The Quality Evaluation of Innovation and Entrepreneurship Education in Colleges and Universities in the Context of Big Data
DOI:
https://doi.org/10.4108/eetsis.7015Keywords:
Big Data, BDC, Cloud Computing, Convolutional Neural Network, CNN, Correlation Analysis, Data techniques, Entrepreneurship Education, Instructional Procedures, Optimization Technique, Pedagogical Infrastructure, Security Metrics, Simulation ResultsAbstract
INTRODUCTION: The advancement of appropriate instructional procedures and data techniques had already facilitated the development of a virtual evolutional innovation and entrepreneurship education process.
OBJECTIVES: The adoption of big data has also tended to result in academic revolutionary movements for businesses. To address the limitations of current university instructional setups and to broaden the scope of Big Data's (BDC) application, this study presents an optimization technique for evaluating the quality of innovation and entrepreneurship education.
METHODS: This paper presents an optimization technique for measuring the quality of education in innovation and entrepreneurship. This technique should help us move beyond the constraints of our existing approaches to higher education's pedagogical infrastructure into the domain of big data.
RESULTS: The proposed optimization algorithm differs from conventional university quality evaluation instructional practices as it integrates big data that dramatically improves the college Entrepreneurship class interaction. This article proposed a Convolution neural network algorithm with BDC for training virtualization representation and employs a standard correlation analysis method in data analysis to retrieve the correlation relationship between the information content enclosed in huge entrepreneurship education and online students.
CONCLUSION: Simulation results revealed that the proposed model has provided an accuracy of 98%. The proposed method provides a platform for sharing instructional materials that function more efficiently under heavy load. Up to 98% and 94% security are achieved under heavy and light loads, respectively. The use of cloud computing in this scenario led to improvements of 7% and 8%, respectively, yielding results of 89% and 86%.
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