共查询到17条相似文献，搜索用时 140 毫秒
1.
大学生接受移动学习的影响因素实证分析 总被引：1，自引：0，他引：1
随着无线通信技术的飞速发展和移动设备的深入普及,移动学习作为一种新的学习方式逐渐进入大学生的校园生活。但实践中大学生对移动学习的应用情况与其对移动设备的热情并不匹配,大学生对移动学习的接受和运用不足现象严重。到底哪些因素影响着大学生接受移动学习？关于移动学习的采纳与接受,已有的研究主要采用技术接受模型（TAM模型）,试图探究影响一般社会群体接受移动学习的因素。TAM模型应用存在一定的局限性,拓展的技术整合和接受理论模型（UTAUT模型）在信息接受模型中具有更好的解释力。以UTAUT模型为基础,利用问卷调查和多元回归分析对北京师范大学学生的研究发现,成就价值、绩效期望、使用经验、感知娱乐性和社会影响等因素对大学生接受移动学习有显著性影响,因此应从加强校园宣传与环境建设、改进移动学习资源设计等方面,提升大学生对移动学习的接受水平。 相似文献
2.
3.
《洛阳师范学院学报》2017,(11)
在技术接受模型TAM基础上,增加感知娱乐性、资源优化性、学习经验、自我效能感四个变量,提出使用微信公众号学习专业知识的扩展技术接受模型,在江南大学开展抽样问卷调查和深度访谈,从SPSS相关性和多元线性回归分析发现,资源优化性对感知娱乐性、感知易用性、感知有用性、使用态度和行为意愿有显著影响;学习经验和自我效能感也是影响使用态度和行为意愿的因素.因此,可以从微信内容发布者对资源设计的优化、引导学生利用微信公众平台开展专业知识学习、组织专业相关研究性学习等方面,提升大学生对微信公众号的接受水平. 相似文献
4.
合理接受网络协作交流工具,能提高师生交互效果,促进协作效率。为探讨网络协作学习中影响学习者接受交流工具的主要因素,本文根据技术接受模型(TAM)理论,提出了基于TAM的协作交流工具接受行为模型,并对理论模型和研究假设进行了分析。研究结果表明,技术特征、任务特征和个体差异显著影响个体的感知有用性和感知易用性;学习环境显著影响个体的感知有用性和接受行为,感知有用性和感知易用性显著影响接受意愿,而接受意愿又对接受行为有显著影响。研究结果从实证角度对协作交流工具接受行为模型提供了支持,也为优化协作学习设计提供了依据。 相似文献
5.
移动计算技术与各类信息终端的进步,极大促进了基于“互联网+”的移动学习平台的发展。当前,纷繁的在线学习平台面临着用户不易接受、满意度和使用率低等诸多问题。基于信息系统技术接受和使用统一整合理论(UTUAT),结合移动情境和心流体验理论,针对当前用户群较为广泛的英语在线学习平台,通过问卷调查数据,对用户在线学习的情境和技术接受感知行为进行深入研究,构建了移动学习平台的用户使用行为模型。数据分析显示,心流体验、感知移动性和服务质量对学习用户的持续使用意愿均有显著影响,在线学习的内容质量通过心流体验对用户行为产生间接影响;感知成本因素方面,付费的移动学习平台对用户行为的影响比免费平台相对显著,但均未通过显著性检验。上述研究方法和结论,对各类移动学习平台的用户行为研究具有一定的参考价值。 相似文献
6.
本研究以技术接受模型(TAM)理论基础,结合具体调研情况,构建了一个由感知有用性、感知易用性、市民化倾向性、社群影响和使用意向组成的新生代农民工移动学习接受模型。实证研究通过对217份有效问卷样本进行结构方程模型分析,验证并讨论了该模型的各种预设研究假设。本研究表明:感知易用性、市民化倾向对使用意向均有积极的正向影响,其中感知易用性对使用意向影响最强;另外,市民化倾向对社群影响有正向影响,社群影响又对感知易用性有正向影响,感知易用性同时对使用意向和感知有用性有正向影响。 相似文献
7.
马如宇 《中国远程教育(综合版)》2009,(8)
随着3G移动通信技术时代的到来,预示着使用手机进行移动学习的用户将愈来愈多,有必要对影响用户进行移动学习的前置性和潜在性因素进行深入分析.文章在科技接受模型的基础上增加了感知移动性价值、感知娱乐性等因素,构建了针对移动学习的扩展式科技接受模型,对影响我国移动手机用户选择移动学习这种新型学习方式的关键件因素进行了实证性探究和解析. 相似文献
8.
随着3G移动通信技术时代的到来,预示着使用手机进行移动学习的用户将呈现愈来愈多的趋势,有必要对影响用户进行移动学习的前置性和潜在性因素进行深入分析.本文在科技接受模型的基础上增加了感知移动性价值、感知娱乐性等因素,构建了针对移动学习的扩展式科技接受模型,对影响我国移动手机用户选择移动学习这种新型学习方式的关键性因素进行了实证性探究和解析. 相似文献
9.
随着无线通信技术的高速发展和各式各样移动通信设备的普及应用,移动学习成为近年来教育技术学领域研究的热点。整体上,感知有用性、感知易用性和感知趣味性对大学生移动学习行为意愿有直接显著的影响。因此,教育工作者在开展移动学习活动和开发移动学习资源的过程中应当充分考虑移动通信设备的易用性、有用性与趣味性等潜在的影响因素,以提高大学生对移动学习的接受度和使用意愿。 相似文献
10.
基于技术接受模型TAM3构建大学生网络学习行为影响因素模型,来自四所高校的343份有效问卷数据分析表明,该模型各项指标均符合要求,整体拟合度较好。研究发现,大学生对网络学习的有用性感知和易用性感知均与大学生网络学习意愿呈显著正相关,易用性感知和工作绩效与有用性感知呈显著正相关,大学生计算机自我效能感、外部支持、感知娱乐性和客观使用与大学生网络学习易用性呈显著正相关。这给制定相关对策提供了一定的启示。 相似文献
11.
Mary Helen Fagan 《学校用计算机》2013,30(2):105-121
The purpose of this study was to explore the acceptance of mobile learning by students in a higher education setting. The unified theory of acceptance and use of technology (UTAUT) was extended to include hedonic motivation, operationalized as enjoyment, as well as social influence in a field study involving the adoption of iPad mobile devices. Survey data were collected from 171 college students and analyzed using partial least squares structural equation modeling (PLS-SEM). The results indicated that enjoyment and performance expectations were important factors influencing the acceptance of mobile learning in this context. For those engaged in the mobile learning pilot project upon which this study was based, the use of the UTAUT and the results provided a theory-based empirical approach to support an assessment that the pilot project goals were achieved. Overall, students perceived iPads to be useful and enjoyable tools for accomplishing educational tasks and improving learning outcomes. 相似文献
12.
Islamoglu Hakan Kabakci Yurdakul Isil Ursavas Omer Faruk 《Educational technology research and development : ETR & D》2021,69(2):1025-1054
The purpose of this study was to develop a mobile learning acceptance model for pre-service teachers and to examine the relationships among technology acceptance factors. The literature on mobile learning acceptance lacks studies on pre-service teachers and studies that include concrete mobile learning scenarios. To overcome these problems, we have developed and implemented a mobile-technology-enabled information technology course. The data collection and analysis were conducted in two separate studies. First, we developed a mobile learning acceptance scale and applied confirmatory factor analysis with 408 participants. The final instrument included 28 items measuring eight technology acceptance factors, namely behavioral intention, attitude towards use, perceived usefulness, perceived ease of use, social influence, facilitating conditions, self-efficacy, and anxiety. After this, we collected a new set of data from 316 participants to examine the relationships among the factors using structural equation modeling. In both studies, we investigated the respective models’ invariance across gender and discipline groups, and both models fulfilled invariance requirements. The results indicated that perceived ease of use and social influence have direct effects on behavioral intention, whereas self-efficacy has an indirect effect. Depending on the group, the explained variance of behavioral intention ranged between 18.1% and 60.6%.
相似文献13.
Sung Youl Park Min‐Woo Nam Seung‐Bong Cha 《British journal of educational technology : journal of the Council for Educational Technology》2012,43(4):592-605
As many Korean universities have recommended the implementation of mobile learning (m‐learning) for various reasons, the number of such tertiary learning opportunities has steadily grown. However, little research has investigated the factors affecting university students' adoption and use of m‐learning. A sample of 288 Konkuk university students participated in the research. The process by which students adopt m‐learning was explained using structural equation modeling technique and the Linear Structural Relationship (LISREL) program. The general structural model based on the technology acceptance model included m‐learning self‐efficacy, relevance for students' major (MR), system accessibility, subjective norm (SN), perceived usefulness, perceived ease of use, attitude (AT), and behavioral intention to use m‐learning. The study results confirmed the acceptability of the model to explain students' acceptance of m‐learning. M‐learning AT was the most important construct in explaining the causal process in the model, followed by students' MR and SN. 相似文献
14.
Shuiqing Yang Shasha Zhou Xiaoying Cheng 《British journal of educational technology : journal of the Council for Educational Technology》2019,50(2):626-637
Retaining learners and facilitating their continuance are critical for the mobile learning providers and educators. Drawing on stimulus-organism-response framework and self-determination theory, this study examines factors that affect college students' mobile learning continuance by considering the self-determination needs and learning involvement. A research model was developed and empirically tested against data collected from 309 college students who are the mobile learning users of an online education platform in China. The results of structural equation modeling analysis showed that perceived learning support, self-management of learning and peer influence significantly influence affective learning involvement, which in turn positively affect mobile learning continuance intention. In addition, perceived learning support and peer influence also significantly influence cognitive learning involvement, which further determine mobile learning continuance intention. Theoretical and practical implications of these findings are also discussed. 相似文献
15.
Investigating the determinants and age and gender differences in the acceptance of mobile learning 总被引：4，自引：0，他引：4
Yi-Shun Wang Ming-Cheng Wu Hsiu-Yuan Wang 《British journal of educational technology : journal of the Council for Educational Technology》2009,40(1):92-118
With the proliferation of mobile computing technology, mobile learning (m-learning) will play a vital role in the rapidly growing electronic learning market. M-learning is the delivery of learning to students anytime and anywhere through the use of wireless Internet and mobile devices. However, acceptance of m-learning by individuals is critical to the successful implementation of m-learning systems. Thus, there is a need to research the factors that affect user intention to use m-learning. Based on the unified theory of acceptance and use of technology (UTAUT), which integrates elements across eight models of information technology use, this study was to investigate the determinants of m-learning acceptance and to discover if there exist either age or gender differences in the acceptance of m-learning, or both. Data collected from 330 respondents in Taiwan were tested against the research model using the structural equation modelling approach. The results indicate that performance expectancy, effort expectancy, social influence, perceived playfulness, and self-management of learning were all significant determinants of behavioural intention to use m-learning. We also found that age differences moderate the effects of effort expectancy and social influence on m-learning use intention, and that gender differences moderate the effects of social influence and self-management of learning on m-learning use intention. These findings provide several important implications for m-learning acceptance, in terms of both research and practice. 相似文献
16.
David A. Sprenger Adrian Schwaninger 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(4):857-877
The technology acceptance model (TAM) uses perceived usefulness and perceived ease of use to predict the intention to use a technology which is important when deciding to invest in a technology. Its extension for e-learning (the general extended technology acceptance model for e-learning; GETAMEL) adds subjective norm to predict the intention to use. Technology acceptance is typically measured after the technology has been used for at least three months. This study aims to identify whether a minimal amount of exposure to the technology using video demonstrations is sufficient to predict the intention to use it three months later. In two studies—one using TAM and one using GETAMEL—we showed students of different cohorts (94 and 111 participants, respectively) video demonstrations of four digital technologies (classroom response system, classroom chat, e-lectures, mobile virtual reality). We then measured technology acceptance immediately after the demonstration and after three months of technology use. Using partial least squares modelling, we found that perceived usefulness significantly predicted the intention to use three months later. In GETAMEL, perceived usefulness significantly predicted the intention to use for three of the four learning technologies, while subjective norm only predicted the intention to use for mobile virtual reality. We conclude that video demonstrations can provide valuable insight for decision-makers and educators on whether students will use a technology before investing in it.
Practitioner notes
What is already known about this topic- The technology acceptance model helps decision-makers to determine whether students and teachers will adopt a new technology.
- Technology acceptance is typically measured after users have used the technology for three to twelve months.
- Perceived usefulness is a strong predictor of intention to use the technology.
- The predictive power of perceived ease of use for the intention to use varies from insignificant to strong.
- For the four digital learning technologies (classroom chat, classroom response system, e-lectures and mobile virtual reality), we measure technology acceptance after a video demonstration and again after three months of usage.
- Using structural equation modelling, we are able to predict intention to use after three months, with perceived usefulness measured after the video demonstration.
- We replicate these findings with a second study using the general extended technology acceptance model.
- Short video demonstrations can provide information for educators to predict whether students will use a technology.
- Early impressions of perceived usefulness are very important and valuable to predict whether students will use a technology.
17.
Perceptions of instructional quality: impact on acceptance and use of an online learning environment
ABSTRACTGiven the growing use of online learning environments in higher education, it is important to further unravel how students’ use is influenced by their perceptions towards these learning environments. This study includes the perceived quality of the instructional design based on the First Principles of Instruction of Merrill and students’ acceptance based on the constructs perceived usefulness and perceived ease of use of the technology acceptance model (TAM). The aim of this study is twofold: a first aim is to investigate the influence of the perceived instructional quality on students’ acceptance and the second aim is to investigate the impact of students’ acceptance and the perceived instructional quality on the quantity (i.e. course activity) and quality (i.e. course performance) of use. In this study, a Moodle-based online learning environment for learning French as a foreign language was studied. Participants were 161 university students. Structural equation modeling (SEM) indicates that the perceived instructional quality has a significant positive influence on students’ acceptance. Furthermore, students’ perceived instructional quality has a positive influence on the quality, but not on the quantity of use, whereas students’ acceptance of the online learning environment has no impact on the use of the learning environment. 相似文献