Automated Teaching Weighted Recurrent Neural Network (Atwrnn) Model: Analysis of Badminton Teaching Mode Based on Online Teaching Platform

Abstract

An online teaching platform serves as a virtual environment where educators and learners interact, collaborate, and engage in various educational activities. Through a combination of multimedia resources, interactive tools, and communication features, online teaching platforms facilitate effective remote learning experiences. Educators can create and deliver engaging instructional content, including video lectures, presentations, quizzes, and assignments, tailored to the needs and preferences of learners. These platforms often incorporate features such as discussion forums, live chat, and virtual classrooms, enabling real-time communication and collaboration among students and instructors. This paper presents an analysis of the badminton teaching mode facilitated by an online teaching platform, augmented with an Automated Teaching Weighted Recurrent Neural Network (ATwRNN). Through this innovative approach, the traditional method of badminton instruction is enhanced by leveraging advanced machine learning techniques to personalize and optimize the teaching process. The online teaching platform provides a virtual environment where instructors can deliver interactive lessons, instructional videos, and practice exercises tailored to individual learner needs. ATwRNN, a specialized neural network model, dynamically adjusts the teaching weightings based on student performance data, providing personalized feedback and adaptive instruction in real time. Simulation results stated that learners who received instruction through the ATwRNN-enhanced platform demonstrated a 25% increase in shot accuracy compared to those in traditional teaching modes. Additionally, the adaptive nature of ATwRNN led to a 30% reduction in the time required for skill acquisition, as learners received personalized feedback tailored to their individual learning pace and preferences. Moreover, user feedback surveys revealed high levels of satisfaction and engagement among participants, with 90% reporting that the ATwRNN-enhanced teaching mode improved their overall learning experience.