
Artificial Intelligence in Education
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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.More details
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Content
- 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
- 1 Introduction
- 2 Literature Review
- 2.1 Automatic Essay Scoring
- 2.2 Argument Mining
- 3 Data
- 3.1 Feedback Prize Dataset
- 3.2 ACT Writing Test Dataset
- 4 System Design
- 4.1 Datasets
- 4.2 Ensemble Model Block
- 4.3 Essay Analysis Block
- 5 Results
- 5.1 Ensemble Model Results
- 5.2 ACT Tests Essays Analysis Results
- 5.3 The Feedback Proving Process
- 6 Discussion
- 7 Conclusion
- Appendix
- References
- Leveraging Deep Reinforcement Learning for Metacognitive Interventions Across Intelligent Tutoring Systems
- 1 Introduction
- 2 Background and Related Work
- 2.1 Metacognitive Interventions for Strategy Instruction
- 2.2 Reinforcement Learning in Intelligent Tutoring Systems
- 3 Logic and Probability Tutors
- 4 Methods
- 4.1 Experiment 1: RFC-Static
- 4.2 Experiment 2: DRL-Adaptive
- 5 Experiments Setup
- 6 Results
- 6.1 Experiment 1: RFC-Static
- 6.2 Experiment 2: DRL-Adaptive
- 6.3 Post-hoc Analysis
- 7 Discussions and Conclusions
- References
- Enhancing Stealth Assessment in Collaborative Game-Based Learning with Multi-task Learning
- 1 Introduction
- 2 Related Work
- 3 Dataset
- 3.1 Out-of-Domain Labeling
- 3.2 Post-test Assessment
- 3.3 Feature Extraction
- 3.4 Class Labeling
- 4 Model Architecture
- 5 Results
- 6 Discussion
- 7 Conclusion
- References
- How Peers Communicate Without Words-An Exploratory Study of Hand Movements in Collaborative Learning Using Computer-Vision-Based Body Recognition Techniques
- 1 Introduction
- 2 Literature Review
- 3 Method
- 3.1 Participants and Learning Context
- 3.2 Data Collection
- 3.3 Data Analysis and Instruments
- 4 Results
- 5 Discussion
- References
- Scalable Educational Question Generation with Pre-trained Language Models
- 1 Introduction
- 2 Related Work
- 2.1 Automatic Question Generation (QG)
- 2.2 Pre-trained Language Models (PLMs) for Educational QG
- 2.3 Related Datasets
- 3 Methodology
- 3.1 Research Questions
- 3.2 Question Generations Models
- 3.3 Data
- 3.4 Evaluation Metrics
- 3.5 Experimental Setup
- 4 Results
- 5 Discussion
- 5.1 Ability of PLMs to Generate Educational Questions (RQ1)
- 5.2 Effect of Pre-training with a Scientific Text Corpus (RQ2)
- 5.3 Impact of the Training Size on the Question Quality (RQ3)
- 5.4 Effect of Fine-Tuning Using Educational Questions (RQ4)
- 5.5 Opportunities
- 5.6 Limitations
- 6 Conclusion
- References
- Involving Teachers in the Data-Driven Improvement of Intelligent Tutors: A Prototyping Study
- 1 Introduction
- 2 Needs-Finding Study
- 3 SolutionVis
- 4 User Study
- 5 Results
- 6 Discussion
- 7 Conclusion
- References
- Reflexive Expressions: Towards the Analysis of Reflexive Capability from Reflective Text
- 1 Introduction
- 2 From Reflective Properties, to Reflexive Interactions
- 3 Reflexivity and Reflective Writing Analytics
- 4 Methodology
- 4.1 Phase T - Theoretical Categories
- 4.2 Phase C - Computational Ngrams
- 4.3 Phase V - Verification Judgements
- 5 Findings and Discussion
- 6 Limitations and Future Work
- 7 Conclusion
- References
- Algebra Error Classification with Large Language Models
- 1 Introduction
- 2 Methodology
- 2.1 The Algebra Error Classification Task
- 2.2 Our Method
- 3 Experimental Evaluation
- 3.1 Dataset Details
- 3.2 Metrics and Baselines
- 3.3 Implementation Details
- 3.4 Results and Analysis
- 4 Discussion, Conclusion, and Future Work
- References
- Exploration of Annotation Strategies for Automatic Short Answer Grading
- 1 Introduction
- 2 Related Work
- 3 Entailment Based Answer Grading
- 3.1 Problem Formulation
- 3.2 Model Description
- 3.3 Fine-Tuning the ASAG Model
- 4 Annotation Strategies
- 5 Experimental Setting
- 6 Few-Shot Experiments
- 7 Cross-Domain Experiments
- 8 Comparison to the State-of-the-Art
- 9 Conclusion
- References
- Impact of Learning a Subgoal-Directed Problem-Solving Strategy Within an Intelligent Logic Tutor
- 1 Introduction
- 2 Related Work
- 3 Method
- 3.1 Deep Thought (DT), the Intelligent Logic Tutor
- 3.2 Experiment Design
- 4 RQ1 (Students' Experience): Difficulties Across MPS and PS Problems
- 5 RQ2: Students' Performance After Training
- 6 RQ3: Proof-Construction and Subgoaling Approach/Skills After Training
- 6.1 Student Approaches in Training-Level Test Problems
- 6.2 Student Approaches in Posttest Problems
- 7 Discussion
- 8 Conclusion and Future Work
- References
- Matching Exemplar as Next Sentence Prediction (MeNSP): Zero-Shot Prompt Learning for Automatic Scoring in Science Education
- 1 Introduction
- 2 Related Work
- 2.1 Natural Language Processing for Automatic Scoring
- 2.2 Prompt Learning
- 3 Approach
- 3.1 Matching Exemplars
- 3.2 Zero Grade Identifier
- 4 Experiment
- 4.1 Setup
- 4.2 Results
- 5 Conclusion and Discussion
- References
- Learning When to Defer to Humans for Short Answer Grading
- 1 Introduction
- 2 Related Work
- 3 Description of the Data
- 4 Methods
- 5 Results
- 6 Discussion and Conclusion
- References
- Contrastive Learning for Reading Behavior Embedding in E-book System
- 1 Introduction
- 2 Related Work
- 3 Contrastive Learning for Reading Behavior Embedding
- 3.1 Reading Log Segmentation
- 3.2 Absolute and Relative Time-Positional Encoding
- 3.3 Network Architecture
- 3.4 Network Training
- 4 Experimental Settings
- 4.1 At-Risk Student Detection Settings
- 4.2 Dataset and Parameter Settings
- 5 Experimental Results
- 5.1 Evaluation of At-Risk Student Detection
- 5.2 CRE Feature Space Analysis
- 6 Conclusion and Discussion
- References
- Generalizable Automatic Short Answer Scoring via Prototypical Neural Network
- 1 Introduction
- 2 Related Work
- 3 Method
- 4 Experiment
- 4.1 Data
- 4.2 Baseline Methods
- 4.3 Training, Evaluation, Metrics and Implementation Details
- 5 Results
- 6 Conclusion and Future Work
- References
- A Spatiotemporal Analysis of Teacher Practices in Supporting Student Learning and Engagement in an AI-Enabled Classroom
- 1 Introduction
- 1.1 Spatiotemporal Factors in Teacher Practices with AI Tutors
- 1.2 Research Questions and Hypotheses
- 2 Methods
- 2.1 Case Study Context
- 2.2 Teacher Visits in the Temporal Context of Student Learning
- 3 Results
- 3.1 RQ1: Factors Associated with Teacher's Choice of Students to Visit
- 3.2 RQ2: Teacher Visit Associations with Student Engagement and Learning
- 4 Discussion and Conclusion
- References
- Trustworthy Academic Risk Prediction with Explainable Boosting Machines
- 1 Introduction
- 2 Background
- 2.1 Academic Risk Prediction
- 2.2 Explainable AI in Education
- 2.3 Explainable Boosting Machines
- 3 Methods
- 3.1 Study Area and Data
- 3.2 Training
- 3.3 Model Assessment
- 4 Experimental Results
- 4.1 Explainability of EBMs
- 4.2 Accuracy
- 4.3 Earliness and Stability
- 4.4 Fairness
- 4.5 Faithfulness of Explanations
- 4.6 Discussion
- 5 Conclusion and Outlook
- References
- Automatic Educational Question Generation with Difficulty Level Controls
- 1 Introduction
- 2 Related Work
- 3 Problem Formulation
- 4 Approach
- 4.1 Age of Acquisition Based Sampling
- 4.2 Energy Components
- 5 Experiments
- 5.1 Data Preparation
- 5.2 Experiment Settings
- 5.3 Expert Models
- 5.4 Baselines
- 5.5 Question Quality Evaluations and Observations
- 5.6 Difficulty Controllability Analysis
- 6 User Study
- 7 Conclusion
- References
- Getting the Wiggles Out: Movement Between Tasks Predicts Future Mind Wandering During Learning Activities
- 1 Introduction
- 1.1 Background and Related Work
- 1.2 Current Work: Contributions and Novelty
- 2 Methods
- 2.1 Data Collection
- 2.2 Machine Learning Models
- 2.3 Sensors, Data Processing, and Feature Extraction
- 3 Results
- 3.1 Model Comparisons
- 3.2 Predictive Features
- 4 Discussion
- References
- Efficient Feedback and Partial Credit Grading for Proof Blocks Problems
- 1 Introduction
- 2 Related Work
- 2.1 Software for Learning Mathematical Proofs
- 2.2 Edit Distance Based Grading and Feedback
- 3 Proof Blocks
- 4 The Edit Distance Algorithm
- 4.1 Mathematical Preliminaries
- 4.2 Problem Definition
- 4.3 Baseline Algorithm
- 4.4 Optimized (MVC-Based) Implementation of Edit Distance Algorithm
- 4.5 Worked Example of Algorithm 2
- 4.6 Proving the Correctness of Algorithm 2
- 5 Benchmarking Algorithms on Student Data
- 5.1 Data Collection
- 5.2 Benchmarking Details
- 5.3 Results
- 6 Conclusions and Future Work
- References
- Dropout Prediction in a Web Environment Based on Universal Design for Learning
- 1 Introduction
- 2 Related Work
- 3 Research Questions
- 4 Methodology
- 4.1 The Learning Platform I3Learn and Dropout Level
- 4.2 Data Collection and Features
- 4.3 Data Aggregation
- 5 Dataset
- 6 Results
- 6.1 RQ 1: Transfer of Methods for Dropout Prediction
- 6.2 RQ 2: Dropout Prediction with Data of Diverse Granularity
- 6.3 RQ 3: Assessments for Predicting Dropout
- 7 Conclusion
- References
- Designing for Student Understanding of Learning Analytics Algorithms
- 1 Introduction
- 2 Prior Work
- 3 Knowledge Components of BKT
- 4 The BKT Interactive Explanation
- 5 Impact of Algorithmic Transparency on User Understanding and Perceptions
- 6 Conclusion
- References
- Feedback and Open Learner Models in Popular Commercial VR Games: A Systematic Review
- 1 Introduction
- 2 Background
- 3 Methods: Game Selection and Coding
- 4 Results
- 5 Discussion and Conclusions
- References
- Gender Differences in Learning Game Preferences: Results Using a Multi-dimensional Gender Framework
- 1 Introduction
- 2 Methods
- 2.1 Participants
- 2.2 Materials and Procedures
- 3 Results
- 3.1 Game Genre Preferences
- 3.2 Game Narrative Preferences
- 3.3 Post-hoc Analyses
- 4 Discussion and Conclusion
- References
- Can You Solve This on the First Try? - Understanding Exercise Field Performance in an Intelligent Tutoring System
- 1 Introduction
- 2 Related Work
- 2.1 Academic Performance Prediction
- 2.2 Investigating Factors that Influence Academic Performance
- 2.3 Explainable AI for Academic Performance Prediction
- 3 Methodology
- 3.1 Dataset
- 3.2 Data Analysis
- 4 Results
- 5 Discussion and Conclusion
- References
- A Multi-theoretic Analysis of Collaborative Discourse: A Step Towards AI-Facilitated Student Collaborations
- 1 Introduction
- 1.1 Background and Related Work
- 2 Methods
- 3 Results and Discussion
- 4 General Discussion
- References
- Development and Experiment of Classroom Engagement Evaluation Mechanism During Real-Time Online Courses
- 1 Introduction
- 2 Related Work
- 2.1 Behavior Estimation
- 2.2 Class Evaluation System
- 3 Online Education Platform
- 4 Multi-reaction Estimation
- 4.1 Student Head Reaction
- 4.2 Student Expression Reaction
- 5 Online Classroom Evaluation
- 6 Experiments
- 6.1 Experiment I: Instruction Experiment
- 6.2 Experiment II: Simulation Classroom Experiment
- 7 Discussion
- 8 Conclusion
- References
- Physiological Synchrony and Arousal as Indicators of Stress and Learning Performance in Embodied Collaborative Learning
- 1 Introduction
- 2 Background and Related Work
- 3 Methods
- 3.1 Educational Context
- 3.2 Apparatus and Data Collection
- 3.3 Feature Engineering
- 3.4 Analysis
- 4 Results
- 5 Discussion
- 6 Conclusion
- References
- Confusion, Conflict, Consensus: Modeling Dialogue Processes During Collaborative Learning with Hidden Markov Models
- 1 Introduction and Related Work
- 2 Methods
- 3 Results
- 4 Discussion
- 4.1 Dialogue States
- 4.2 Transitions into and Out of Exploratory Talk
- 4.3 Design Implications
- 5 Conclusion
- References
- Unsupervised Concept Tagging of Mathematical Questions from Student Explanations
- 1 Introduction
- 2 Related Work
- 3 Experiment Setup
- 4 Unsupervised Question Tagging Based on Student Explanations
- 4.1 Manual Tagging Based on Drag-and-Drop Activity
- 4.2 Our Method: Unsupervised Skill Tagging (UST)
- 5 Results and Discussion
- 6 Conclusion and Future Work
- References
- Robust Team Communication Analytics with Transformer-Based Dialogue Modeling
- 1 Introduction
- 2 Related Work
- 3 Team Communication
- 4 Dataset
- 5 Team Communication Analysis Framework
- 6 Evaluation
- 7 Conclusion
- References
- Teacher Talk Moves in K12 Mathematics Lessons: Automatic Identification, Prediction Explanation, and Characteristic Exploration
- 1 Introduction
- 2 Related Work
- 2.1 Automated Models on Classroom Discourse
- 2.2 Explainable Artificial Intelligence
- 3 Method
- 3.1 Dataset
- 3.2 Model Construction
- 3.3 Model Explanation
- 4 Experiments and Results
- 4.1 Interpreting Results Validation
- 4.2 Talk Move Characteristics Exploration
- 5 Discussion and Conclusion
- References
- Short Papers
- Ethical and Pedagogical Impacts of AI in Education
- 1 Introduction
- 2 Research Method
- 3 Results, Discussion and Practical Implication
- 3.1 Learner Status
- 3.2 Learning Environment and Experience
- 3.3 Educational Processes and Approaches
- 3.4 Interaction, Pedagogical Relationship, and Roles
- 4 Conclusion
- References
- Multi-dimensional Learner Profiling by Modeling Irregular Multivariate Time Series with Self-supervised Deep Learning
- 1 Introduction
- 2 Approach
- 3 Experiments
- 4 Conclusion
- References
- Examining the Learning Benefits of Different Types of Prompted Self-explanation in a Decimal Learning Game
- 1 Introduction
- 2 A Digital Learning Game for Decimal Numbers
- 3 Methods
- 4 Results
- 5 Discussion and Conclusion
- References
- Plug-and-Play EEG-Based Student Confusion Classification in Massive Online Open Courses
- 1 Introduction
- 2 Dataset
- 3 Methodology
- 4 Results
- 5 Conclusion
- References
- CPSCoach: The Design and Implementation of Intelligent Collaborative Problem Solving Feedback
- 1 Introduction and Related Work
- 2 Intervention Design and User Study
- 3 Results and Discussion
- References
- Development of Virtual Reality SBIRT Skill Training with Conversational AI in Nursing Education
- 1 Introduction
- 1.1 Conversational AI in Healthcare Education
- 2 Interaction Design and Implementation of the Application
- 2.1 SBIRT Conversation Data Collection
- 2.2 Design of Interaction Modes
- 3 Mode 3: Virtual Patient Conversational AI Mode.
- 4 Conclusion
- References
- Balancing Test Accuracy and Security in Computerized Adaptive Testing
- 1 Introduction
- 2 Methodology
- 2.1 BOBCAT Background
- 2.2 C-BOBCAT
- 3 Experiments
- 3.1 Data, Experimental Setup, Baseline, and Evaluation Metrics
- 3.2 Results and Discussion
- 4 Conclusions and Future Work
- References
- A Personalized Learning Path Recommendation Method for Learning Objects with Diverse Coverage Levels
- 1 Introduction
- 2 The Learning Path Recommendation Framework
- 3 The Graph-Based Genetic Algorithm
- 3.1 Problem Modeling
- 3.2 Chromosome Representation
- 3.3 The Genetic Operators
- 4 Experiments
- 5 Conclusion
- References
- Prompt-Independent Automated Scoring of L2 Oral Fluency by Capturing Prompt Effects
- 1 Introduction
- 2 Related Work
- 3 Prompt-Independent Automated Fluency Scoring
- 4 Experiment
- 5 Results and Discussion
- References
- Navigating Wanderland: Highlighting Off-Task Discussions in Classrooms
- 1 Introduction
- 2 Methodology
- 2.1 Dataset
- 2.2 Models
- 3 Results
- 4 Conclusion
- References
- C2Tutor: Helping People Learn to Avoid Present Bias During Decision Making
- 1 Introduction
- 2 Literature Review and Research Gaps
- 3 C2Tutor - Reducing Present Bias
- 4 Experimental Design
- 5 Findings
- 6 Conclusion and Future Work
- References
- A Machine-Learning Approach to Recognizing Teaching Beliefs in Narrative Stories of Outstanding Professors
- 1 Introduction
- 2 Related Work
- 3 Dataset
- 4 Methodology
- 5 Evaluation
- 5.1 Evaluation Setting
- 5.2 Comparative Method
- 5.3 Evaluation Result
- 6 Conclusion
- References
- BETTER: An Automatic feedBack systEm for supporTing emoTional spEech tRaining
- 1 Introduction
- 2 Related Work
- 3 Methodology
- 3.1 BETTER Version 1.0
- 3.2 Preliminary Experiment
- 3.3 BETTER Version 2.0
- 4 Conclusion
- References
- Eliciting Proactive and Reactive Control During Use of an Interactive Learning Environment
- 1 Introduction
- 2 Task Design
- 3 Study 1: Inducing Proactive and Reactive Control
- 4 Study 2: Shifting from Proactive to Reactive Control
- 5 Discussion
- References
- How to Repeat Hints: Improving AI-Driven Help in Open-Ended Learning Environments
- 1 Introduction
- 2 UnityCT
- 3 User Study
- 4 Analysis and Results
- 5 Discussion and Future Work
- References
- Automatic Detection of Collaborative States in Small Groups Using Multimodal Features
- 1 Introduction
- 2 Methods
- 2.1 Data Collection
- 2.2 Annotations
- 2.3 Verbal Features
- 2.4 Prosodic Features
- 2.5 Model Training
- 3 Results
- 4 Discussion
- 4.1 Qualitative Error Analysis
- 5 Limitations, Future Work, and Conclusion
- References
- Affective Dynamic Based Technique for Facial Emotion Recognition (FER) to Support Intelligent Tutors in Education
- 1 Introduction
- 2 Proposed Method
- 2.1 Affective Dynamics Model
- 2.2 Affective Dynamics Based FER Technique
- 3 Experimental Evaluations
- 3.1 Results
- 4 Conclusion
- References
- Real-Time Hybrid Language Model for Virtual Patient Conversations
- 1 Introduction
- 2 Related Works
- 3 Dataset
- 4 Methodology
- 5 Results and Discussion
- 6 Conclusion
- References
- Towards Enriched Controllability for Educational Question Generation
- 1 Introduction
- 2 Generating Explicit and Implicit Questions
- 3 Experimental Setup
- 4 Results
- 5 Conclusion
- References
- A Computational Model for the ICAP Framework: Exploring Agent-Based Modeling as an AIED Methodology
- 1 Introduction
- 2 ABICAP
- 2.1 Learning
- 3 Simulations and Results
- 4 Discussion
- References
- Automated Program Repair Using Generative Models for Code Infilling
- 1 Introduction
- 2 Background
- 3 Methodology
- 4 Results
- 5 Discussion and Conclusion
- References
- Measuring the Quality of Domain Models Extracted from Textbooks with Learning Curves Analysis
- 1 Introduction
- 2 Background
- 3 Experiment
- 4 Results and Analysis
- 5 Conclusion and Future Work
- References
- Predicting Progress in a Large-Scale Online Programming Course
- 1 Introduction
- 2 Background and Related Work
- 3 Data
- 4 Method
- 4.1 Slide Interaction Data Extraction and Train-Test Split
- 4.2 Feature Selection and Ranking
- 4.3 Classification
- 5 Results
- 5.1 Evaluation by Educators
- 6 Discussion
- 6.1 Accuracy in Predicting End of Module Outcomes
- 6.2 Effects of Feature Selection
- 6.3 Key Observations on Chosen Slides
- 7 Conclusion
- References
- Examining the Impact of Flipped Learning for Developing Young Job Seekers' AI Literacy
- 1 Introduction
- 2 Methodology
- 2.1 Participants and Research Design
- 2.2 Data Collection and Analysis
- 3 Results
- 3.1 Academic Achievement
- 3.2 Educational Satisfaction
- 3.3 Focus Group Interview
- 4 Discussion and Conclusion
- References
- Automatic Analysis of Student Drawings in Chemistry Classes
- 1 Introduction
- 2 Automatic Categorization of Student Drawings
- 2.1 Automatic Dataset Creation
- 2.2 Detection of Objects in Student Drawings
- 2.3 Classification of Drawing Characteristics
- 3 Experimental Evaluation
- 3.1 Datasets
- 3.2 Detection of Objects
- 3.3 Classification of Drawing Characteristics Used by Students
- 4 Conclusions
- References
- Training Language Models for Programming Feedback Using Automated Repair Tools
- 1 Introduction
- 2 Related Work
- 3 Methodology
- 4 Results and Discussion
- 5 Conclusion
- References
- Author Index
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