experiential Learning -- An Automated Approach to Assist Teachers in Recommending Groups of Students Associated with Collaborative Learning Techniques using Learning Paths in Virtual Learning Environments -- Structures in Online Discussion Forums: Promoting Inclusion or Exclusion? -- Assessment in Conversational Intelligent Tutoring Systems: Are contextual embeddings really better? -- A Recommendation System for Nurturing Students' Sense of Belonging -- Desirable Difficulties? The Effects of Spaced and Interleaved Practice in an Educational Game -- Evaluating a conversational agent for second language learning aligned with the school curriculum -- EngageMe: Assessing Student Engagement in Online Learning Environment Using Neuropsychological Tests -- Exploring the Effects of "AI-generated" Discussion Summaries on Learners’ Engagement in Online Discussions -- Building Educational Technology Quickly and Robustly with an Interactively Teachable AI -- Investigating the impact of the mindset of the learners on their behaviour in a computer-based learning environment -- Leave No One Behind - A Massive Online Learning Platform Free For Everyone -- Innovative Software to Efficiently Learn English through Extensive Reading and Personalized Vocabulary Acquisition -- A Student-Teacher Multimodal Interaction Analysis System for Classroom Observation -- Rewriting Math Word Problems to Improve Learning Outcomes for Emerging Readers: A Randomized Field Trial in Carnegie Learning's MATHia -- Automated Essay Scoring Incorporating Multi-level Semantic Features -- Promising Long Term Effects of ASSISTments Online Math Homework Support -- Using Decomposed Prompting to Answer Questions on a Course Discussion Board -- Consistency of Inquiry Strategies Across Subsequent Activities in Different Domains -- Improving the Item Selection Process with Reinforcement Learning in Computerized Adaptive Testing -- The Role of Social Presence in MOOC Students’ Behavioral Intentions and Sentiments Toward the Usage of a Learning Assistant Chatbot: A Diversity, Equity, and Inclusion Perspective Examination -- Audio Classifier for Endangered Language Analysis and Education -- Quantifying Re-Engagement in Minecraft -- Teamwork Dimensions Classification Using BERT -- Data augmentation with GAN to improve the prediction of at-risk students in a virtual learning environment -- Prediction of Students' Self-Confidence Using Multimodal Features in an Experiential Nurse Training Environment -- Learning from Auxiliary Sources in Argumentative Revision Classification -- Exploring the Effect of Autoencoder Based Feature Learning for a Deep Reinforcement Learning Policy for Providing Proactive Help -- Who and How: Using Sentence-level NLP to Evaluate Idea Completeness -- Comparing Different Approaches to Generating Mathematics Explanations Using Large Language Models -- Analyzing Response Times and Answer Feedback Tags in an Adaptive Assessment -- Enhancing the Automatic Identification of Common Math Misconceptions Using Natural Language Processing -- User Adaptive Language Learning Chatbots with a Curriculum -- Learning about circular motion of celestial bodies with interactive qualitative representations -- Desirable Difficulties? The Effects of Spaced and Interleaved Practice in an Educational Game -- GPTutor: a ChatGPT-powered programming tool for code explanation -- The Good and Bad of Stereotype Threats: Understanding Its Effects on Negative Thinking and Learning Performance in Gamified Tutoring Systems -- Practice of Tutoring Support System Based on Impasse Detection for Face-to-Face and On-demand Programming Exercises -- Investigating Patterns of Tone and Sentiment in Teacher Written Feedback Messages -- Performance by Preferences – An Experiment in Language Learning to argue for Personalization -- Emotionally Adaptive Intelligent Tutoring System To Reduce Foreign Language Anxiety -- Amortised Design Optimization for Item Response Theory -- Early Prediction of Student Performance in Online Programming Courses -- Classifying Mathematics Teacher Questions to Support Equitable and Inclusive Mathematical Teaching -- Multimodal Task-Based Language Learning System with Personalization and Dynamic Adaptation -- Bayesian Analysis of Adolescent STEM Interest Using Minecraft -- Automatic Slide Generation Using Discourse Relations -- RoboboITS: a Simulation-Based Tutoring System to Support AI Education through Robotics -- Towards analyzing psychomotor group activity for collaborative teaching using neural networks -- Warming up the Cold Start: Adaptive Step Size Method for the Urnings Algorithm -- Gamiflow: A Flow Theory-Based Gamification Framework for Learning Scenarios -- Using large language models to develop readability formulas for educational settings -- A quantitative study of NLP approaches to question difficulty estimation -- Learning from AI: An Interactive Learning Method Using a DNN Model Incorporating Expert Knowledge as a Teacher -- AI Cognitive - Based Systems Supporting Learning Processes -- Modeling problem-solving strategy invention (PSSI) in an online math learning environment -- A SHAP-inspired method for computing interaction contribution in deep knowledge tracing -- Analyzing Users’ Interaction with Writing Feedback and Their Effects on Writing Performance -- Annotating Educational Dialog Act with Data Augmentation in Online One-on-one Tutoring -- Improving Comprehension of Program Examples through Automatic Assessment and Scaffolding of Self-Explanations -- Using Transformer Language Models to Provide Formative Feedback in Intelligent Textbooks -- Utilizing Natural Language Processing for Automated Assessment of Classroom Discussion -- It’s Good to Explore: Investigating Silver Pathways and the Role of Frustration during Game-based Learning -- Ghost in the machine: AVATAR, a prototype for supporting student authorial voice -- Evaluating Language Learning Apps for Behaviour Change Using the Behaviour Change Scale -- Evaluating the Rater Bias in Response Scoring in Digital Learning Platform: Analysis of Student Writing Styles -- Generative AI for learning: Investigating the potential of learning videos with synthetic virtual instructors -- Virtual Agent Approach for Teaching the Collaborative Problem Solving Skill of Negotiation -- How Useful are Educational Questions Generated by Large Language Models? -- Towards Extracting Adaptation Rules From Neural Networks -- A Support System to Help Teachers Design Course Plans Conforming to National Curriculum Guidelines -- Predicting Student Scores Using Browsing Data and Content Information of Learning Materials -- Preserving Privacy of Face and Facial Expression in Computer Vision Data Collected in Learning Environments -- Item difficulty constrained uniform adaptive testing -- "A Fresh Squeeze on Data”: Exploring Gender Differences in Self-Efficacy and Career Interest in Computing Science and Artificial Intelligence among Elementary Students -- Simulating Learning From Language and Examples -- Learner Perception of Pedagogical Agents -- Using intelligent tutoring on the first steps of learning to program: affective and learning outcomes -- A Unified Batch Hierarchical Reinforcement Learning Framework for Pedagogical Policy Induction with Deep Bisimulation Metrics -- Impact of Experiencing Misrecognition by Teachable Agents on Learning and Rapport -- Nuanced Growth Patterns of Students with Disability -- Visualizing Self-Regulated Learner Profiles in Dashboards: Design Insights from Teachers -- Classification of brain signals collected during a rule learning paradigm -- Q-GENius: A GPT based modified MCQ generator for identifying learner deficiency -- Towards Automatic Tutoring of Custom Student-Stated Math Word Problems -- A Software Platform for Evaluating Student Essays in Interdisciplinary Learning with Topic Classification Techniques -- Automated Scoring of Logical Consistency of Japanese Essays -- Exercise Generation Supporting Adaptivity in Intelligent Tutoring Systems -- Context Matters: A Strategy to Pre-train Language Model for Science Education -- Identifying Usability Challenges in AI-based Essay Grading Tools -- Enhancing Engagement Modeling in Game-Based Learning Environments with Student-Agent Discourse Analysis.-Understanding the Impact of Reinforcement Learning Personalization on Subgroups of Students in Math Tutoring -- Automatic Assessment of Comprehension Strategies from Self-Explanations using Transformers and Multi-Task Learning -- Ensuring Fairness of Human- and AI-generated Test Items -- Deidentifying Student Writing with Rules and Transformers -- Comparative Analysis of Learnersourced Human-Graded and AI-Generated Responses for Autograding Online Tutor Lessons -- Using Similarity Learning with SBERT to Optimize Teacher Report Embeddings. |
| Record Nr. | UNISA-996546823603316 |
|
Wang Ning
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky : 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings / / edited by Ning Wang, Genaro Rebolledo-Mendez, Vania Dimitrova, Noboru Matsuda, Olga C. Santos
| Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky : 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings / / edited by Ning Wang, Genaro Rebolledo-Mendez, Vania Dimitrova, Noboru Matsuda, Olga C. Santos |
| Autore | Wang Ning |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (855 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
Rebolledo-MendezGenaro
DimitrovaVania MatsudaNoboru SantosOlga C |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
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 |
| ISBN | 3-031-36336-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Our Interactions Between AI and Education: Broadening Our Perspective on What AI Can Offer Education -- Computational Models of Learning: Deepening Care and Carefulness in AI in Education -- Towards the Future of AI-augmented Human Tutoring in Math Learning -- Empowering Education with LLMs - the Next-Gen Interface and Content Generation -- Conducting Rapid Experimentation with an Open-source Adaptive Tutoring System -- Workshop on AI Education in K-12 -- Tutorial: Educational Recommender Systems -- Equity, Diversity, and Inclusion in Educational Technology Research and Development -- AI and Educational Policy: Bridging Research and Practice -- Automated assessment and guidance of project work -- How to Open Science: Promoting Principles and Reproducibility Practices within the Artificial Intelligence in Education Community -- AI and Education. A view through the lens of human rights, democracy and the rule of law. -- AI in Education. Coming of Age. The Community Voice -- TUTORIAL: Designing, Building and Evaluating Intelligent Psychomotor AIED systems (IPAIEDS@AIED2023) -- Intelligent Textbooks -- AI to Support Guided Experiential Learning -- An Automated Approach to Assist Teachers in Recommending Groups of Students Associated with Collaborative Learning Techniques using Learning Paths in Virtual Learning Environments -- Structures in Online Discussion Forums: Promoting Inclusion or Exclusion? -- Assessment in Conversational Intelligent Tutoring Systems: Are contextual embeddings really better? -- A Recommendation System for Nurturing Students' Sense of Belonging -- Desirable Difficulties? The Effects of Spaced and Interleaved Practice in an Educational Game -- Evaluating a conversational agent for second language learning aligned with the school curriculum -- EngageMe: Assessing Student Engagement in Online Learning Environment Using Neuropsychological Tests -- Exploring the Effects of "AI-generated" Discussion Summaries on Learners’ Engagement in Online Discussions -- Building Educational Technology Quickly and Robustly with an Interactively Teachable AI -- Investigating the impact of the mindset of the learners on their behaviour in a computer-based learning environment -- Leave No One Behind - A Massive Online Learning Platform Free For Everyone -- Innovative Software to Efficiently Learn English through Extensive Reading and Personalized Vocabulary Acquisition -- A Student-Teacher Multimodal Interaction Analysis System for Classroom Observation -- Rewriting Math Word Problems to Improve Learning Outcomes for Emerging Readers: A Randomized Field Trial in Carnegie Learning's MATHia -- Automated Essay Scoring Incorporating Multi-level Semantic Features -- Promising Long Term Effects of ASSISTments Online Math Homework Support -- Using Decomposed Prompting to Answer Questions on a Course Discussion Board -- Consistency of Inquiry Strategies Across Subsequent Activities in Different Domains -- Improving the Item Selection Process with Reinforcement Learning in Computerized Adaptive Testing -- The Role of Social Presence in MOOC Students’ Behavioral Intentions and Sentiments Toward the Usage of a Learning Assistant Chatbot: A Diversity, Equity, and Inclusion Perspective Examination -- Audio Classifier for Endangered Language Analysis and Education -- Quantifying Re-Engagement in Minecraft -- Teamwork Dimensions Classification Using BERT -- Data augmentation with GAN to improve the prediction of at-risk students in a virtual learning environment -- Prediction of Students' Self-Confidence Using Multimodal Features in an Experiential Nurse Training Environment -- Learning from Auxiliary Sources in Argumentative Revision Classification -- Exploring the Effect of Autoencoder Based Feature Learning for a Deep Reinforcement Learning Policy for Providing Proactive Help -- Who and How: Using Sentence-level NLP to Evaluate Idea Completeness -- Comparing Different Approaches to Generating Mathematics Explanations Using Large Language Models -- Analyzing Response Times and Answer Feedback Tags in an Adaptive Assessment -- Enhancing the Automatic Identification of Common Math Misconceptions Using Natural Language Processing -- User Adaptive Language Learning Chatbots with a Curriculum -- Learning about circular motion of celestial bodies with interactive qualitative representations -- Desirable Difficulties? The Effects of Spaced and Interleaved Practice in an Educational Game -- GPTutor: a ChatGPT-powered programming tool for code explanation -- The Good and Bad of Stereotype Threats: Understanding Its Effects on Negative Thinking and Learning Performance in Gamified Tutoring Systems -- Practice of Tutoring Support System Based on Impasse Detection for Face-to-Face and On-demand Programming Exercises -- Investigating Patterns of Tone and Sentiment in Teacher Written Feedback Messages -- Performance by Preferences – An Experiment in Language Learning to argue for Personalization -- Emotionally Adaptive Intelligent Tutoring System To Reduce Foreign Language Anxiety -- Amortised Design Optimization for Item Response Theory -- Early Prediction of Student Performance in Online Programming Courses -- Classifying Mathematics Teacher Questions to Support Equitable and Inclusive Mathematical Teaching -- Multimodal Task-Based Language Learning System with Personalization and Dynamic Adaptation -- Bayesian Analysis of Adolescent STEM Interest Using Minecraft -- Automatic Slide Generation Using Discourse Relations -- RoboboITS: a Simulation-Based Tutoring System to Support AI Education through Robotics -- Towards analyzing psychomotor group activity for collaborative teaching using neural networks -- Warming up the Cold Start: Adaptive Step Size Method for the Urnings Algorithm -- Gamiflow: A Flow Theory-Based Gamification Framework for Learning Scenarios -- Using large language models to develop readability formulas for educational settings -- A quantitative study of NLP approaches to question difficulty estimation -- Learning from AI: An Interactive Learning Method Using a DNN Model Incorporating Expert Knowledge as a Teacher -- AI Cognitive - Based Systems Supporting Learning Processes -- Modeling problem-solving strategy invention (PSSI) in an online math learning environment -- A SHAP-inspired method for computing interaction contribution in deep knowledge tracing -- Analyzing Users’ Interaction with Writing Feedback and Their Effects on Writing Performance -- Annotating Educational Dialog Act with Data Augmentation in Online One-on-one Tutoring -- Improving Comprehension of Program Examples through Automatic Assessment and Scaffolding of Self-Explanations -- Using Transformer Language Models to Provide Formative Feedback in Intelligent Textbooks -- Utilizing Natural Language Processing for Automated Assessment of Classroom Discussion -- It’s Good to Explore: Investigating Silver Pathways and the Role of Frustration during Game-based Learning -- Ghost in the machine: AVATAR, a prototype for supporting student authorial voice -- Evaluating Language Learning Apps for Behaviour Change Using the Behaviour Change Scale -- Evaluating the Rater Bias in Response Scoring in Digital Learning Platform: Analysis of Student Writing Styles -- Generative AI for learning: Investigating the potential of learning videos with synthetic virtual instructors -- Virtual Agent Approach for Teaching the Collaborative Problem Solving Skill of Negotiation -- How Useful are Educational Questions Generated by Large Language Models? -- Towards Extracting Adaptation Rules From Neural Networks -- A Support System to Help Teachers Design Course Plans Conforming to National Curriculum Guidelines -- Predicting Student Scores Using Browsing Data and Content Information of Learning Materials -- Preserving Privacy of Face and Facial Expression in Computer Vision Data Collected in Learning Environments -- Item difficulty constrained uniform adaptive testing -- "A Fresh Squeeze on Data”: Exploring Gender Differences in Self-Efficacy and Career Interest in Computing Science and Artificial Intelligence among Elementary Students -- Simulating Learning From Language and Examples -- Learner Perception of Pedagogical Agents -- Using intelligent tutoring on the first steps of learning to program: affective and learning outcomes -- A Unified Batch Hierarchical Reinforcement Learning Framework for Pedagogical Policy Induction with Deep Bisimulation Metrics -- Impact of Experiencing Misrecognition by Teachable Agents on Learning and Rapport -- Nuanced Growth Patterns of Students with Disability -- Visualizing Self-Regulated Learner Profiles in Dashboards: Design Insights from Teachers -- Classification of brain signals collected during a rule learning paradigm -- Q-GENius: A GPT based modified MCQ generator for identifying learner deficiency -- Towards Automatic Tutoring of Custom Student-Stated Math Word Problems -- A Software Platform for Evaluating Student Essays in Interdisciplinary Learning with Topic Classification Techniques -- Automated Scoring of Logical Consistency of Japanese Essays -- Exercise Generation Supporting Adaptivity in Intelligent Tutoring Systems -- Context Matters: A Strategy to Pre-train Language Model for Science Education -- Identifying Usability Challenges in AI-based Essay Grading Tools -- Enhancing Engagement Modeling in Game-Based Learning Environments with Student-Agent Discourse Analysis.-Understanding the Impact of Reinforcement Learning Personalization on Subgroups of Students in Math Tutoring -- Automatic Assessment of Comprehension Strategies from Self-Explanations using Transformers and Multi-Task Learning -- Ensuring Fairness of Human- and AI-generated Test Items -- Deidentifying Student Writing with Rules and Transformers -- Comparative Analysis of Learnersourced Human-Graded and AI-Generated Responses for Autograding Online Tutor Lessons -- Using Similarity Learning with SBERT to Optimize Teacher Report Embeddings. |
| Record Nr. | UNINA-9910746100503321 |
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Wang Ning
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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