Andrew Kemp, Edward Palmer, Peter Strelan, Helen (Mery) Thompson
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Exploratory factor analysis, confirmatory factor analysis and structural equation modelling were used to analyse the data. The model accounted for 78% of variance of behavioural intent, with comfort and well-being demonstrating the strongest influence. Cognitive engagement, access and convenience influenced perceived usefulness, and system attributes and self-efficacy influenced perceived ease of use. Feedback, instructor practice and class interaction and communication were not significant as educational constructs for this cohort. Based on this analysis, a final extended educational technology acceptance model (EETAM) is proposed for further use and testing.\n </p>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"55 5","pages":"2110-2131"},"PeriodicalIF":6.7000,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13440","citationCount":"0","resultStr":"{\"title\":\"Testing a novel extended educational technology acceptance model using student attitudes towards virtual classrooms\",\"authors\":\"Andrew Kemp, Edward Palmer, Peter Strelan, Helen (Mery) Thompson\",\"doi\":\"10.1111/bjet.13440\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Many technology acceptance models used in education were originally designed for general technologies and later adopted by education researchers. This study extends Davis' technology acceptance model to specifically evaluate educational technologies in higher education, focusing on virtual classrooms. Prior research informed the construction of the model, which contains perceived usefulness, perceived ease of use, behavioural intent, access and convenience, system attributes and self-efficacy. Education-specific constructs include cognitive engagement, feedback, instructor practice and class interaction and communication. Additionally, a new construct called comfort and well-being is introduced. A total of 427 valid responses on a 5-point Likert scale were received from university students. Exploratory factor analysis, confirmatory factor analysis and structural equation modelling were used to analyse the data. The model accounted for 78% of variance of behavioural intent, with comfort and well-being demonstrating the strongest influence. Cognitive engagement, access and convenience influenced perceived usefulness, and system attributes and self-efficacy influenced perceived ease of use. Feedback, instructor practice and class interaction and communication were not significant as educational constructs for this cohort. Based on this analysis, a final extended educational technology acceptance model (EETAM) is proposed for further use and testing.\\n </p>\",\"PeriodicalId\":48315,\"journal\":{\"name\":\"British Journal of Educational Technology\",\"volume\":\"55 5\",\"pages\":\"2110-2131\"},\"PeriodicalIF\":6.7000,\"publicationDate\":\"2024-02-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13440\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"British Journal of Educational Technology\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/bjet.13440\",\"RegionNum\":1,\"RegionCategory\":\"教育学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"EDUCATION & EDUCATIONAL RESEARCH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"British Journal of Educational Technology","FirstCategoryId":"95","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/bjet.13440","RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
Testing a novel extended educational technology acceptance model using student attitudes towards virtual classrooms
Many technology acceptance models used in education were originally designed for general technologies and later adopted by education researchers. This study extends Davis' technology acceptance model to specifically evaluate educational technologies in higher education, focusing on virtual classrooms. Prior research informed the construction of the model, which contains perceived usefulness, perceived ease of use, behavioural intent, access and convenience, system attributes and self-efficacy. Education-specific constructs include cognitive engagement, feedback, instructor practice and class interaction and communication. Additionally, a new construct called comfort and well-being is introduced. A total of 427 valid responses on a 5-point Likert scale were received from university students. Exploratory factor analysis, confirmatory factor analysis and structural equation modelling were used to analyse the data. The model accounted for 78% of variance of behavioural intent, with comfort and well-being demonstrating the strongest influence. Cognitive engagement, access and convenience influenced perceived usefulness, and system attributes and self-efficacy influenced perceived ease of use. Feedback, instructor practice and class interaction and communication were not significant as educational constructs for this cohort. Based on this analysis, a final extended educational technology acceptance model (EETAM) is proposed for further use and testing.
期刊介绍:
BJET is a primary source for academics and professionals in the fields of digital educational and training technology throughout the world. The Journal is published by Wiley on behalf of The British Educational Research Association (BERA). It publishes theoretical perspectives, methodological developments and high quality empirical research that demonstrate whether and how applications of instructional/educational technology systems, networks, tools and resources lead to improvements in formal and non-formal education at all levels, from early years through to higher, technical and vocational education, professional development and corporate training.