چارچوب تصمیم گیری یکپارچه برای ارزیابی و انتخاب محصول آموزش الکترونیکی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|17631||2011||9 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Applied Soft Computing, Volume 11, Issue 3, April 2011, Pages 2990–2998
A sound decision methodology for evaluating and selecting e-learning products should consider multiple and conflicting criteria and the interactions among them. In this paper, a decision framework which employs quality function deployment (QFD), fuzzy linear regression and optimization is presented for e-learning product selection. First, a methodology for determining the target values for e-learning product characteristics that maximize overall customer satisfaction is presented. The QFD framework is employed to allocate resources and to coordinate skills and functions based on customer needs. Differing from earlier QFD applications, the proposed methodology employs fuzzy regression to determine the parameters of functional relationships between customer needs and e-learning product characteristics, and among e-learning product characteristics themselves. Finally, the e-learning product alternatives are evaluated and ranked with respect to deviations from the target product characteristic values. The potential use of the proposed decision framework is illustrated through an application on e-learning products provided by the universities in Turkey.
In recent years, advances in information technology transformed the way of learning and teaching towards a learner-centered education  and . E-learning can be defined as an effective learning process created by combining digitally delivered content with learning support services. Worldwide, the e-learning market has a growth rate of 35.6% . In particular, educational institutions pursue the potential education and financial benefits that the e-learning products promise. In contrast to campus based learning, a learner is able to customize the e-learning products to a certain extent, since the products do not have any time and space constraint. However, each user has different expectations in terms of technical and social attributes of a product. Hence, it is a challenging task to select the most satisfying e-learning product in such an environment. Moreover combined with the globalization of the economies, which drastically changed the relationship between the customers and the product or service providers, the providers can no longer impose on the customers the products or services they are to use. Rather, the customer chooses the products or services that he/she requires. This trend can also be observed in the e-learning market. As a result, e-learning service providers have intended to introduce their own new product/service development and improvement mechanisms to assure the quality of their products and services. This outcome was unavoidable since they are part of a globally competitive market and they have to survive and maintain their market shares. One of the well-known strategic quality management tools that can provide a competitive advantage throughout this process is the quality function deployment (QFD), which simply intends to design products and services considering customers’ needs (CNs) to guarantee satisfaction. In this work, our main objective is to propose a mechanism to improve the e-learning products so that the customers’ satisfaction is ensured. We apply the proposed decision methodology to evaluate several e-learning applications in Turkey. We initially explore the essential criteria for a successful e-learning environment. These criteria establish the basis for a comprehensive model for measuring e-learner satisfaction. Then, the QFD framework to allocate resources and to coordinate skills and functions based on CNs are introduced. This methodology enables us to easily develop the appropriate services for the customers. It ignores aspects with little or no meaning to customer; giving more importance to aspects meaning a lot. The main contribution of this study over previous QFD applications in education is that the proposed methodology employs fuzzy regression analysis to determine the parameters of functional relationships between customer needs and product characteristics, and among product characteristics themselves. Fuzzy regression analysis used in this research addresses the problem of subjectivity and vagueness in determining the relationships between customer needs and product characteristics, and the dependencies among product characteristics. Since fuzzy regression is a viable alternative approach for finding the values for these relationships, it improves the applicability of QFD as a decision support tool for determining the target values for product characteristics to be considered while designing an e-learning program, and thus, providing a roadmap for e-learning product developers. Finally, a weighted distance-based measure is introduced for ranking e-learning product alternatives. The decision framework presented in this paper has advantages in comparison to the previously proposed analytical approaches for evaluation of educational products/programs such as statistical methods or analytic hierarchy process (AHP). Statistical techniques such as ordinary least squares are based on determining a meaningful statistical relationship between product characteristics that is difficult to obtain when the number of alternatives taken into consideration are relatively few. The AHP, which is a widely-used multi-attribute decision making technique, assumes that preferential independence of the product characteristics hold; however, this assumption generally does not hold in real-world applications. Data envelopment analysis (DEA) which is a nonparametric approach does not require the preferential independence assumption, while it is based on the assumption that every characteristic defined as output is related to every input. The remaining part of the paper is structured as follows: Section 2 presents the e-learning concept and a brief literature review on related works, and concludes with the development of evaluation criteria. Section 3 summarizes the main steps of the QFD along with related research. Section 4 provides the essentials for fuzzy linear regression. In Section 5, the decision methodology that combines QFD, fuzzy linear regression, optimization, and a distance-based measure is presented. In Section 6, the proposed framework is illustrated through an e-learning program evaluation study in Turkey. The last section presents concluding remarks driven from the case study.
نتیجه گیری انگلیسی
This paper contributes to the e-learning product selection literature by introducing a novel modeling framework that can integrate customer needs with e-learning product characteristics. Determining product characteristics of e-learning products that are aligned with customers’ expectations necessitates a comprehensive model, which makes the necessary trade-off between customers’ requirements considering the relationship between customer needs and product characteristics and the dependencies among product characteristics. QFD provides the required means for incorporating these relationships into the decision framework. Moreover, fuzzy regression analysis used in this research addresses the problem of subjectivity and vagueness in determining the parameters of functional relationships between customer needs and product characteristics, and among product characteristics themselves. Since fuzzy regression is a viable alternative approach for finding the values for these relationships without requiring the rigid assumptions of statistical regression, it improves the applicability of QFD. The proposed methodology can be considered as a sound alternative decision aid that can be used for e-learning product selection problems accounting for both quantitative and qualitative data concerning user demands and e-learning product characteristics, relationships between user demands and e-learning product characteristics, interactions among e-learning product characteristics, which are generally expressed in vague and imprecise manner. Yoon  addressed the selection of distance function for computing distances between alternatives, and introduced a credibility measurement of distance functions. A similar approach can be employed by considering a unified distance metric that incorporates the relative credibility of distance functions tabulated in . For our case, using a unified distance measure would not have a substantial effect on the ranking of the e-learning product alternatives since a very similar rank-order is obtained for both distance metrics with the only difference occurring in the last two positions. Nonetheless, a unified distance measure might prove to be useful for other cases where the rankings vary according to different distance metrics.