Research on the Multimodal Digital Teaching Quality Data Evaluation Model Based on Fuzzy BP Neural Network

Abstract

We propose in this paper a fuzzy BP neural network model and DDAE-SVR deep neural network model to analyze multimodal digital teaching, establish a multimodal digital teaching quality data evaluation model based on a fuzzy BP neural network, and optimize the initial weights and thresholds of BP neural network by using adaptive variation genetic algorithm. Since the BP neural network is highly dependent on the initial weights and points, the improved genetic algorithm is used to optimize the initial weights and thresholds of the BP neural network, reduce the time for the BP neural network to find the importance and points that satisfy the training termination conditions, and improve the prediction accuracy and convergence speed of the neural network on the teaching quality evaluation results. The entropy value method, a data-based objectivity evaluation method, is introduced as the guidance mechanism of the BP neural network. The a priori guidance sample is obtained by the entropy method. Then, the adaptive variational genetic algorithm is used to optimize the BP neural network model to learn the a priori sample knowledge and establish the evaluation model, which reduces the subjectivity of the BP neural network learning sample. To better reflect and compare the effects of the two neural network evaluation models, BP and GA-BP, the sample data were continued to be input into the original GA and BSA to obtain the evaluation results and errors; then, the evaluation results of the two evaluation models, BP and GA-BP, were compared with the evaluation results of the two algorithms, GA and BSA. It was found that the GA-BP neural network evaluation model has higher accuracy and can be used for multimodal digital teaching quality evaluation, providing a more feasible solution.

Design And Realization of A Comprehensive Quality Assessment System for College Students Based on the Bp Neural Network

“…explores the application of a GA-BP neural network in evaluating online education quality in colleges and universities, highlighting the role of advanced technologies in enhancing learning outcomes. Feng and Feng (2022) contribute to this discourse with their research on a multimodal digital teaching quality evaluation model based on a fuzzy BP neural network, underscoring the importance of incorporating diverse data sources for comprehensive assessment. Meanwhile, Liu, Zhao, and Li (2023) focus on regional basic education quality assessment using a deep convolutional neural network, illustrating the adaptability of neural network models across different educational contexts.…”

“…At its core, this system embraces traditional assessments such as examinations, quizzes, and term papers to evaluate students' comprehension of course material and their ability to apply concepts. These assessments provide valuable insights into students' knowledge retention, analytical skills, and problem-solving abilities within specific academic domains [5]. autonomy and metacognitive awareness.…”

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