Prospective authors are invited to submit high-quality original technical papers for presentation at the conference and publication in the conference proceedings. Please check the topics of ICBDE 2025. The topics are not limited to those as below:
Novel theoretical models for
educational data
Computational methods for educational data analysis
Data mining techniques in online learning
Predictive analytics for student performance
Big data visualization in education
Data-driven insights for curriculum improvement
Ethical considerations in big data science
Scaling data models for educational environments
Personalized learning through big
data
Big data for intelligent tutoring systems
Data-driven strategies for course design
Adaptive learning technologies using big data
Big data in student assessment
Case studies of big data in online education
Real-time learning analytics applications
Leveraging big data for student retention
Data governance frameworks in
education
Cloud-based big data solutions for schools
Ensuring data privacy in educational systems
Real-time data processing for online education
Integrating multiple data sources in education
Data quality and consistency management
Data security protocols for educational data
Managing large-scale student data sets
AI-driven analytics for personalized
learning
Machine learning models for student success
prediction
AI-based assessment tools in education
Detecting learning gaps with AI and big data
Automating curriculum design with AI
Ethical implications of AI in education
AI and big data for intelligent classroom management
AI-driven feedback systems for students
Enhancing digital classrooms with
big data
Learning analytics for digital course delivery
Data-driven strategies for improving student
engagement
Personalization of digital learning experiences
Gamification using big data insights
Big data for improving learning outcomes
Learning behavior analytics in digital platforms
Data visualization tools for digital education
Big data for STEM curriculum design
Predictive analytics in STEM education
STEM student engagement using big data
Data-driven teaching strategies in STEM
Big data to track STEM student performance
Enhancing STEM teaching with big data tools
Personalized learning paths for STEM students
Data applications in STEM-based assessments