Sustainable Smart Education Based on AI Models Incorporating Firefly Algorithm to Evaluate Further Education

Enhui Li, Zixi Wang (), Jin Liu () and Jiandong Huang
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Enhui Li: College of Music and Dance, Guangzhou University, Guangzhou 510006, China
Zixi Wang: College of Music and Dance, Guangzhou University, Guangzhou 510006, China
Jin Liu: College of Music and Dance, Guangzhou University, Guangzhou 510006, China
Jiandong Huang: School of Civil and Transportation Engineering, Guangzhou University, Guangzhou 510006, China

Sustainability, 2024, vol. 16, issue 24, 1-22

Abstract: With the popularity of higher education and the evolution of the workplace environment, graduate education has become a key choice for students planning their future career paths. Therefore, this study proposes to use the data processing ability and pattern recognition ability of machine learning models to analyze the relevant information of graduate applicants. This study explores three different models—backpropagation neural networks (BPNN), random forests (RF), and logistic regression (LR)—and combines them with the firefly algorithm (FA). Through data selection, the model was constructed and verified. By comparing the verification results of the three composite models, the model whose evaluation results were closest to the actual data was selected as the research result. The experimental results show that the evaluation result of the BPNN-FA model is the best, with an R value of 0.8842 and the highest prediction accuracy. At the same time, the influence of each characteristic parameter on the prediction result was analyzed. The results show that CGPA has the greatest influence on the evaluation results, which provides the evaluation direction and evaluation results for the evaluators to analyze the level of students’ scientific research ability, as well as providing impetus to continue to promote the combination of education and artificial intelligence.

Keywords: backpropagation neural networks; random forests; logistic regression; student ability assessment; machine learning model; model combination; graduate education; scientific research ability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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