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A hybrid approach to develop an analytical model for enhancing the service quality of e-learning

机译:开发分析模型以提高电子学习服务质量的混合方法

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摘要

The digital content industry is flourishing as a result of the rapid development of technology and the widespread use of computer networks. As has been reported, the market size of the global e-learning (i.e., distance education and telelearning) will reach USD 49.6 billion in 2014. However, to retain and/or increase the market share associated with e-learning, it is important to maintain or increase service quality in this sector. This research was intended to develop an analytical model for enhancing the service quality of e-learning using a hybrid approach from the perspective of customers. The evaluation methodology integrates the three methods: rough set theory (RST), quality function deployment (QFD), and grey relational analysis (GRA). First, important criteria affecting service quality (referred to as customer requirements (CRs)) and relevant technical information (referred to as technical requirements (TRs)) for e-learning are compiled from an extensive literature review. Using the data regarding customer satisfaction collected from a questionnaire survey, RST is then used to reduce the number of attributes considered and to determine the CRs. Furthermore, in consultation with domain experts, QFD is used together with GRA to analyze the interrelationships between CRs (which represent the voice of customer (VOC)) and TRs (which represent the voice of the engineer (VOE)) and to create an order of priority for the TRs given the CRs based on objective weighting using the entropy value. An illustrative example is provided-an empirical analysis of the students who participated in the e-learning program at a particular university. The results reveal that of the fourteen TRs, "Curriculum development" has the greatest effect on e-learning service quality, followed by "Evaluation", "Guidance and tracing", "Instructional design", and "Teaching materials". Both the CRs and the TRs may vary depending on the individual organization. Nevertheless, the proposed model can be a useful point of reference for e-learning service providers, helping them to identify the TRs that they can use to enhance service quality and to target vital CRs.
机译:由于技术的飞速发展和计算机网络的广泛使用,数字内容产业蓬勃发展。据报道,2014年全球电子学习(即远程教育和远程学习)的市场规模将达到496亿美元。然而,要保持和/或增加与电子学习相关的市场份额,这一点很重要保持或提高该部门的服务质量。这项研究旨在开发一种分析模型,以从客户的角度使用混合方法来提高电子学习的服务质量。评估方法集成了三种方法:粗糙集理论(RST),质量功能部署(QFD)和灰色关联分析(GRA)。首先,从广泛的文献综述中得出了影响服务质量的重要标准(称为客户要求(CR))和有关电子学习的相关技术信息(称为技术要求(TR))。然后,使用从问卷调查中收集的有关客户满意度的数据,使用RST来减少考虑的属性数量并确定CR。此外,在与领域专家协商后,QFD与GRA一起用于分析CR(代表客户的声音(VOC))和TR(代表工程师的声音(VOE))之间的相互关系,并创建订单给定CR的TR的优先级基于使用熵值的客观加权。提供了一个说明性示例-对特定大学中参加电子学习计划的学生的经验分析。结果显示,在这14个TR中,“课程开发”对电子学习服务质量的影响最大,其次是“评估”,“指导和跟踪”,“教学设计”和“教材”。 CR和TR可能会因各个组织而异。尽管如此,所提出的模型对于电子学习服务提供商还是有用的参考点,可以帮助他们确定可用于提高服务质量和确定关键CR的TR。

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