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15 January 2024 Improving strategies for educational imbalance based on large language model
Bochun Cao
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Proceedings Volume 12983, Second International Conference on Electrical, Electronics, and Information Engineering (EEIE 2023); 1298321 (2024) https://doi.org/10.1117/12.3017920
Event: Second International Conference on Electrical, Electronics, and Information Engineering (EEIE 2023), 2023, Wuhan, China
Abstract
It can be observed on a global scale that the quality of education between different regions (such as between urban and rural areas) is usually affected by the local economic structure and development stage. For example, many well-known metropolises have high education levels and famous colleges and universities, and the proportion of students admitted to prestigious universities is also high. Rural areas often lack famous schools and corresponding resources, resulting in lower overall student performance and a lower proportion of students admitted to prestigious universities. This article uses statistical methods to study this issue. Further use data including infrastructure investment, hiring more high-quality teachers, providing more diverse and inclusive courses, providing more learning materials and resources, and using a variety of machine learning and deep learning and large language model techniques to predict the growth of educational resources in underdeveloped areas. Evaluation of these predictive models reveals the strength of large language models in predicting the growth of educational resources in underdeveloped regions. The findings provide strategic insights for education policy makers and stakeholders to close pervasive education disparities.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Bochun Cao "Improving strategies for educational imbalance based on large language model", Proc. SPIE 12983, Second International Conference on Electrical, Electronics, and Information Engineering (EEIE 2023), 1298321 (15 January 2024); https://doi.org/10.1117/12.3017920
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KEYWORDS
Machine learning
Data modeling
Deep learning
Artificial intelligence
Detection and tracking algorithms
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