Research on the Influence Mechanism of Students'Scientific Literacy Based on Fuzzy Neural Network
The influence mechanisms of scientific literacy play a profound role in optimizing education policies,enhancing the quality of talent cultivation,and fostering scientific and technological innovation and sustainable development of the society.However,conventional classical educational statistical approaches struggle to comprehensively unravel this intricate process.To transcend this limitation,an advanced fuzzy neural network technique was employed to conduct an in-depth data mining analysis on the PISA2015 database encompassing 113,314 valid samples from 53 countries.Following meticulous feature selection,ten positively influential factors and one negatively impactful factor for improving students'scientific literacy were identified.The findings indicate that the level of socio-economic development serves as a critical determinant of students'scientific literacy levels;family investment in education exhibits the strongest correlation with the development of scientific literacy.Moreover,qualities of learning,higher-order thinking skills,and understanding of the nature of science are pivotal drivers in the advancement of scientific literacy.The application of information technology,on the other hand,exerts a double-edged sword effect on students'performance in scientific subjects.Based on these empirical discoveries,it is recommended that the education sector align with the trends of the intelligent era by leveraging machine learning for data mining,thereby driving a paradigm shift in computational educational research.Furthermore,restructuring the allocation of educational resources is advocated to promote equitable access to scientific literacy education.Lastly,reforming student nurturing strategies is essential to realizing the high-quality development of students'scientific literacy.
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