Buch, Englisch, 750 Seiten, Format (B × H): 155 mm x 235 mm
Proceedings of the 2026 5th International Symposium on Big Data and Artificial Intelligence (ISBDAI2026)
Buch, Englisch, 750 Seiten, Format (B × H): 155 mm x 235 mm
Reihe: Lecture Notes in Networks and Systems
ISBN: 978-3-032-38733-2
Verlag: Springer Nature Switzerland AG
This book presents recent research on the intersection of Data Science, Cloud Computing, IoT, AI, Computer Science, and education. Currently, the fields of big data, artificial intelligence, and education are undergoing rapid development and transformation, with the integrated application of related new technologies becoming the focus of both academic and industry attention. To promote progress in this interdisciplinary field, academia and industry need to build an efficient communication platform to share cutting-edge achievements and practical experiences. The methods of research, teaching, and application have fundamentally changed, driven by the extensive use of advanced technical means such as intelligent algorithms, big data analysis, and smart pedagogy.
In addition, the continuous emergence of various emerging technologies is currently challenging traditional educational models, data management, and instructional systems. Effective practices in leveraging artificial intelligence and big data for educational transformation must be built upon solid theoretical foundations and rich real-world application cases.
The 5th International Symposium on Big Data and Artificial Intelligence: For Education Transformation (ISBDAI 2026), held at The Hong Kong Polytechnic University (PolyU), Hong Kong, China, from May 26 to 28, 2026, served as an ideal platform for presenting and discussing current trends at the intersection of computing technologies and educational innovation.
Interested readership includes policy makers in the field of educational data governance, academics specializing in artificial intelligence and big data analytics, researchers in computer science and information technology, industry professionals engaged in educational technology (EdTech) development, administrators and leaders in higher education institutions, practitioners in the big data and smart systems industries, and lecturers and educators implementing technology-enhanced learning environments, among others.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Personalized Learning Path Recommendation System Based on Knowledge Graph and Multimodal Learning.- Reconstructing the General Education Knowledge Graph: A Study on AI Course Generation Model Based on LDA Topic Modeling and Node Centrality Analysis.- Analysis of Tourism Service Quality Based on the SERVQUAL Model Enhanced by
Machine Learning.- Research on Optimizing Semantic Recognition in Home-School Collaborative General Curriculum with the BERT Model.
