Teaching Skill Development of Teachers Based on High-Resolution Neural Networks in Edge Computing Environment

Abstract

In today's dynamic educational landscape, the demand for effective teaching practices necessitates continuous professional development among educators. This paper presents a novel approach to teaching skill development by leveraging high-resolution neural networks (HRNNs) within an edge computing environment. Through real-time analysis of teacher-student interactions and instructional materials, HRNN-based teaching assistance systems offer personalized support and feedback to educators, leading to significant improvements in teaching effectiveness, student engagement, and instructional efficiency. The experimental results demonstrate the transformative potential of HRNNs in revolutionizing teaching practices and enriching learning experiences. However, integration challenges and considerations, such as data privacy, computational resource constraints, and user interface design, must be addressed to fully harness the benefits of HRNN-based educational technologies. Future research and innovation in this area hold promise for democratizing access to high-quality teaching resources and preparing students for success in the digital age.