Design and Implementation Evaluation of Personalized and Differentiated Teaching Strategies for Preschool Children Based on Fuzzy Decision Support Systems

School operations have changed due to information technology. Personalized teaching for preschool children requires innovative and adaptable strategies for providing the best-afford experience and learning environment. Conventional teaching approaches may not always accommodate students’ learning styles. This can lower performance and involvement. The expanding variety of pre-schoolers necessitates creative evaluation and optimization methods that promote inclusion and success for all children. The article introduces a preschool teaching evaluation model to improve the students’ learning experience. In particular, the evaluation model is designed for various assessment strategies irrespective of personalization. The proposed model inputs the students’ learning feasibility and the teaching mode to evaluate the personalization adaptability. The fuzzy decision support system improves the evaluation based on the above factors. It evaluates student-specific indicators, including attentiveness, body language, and engagement, to determine instructional flexibility. The model uses real-time classroom observations and student assessments to find realistic approaches with low-to-high fuzzy derivatives. The proposed system is designed to compute the adaptability from both factors under low-to-high fuzzy derivatives. By defining the maximum feasibility range, the teaching strategy is optimized to meet the adaptability. Thus, the low-level strategies are discarded using adaptability measures to reduce personalization failures. The proposed model is verified using adaptability, feasibility, and mode improvements. Research shows that the fuzzy decision support system makes courses more adaptive and feasible in many contexts, particularly those that involve games, audiovisual approaches, and crafts. Preschoolers, parents, and teachers indicated increased enjoyment, fewer customization failures, and greater involvement. Fuzzy-based evaluation increased feasibility ratings and approach versatility. The research provides a valuable foundation for solving classroom customization difficulties in preschool settings, emphasizing data-driven, adaptive teaching techniques. This method enables scalable applications in early childhood education through fuzzy decision-making and real-time evaluations.

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Publication Details

Journal
International Journal of Computational Intelligence Systems
Published
2025-02-27
DOI
https://doi.org/10.1007/s44196-025-00748-0
Citations
6
Primary Topic
Educational Technology and Pedagogy
Type
article
Field-Weighted Citation Impact
14.89
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article

Design and Implementation Evaluation of Personalized and Differentiated Teaching Strategies for Preschool Children Based on Fuzzy Decision Support Systems

Yali Hou
6 citations
International Journal of Computational Intelligence Systems
Educational Technology and Pedagogy
14.89
article

Design and Implementation Evaluation of Personalized and Differentiated Teaching Strategies for Preschool Children Based on Fuzzy Decision Support Systems

Yali Hou
article en
6 citations

Abstract

School operations have changed due to information technology. Personalized teaching for preschool children requires innovative and adaptable strategies for providing the best-afford experience and learning environment. Conventional teaching approaches may not always accommodate students’ learning styles. This can lower performance and involvement. The expanding variety of pre-schoolers necessitates creative evaluation and optimization methods that promote inclusion and success for all children. The article introduces a preschool teaching evaluation model to improve the students’ learning experience. In particular, the evaluation model is designed for various assessment strategies irrespective of personalization. The proposed model inputs the students’ learning feasibility and the teaching mode to evaluate the personalization adaptability. The fuzzy decision support system improves the evaluation based on the above factors. It evaluates student-specific indicators, including attentiveness, body language, and engagement, to determine instructional flexibility. The model uses real-time classroom observations and student assessments to find realistic approaches with low-to-high fuzzy derivatives. The proposed system is designed to compute the adaptability from both factors under low-to-high fuzzy derivatives. By defining the maximum feasibility range, the teaching strategy is optimized to meet the adaptability. Thus, the low-level strategies are discarded using adaptability measures to reduce personalization failures. The proposed model is verified using adaptability, feasibility, and mode improvements. Research shows that the fuzzy decision support system makes courses more adaptive and feasible in many contexts, particularly those that involve games, audiovisual approaches, and crafts. Preschoolers, parents, and teachers indicated increased enjoyment, fewer customization failures, and greater involvement. Fuzzy-based evaluation increased feasibility ratings and approach versatility. The research provides a valuable foundation for solving classroom customization difficulties in preschool settings, emphasizing data-driven, adaptive teaching techniques. This method enables scalable applications in early childhood education through fuzzy decision-making and real-time evaluations.

International Journal of Computational Intelligence SystemsVol. 18(1)
Shaanxi University of Science and Technology (CN)
Peace, Justice and strong institutions
Openalex Percentile: Top 1%
Educational Technology and Pedagogy
14.89
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