{"title":"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","authors":"Huaying Zhang","doi":"10.1007/s12652-024-04765-3","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Design of students’ learning state evaluation model in online education based on double improved neural network\",\"authors\":\"Huaying Zhang\",\"doi\":\"10.1007/s12652-024-04765-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":14959,\"journal\":{\"name\":\"Journal of Ambient Intelligence and Humanized Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Ambient Intelligence and Humanized Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s12652-024-04765-3\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Ambient Intelligence and Humanized Computing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s12652-024-04765-3","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
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.
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