Research on Grade Point Innovation and Grade Point Average Based on Deep Learning Networks and Evolutionary Algorithms for College Innovation Education

• DOI: 10.3390/su17052171
• Publication Date: 2025-03-03T10:52:16Z
• Authors: Yang Zhang; Ziyu Hu

This study applies deep learning predictive networks and multi-objective decision-making algorithms to the context of innovation and entrepreneurship education, aiming to explore the characteristics of students in different majors regarding innovation and entrepreneurship. It also investigates how their inputs contribute to the enhancement of their innovation and entrepreneurship abilities, as well as the improvement of their academic performance. The researchers designed survey questions across four levels: internal and external factors, and subjective and objective factors. Longitudinal data are collected from 650 students at different grade levels. The results show a clear positive correlation between grade point innovation (GPI) and grade point average (GPA), and the relationship between students’ learning characteristics and GPI and GPA is established using a deep network of deep kernel extreme learning machines. The strategies in the questionnaire are used as control variables to obtain learning strategies for different students using a multi-objective decision-making approach based on evolutionary algorithms. This study shows the effect of different resources on the improvement of students’ innovation abilities and provides possible innovation strategy suggestions for different groups. The results of this study may contribute to the improvement of innovation and entrepreneurial curricula and educational methods.