BIG DATA ANALYSIS AND INFORMATION SHARING FOR INNOVATION AND ENTREPRENEURSHIP EDUCATION.

QIAN XIE

This study delves into the transformative potential of integrating big data analysis and information sharing in innovation and entrepreneurship education. Employing a comprehensive methodology encompassing K-Means Clustering, Decision Trees, Apriori Algorithm, and Neural Networks, the research investigates student engagement patterns, influential factors, collaborative relationships, and predictive modelling within educational settings. The findings reveal significant outcomes, with K-Means achieving a clustering precision of 75%, Decision Trees demonstrating an accuracy of 82%, Apriori Algorithm uncovering frequent itemsets with 68% support, and Neural Networks achieving a notable accuracy of 90%. Drawing insights from a diverse range of literature, including studies on big data management, demand prediction models, ecological approaches to entrepreneurship education, qualitative inquiries into startup strategies, applications of ICTs in education, and the impact of virtual gaming on SMEs' growth, the research provides a robust foundation for understanding innovation and entrepreneurship education. This study contributes to both theoretical understanding and practical implications, guiding educators and policymakers in tailoring interventions and strategies to foster an adaptive and effective educational environment..

APRIORI algorithm; DATA analytics; ENTREPRENEURSHIP education; K-means clustering; EDUCATIONAL innovations; DEMAND forecasting

Scalable Computing: Practice & Experience, 2024, Vol 25, Issue 6, p5518

1895-1767

Academic Journal

10.12694/scpe.v25i6.3346