Optimization of the game improvement and data analysis model for the early childhood education major via deep learning

Abstract

An ever-growing portion of the economy is dedicated to the field of education, intensifying the urgency of identifying strategies to secure the sector’s enduring prosperity and elevate educational standards universally. This study introduces a model for enhancing games and optimizing data analysis within the context of early childhood education (ECE) majors, hinging on deep learning (DL). This approach aims to enhance the quality of instruction provided to ECE majors and refine the effectiveness of their professional pursuits. This study commences by examining the incorporation of DL technologies within the domain of ECE and delving into their fundamental underpinnings. Subsequently, it expounds upon the design philosophy underpinning ECE games operating within the framework of DL. Finally, it outlines the game improvement and data analysis (GIADA) model tailored to ECE majors. This model is constructed upon DL technology and further refined through the integration of convolutional neural networks (CNN). Empirical findings corroborate that the DL-CNN GIADA model achieves data analysis accuracy ranging from 83 to 93% across four datasets, underscoring the pronounced optimization prowess bestowed by CNN within the DL-based GIADA model. This study stands as an invaluable reference for the application and evolution of artificial intelligence technology within the realm of education, thereby contributing substantively to the broader landscape of educational advancement.