ICET 2024 is the scientific conference addressing the real topics as seen by teachers, students, parents and school leaders. Both scientists, professionals and institutional leaders are invited to be informed by experts, sharpen the understanding what education needs and how to achieve it. The topics include but not limited to below:
 

Track 1: Learning Analytics and Educational Data Mining

Chair: Lecturer Dr. Xian Peng, Central China Normal University, China

 

Data-Informed Learning Theories
Learning and Teaching Processing Mining
Emotional Learning Analysis
Learner Engagement and Involvement Quantification and Analysis
Learning Early Warning and Learning Intervention
Design and Adoption of Learning Analytics Tools
Adaptive Learning Decision Support and Feedback
Multivariate and Multimodal Learning Assessment
Data-Driven Performance Prediction
Discourse analysis in Interactive Learning Environments
Collaborative Learning Analytics
Social and Epistemic Network Analysis
Ethical Issues in Learning Analytics

Track 2: Artificial Intelligence Applications and Innovations in Education

Chair: Assoc. Prof. Dr. Jianwei Li, Beijing University of Posts and Telecommunications, China

 

Modelling and Representation of individual and group learning
AI and the Future of Learning
Wearable and Undisturbed Learning Sensing Technologies
Affective Computing in Education
Generative AI in Education
AI-enabled Personalization Learning
Learning Content Recommendation
Intelligent Learning Environments
Human-AI Hybrid Systems for Learning
Intelligent Agent (Assistants)
Intelligent Tutoring Systems
Educational Process Visualization and Dashboard
AI-driven Transformation of Learning/Curriculum
AI Ethical, Privacy and Security Challenges in Education

Track 3: Technology-Enhanced Learning and Instruction

Chair: Senior Lecturer Dr. Qingqing Xing, The Hong Kong University of Science and Technology (Guangzhou), China

 

Designing Interactive Virtual Reality (VR) Simulations for Science Education
Integrating Gamification Elements into Online/Offline Course Design
Utilizing Learning Analytics to Inform Instructional Design Decisions
Video Conferencing in Learning
Social Media Integration in Instruction
Digital Resources and Tools for Instruction
Interactive Simulations for Concept Teaching in STEM Education
Automated Grading Systems: Benefits, Challenges, and Best Practices
Personalizing Feedback to Support Student Learning
Technology-Enhanced Classroom Management Strategies
Digital Attendance Tracking and Student Progress Monitoring
Innovations in Technology Integration for Instruction
Interactive Whiteboards and Multimedia Presentations
Flipped Classroom Models
Integrating Robotics and Coding in STEM Instruction
Identifying At-Risk Students and Tailoring Support Interventions
Evidence-Based Instructional Decision Making

 

Track 4:  Technology-Enabled Learning Science and Learning Mechanisms

Chair: Prof.  Hang Hu, Southwest University, China

 

Multisensing Devices Driven Cognitive Neuroscience
Behavioral, Physiological and Psychological Multilayer Computation
Bahavioral, Cognitive, Emotional Mechanisms in Interactive Learning Enviroments
Cognitive and Neuroscience Inspired Artificial Intelligence
Brain-Like Artificial Intelligence
Brain-Computer Interfaces in Education
Computational Educational Neuroscience
Mind, Brain and Educational Computing
Brain Cognitive Mechanisms in Multimedia Learning
Cognitive Computing in Human Languages
Neuroimaging for Leaner Cognitive and Emotional Modeling
Cognitive Psychology and Learning Science in Big Data

 

Track 5: Data and Teories-Driven Empirical Research in Education

Chair: Prof. Lau Bee Theng, Swinburne University of Technology Sarawak, Malaysia
 

Causal Inference Techniques in Education
Empirical Research on Artificial Intelligence Methods and Education
Data Modeling and Demonstration in Social and Behavioral Sciences
Analysis and Mediating/Moderating Effects in Technology-enhanced Education
Brain Scientific Methods and Their Applications in Educational Research
Methods and Techniques of Educational Empirical Data Collection
Application and Reflection of Structural Equation Model In Education
Application and Reflection of Large Language Models In Education
Data Mining and Learning Analytics In Educational Research
Application and Reflection of Grounded Theory in Educational Technology Research
Trends and Prospects in The Research of Educational Quality
Application and Reflection of Computationalism Research Methodology in Education
Methodological Innovation of Educational Empirical Research in Big Data
Multimodal Learning Analytics in Multi-Context Learning Situations

 

Track 6: Artificial Intelligence Enhanced Special Education

Chair: Prof. Jingying Chen, Central China Normal University, China

 

AI-based Assistive Technology for Special Children
AI-Powered Intervention Technology for Children with ASD
Intelligent Behavior Sensing for Special Children
AI-Assisted Assessment of Children with ASD
Brain Source Reconstruction for Mental Disorders
EEG Analysis for Special Children
Intelligent Screening for Children with ASD
Personalized Learning Techniques for Special Children
Human-Computer Interaction Technology for Special Education
Student Support Technology for Special Children
Intelligent Tutoring Systems for Special Education