Artificial Intelligence in Education

Name: Artificial Intelligence in Education | 24th International Conference, AIED 2023, Tokyo, Japan, July 3-7, 2023, Proceedings
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24th International Conference, AIED 2023, Tokyo, Japan, July 3-7, 2023, Proceedings

Ning Wang Genaro Rebolledo-Mendez Noboru Matsuda Olga C. Santos Vania Dimitrova(Editor)

Springer (Publisher)

Published on 25. June 2023

XXVI, 840 pages

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978-3-031-36272-9 (ISBN)

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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
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Artificial Intelligence in Education

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