8th International Conference on Big Data and Education(ICBDE 2025)

Submission Guidelines 1. Originality: Submissions must be original and should not have been published previously. 2. Language: English is the official language of the conference. The paper should be written and presented only in English. 3. Publication & Presentation: Full paper (4 full pages at least) submission is required for paper publication. One regular registration covers 5 pages, including tables, figures, and references. Additional pages will be charged at. 4. Presentation Only: Abstract (200-300 words) submission is acceptable for presentation only and the abstract will not be published. 5. Paper Formatting: conference-latex-template.zip Submission Methods (Choose one of the two, no duplicate submissions) 1. Online System: 2. Email: .net Call For Papers 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: Topic 1: Big Data Science 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 Topic 2: Big Data Applications 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 Topic 3: Big Data Management 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 Topic 4: AI in education 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 Topic 5: Digital Learning 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 Topic 6: STEM 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 Sponsored By: North China University of Technology, China Supported By: Rajamangala University of Technology Krungthep, Thailand Contact Us Conference Secretary: Ms. Roselyn Law Email: