基于双改进神经网络的在线教育中学生学习状态评价模型的设计

3区 计算机科学 Q1 Computer Science
Huaying Zhang
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引用次数: 0

摘要

在当今信息技术高度发达的时代,在线教育逐渐成为一种重要的教学模式。在线教育通过网络平台提供便捷的学习资源和灵活的学习方式,学生可以根据自己的时间安排和学习需求进行学习。然而,与传统教育相比,在线教育面临着一些挑战,其中之一就是如何准确评估学生的学习状况。设计基于双改进神经网络的在线教育学生学习状态评价模型,旨在提高学生的学习效果。利用系统聚类统计方法初步分析网络教育学生学习状态的影响因素,构建初始评价指标体系;利用 Apriori 算法对初始指标进行筛选,最终构建网络教育学生学习状态评价指标体系。利用小波去噪方法去除评价指标数据中的噪声,构建双改进径向基函数神经网络模型作为输入。利用 K-means 聚类算法确定网络中的隐层数,从而确定网络结构;在最优网络结构的基础上,利用状态转换算法调整网络参数,将训练好的神经网络用于在线教育学生学习状态评价,输出在线教育学生学习状态的最终评价结果。实验结果表明,基于最优的模型结构和参数,模型的指标信息贡献率达到了93%,能够准确评价在线教育学生的学习状态。上述结果表明,所构建的模型可以帮助教师和学生实时了解学生的学习需求和困难,并提供相应的教学支持和指导,促进个性化教学,改善学生的学习体验和学习效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Design of students’ learning state evaluation model in online education based on double improved neural network

Design of students’ learning state evaluation model in online education based on double improved neural network

In today's highly developed era of information technology, online education is gradually becoming an important teaching mode. Online education provides convenient learning resources and flexible learning methods through online platforms, allowing students to learn according to their own schedule and learning needs. However, compared to traditional education, online education faces some challenges, one of which is how to accurately assess students' learning status. Design an online education student learning status evaluation model based on dual improved neural networks with the aim of improving student learning effectiveness. Using systematic clustering statistical methods to preliminarily analyze the influencing factors of online education students' learning status, and construct an initial evaluation index system; Using the Apriori algorithm to filter the initial indicators, a final online education student learning status evaluation index system is constructed. Using wavelet denoising method to remove noise from evaluation index data, a dual improved radial basis function neural network model is constructed as input. Determine the number of hidden layers in the network using the K-means clustering algorithm, thereby determining the network structure; Based on the optimal network structure, the state transition algorithm is used to adjust the network parameters, and the trained neural network is used for online education student learning state evaluation, outputting the final evaluation result of online education student learning state. The experimental results show that the contribution rate of the model's indicator information reaches 93%, which can accurately evaluate the learning status of online education students based on the optimal model structure and parameters. The above results demonstrate that the constructed model can help teachers and students understand students' learning needs and difficulties in real-time, and provide corresponding teaching support and guidance to promote personalized teaching and improve students' learning experience and outcomes.

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来源期刊
Journal of Ambient Intelligence and Humanized Computing
Journal of Ambient Intelligence and Humanized Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.60
自引率
0.00%
发文量
854
期刊介绍: The purpose of JAIHC is to provide a high profile, leading edge forum for academics, industrial professionals, educators and policy makers involved in the field to contribute, to disseminate the most innovative researches and developments of all aspects of ambient intelligence and humanized computing, such as intelligent/smart objects, environments/spaces, and systems. The journal discusses various technical, safety, personal, social, physical, political, artistic and economic issues. The research topics covered by the journal are (but not limited to): Pervasive/Ubiquitous Computing and Applications Cognitive wireless sensor network Embedded Systems and Software Mobile Computing and Wireless Communications Next Generation Multimedia Systems Security, Privacy and Trust Service and Semantic Computing Advanced Networking Architectures Dependable, Reliable and Autonomic Computing Embedded Smart Agents Context awareness, social sensing and inference Multi modal interaction design Ergonomics and product prototyping Intelligent and self-organizing transportation networks & services Healthcare Systems Virtual Humans & Virtual Worlds Wearables sensors and actuators
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