{"title":"Entertainment social media based on deep learning and interactive experience application in English e-learning teaching system","authors":"Cheng Chen","doi":"10.1016/j.entcom.2024.100846","DOIUrl":null,"url":null,"abstract":"With the rapid development of the Internet, e-learning is becoming more and more popular as a convenient and flexible way of learning. This study aims to explore the application of entertainment social media based on deep learning and interactive experience in English e-learning teaching system to improve students’ learning effect and motivation. The interface design of e-learning system incorporates gamification elements, and deep learning algorithms are used to analyze students’ learning habits and progress. The system can provide personalized learning paths and recommended content for each student to ensure that learning resources match students’ needs and abilities. A variety of interactive learning activities are built into the system to encourage students to actively participate in and practice their English speaking and listening skills. With integrated social media capabilities, students can create profiles on the learning platform, join interest groups, communicate with peers and study collaboratively. Through the data analysis of experiments and questionnaires, we found that entertainment social media based on deep learning and interactive experience can effectively improve students’ learning interest and motivation in the English e-learning teaching system. Students showed higher engagement and motivation when using the system and achieved better learning outcomes.","PeriodicalId":55997,"journal":{"name":"Entertainment Computing","volume":"182 1","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Entertainment Computing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1016/j.entcom.2024.100846","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, CYBERNETICS","Score":null,"Total":0}
引用次数: 0
Abstract
With the rapid development of the Internet, e-learning is becoming more and more popular as a convenient and flexible way of learning. This study aims to explore the application of entertainment social media based on deep learning and interactive experience in English e-learning teaching system to improve students’ learning effect and motivation. The interface design of e-learning system incorporates gamification elements, and deep learning algorithms are used to analyze students’ learning habits and progress. The system can provide personalized learning paths and recommended content for each student to ensure that learning resources match students’ needs and abilities. A variety of interactive learning activities are built into the system to encourage students to actively participate in and practice their English speaking and listening skills. With integrated social media capabilities, students can create profiles on the learning platform, join interest groups, communicate with peers and study collaboratively. Through the data analysis of experiments and questionnaires, we found that entertainment social media based on deep learning and interactive experience can effectively improve students’ learning interest and motivation in the English e-learning teaching system. Students showed higher engagement and motivation when using the system and achieved better learning outcomes.
期刊介绍:
Entertainment Computing publishes original, peer-reviewed research articles and serves as a forum for stimulating and disseminating innovative research ideas, emerging technologies, empirical investigations, state-of-the-art methods and tools in all aspects of digital entertainment, new media, entertainment computing, gaming, robotics, toys and applications among researchers, engineers, social scientists, artists and practitioners. Theoretical, technical, empirical, survey articles and case studies are all appropriate to the journal.