بررسی ضوابط و اثربخشی از راه دور آموزش الکترونیکی با روابط اولویت فازی سازگار

The electronic learning (e-learning) has gradually become more and more important in today’s school in Taiwan. Many colleges and universities offer distance e-learning courses or programs for students. An effective teaching or learning through a distance web e-learning system depends on many factors (or criteria). The analytic hierarchy process (AHP) model is suitable for dealing with the multi-criteria problems. This paper utilizes the consistent fuzzy preference relations (CFPR) in AHP model to evaluate these factors. The CFPR is computational simplicity and guarantees the consistence of decision matrices. Rating the criteria is important. An empirical example using CFPR in AHP model to find the weights is presented. The weight can point out which factor is important, especially when the time, manpower, and financial support are limited. The rating results can be directly used to evaluate the distance e-learning effectiveness and can provide teachers and decision-makers in schools important information for improving e-learning practice in the future.

Along with the advancement of information technology, the electronic learning (e-learning) has played an important role in teaching and learning, which has become more and more popular not only in different levels of schools but also in various commercial or industrial companies in Taiwan. Why e-learning is so popular and become more and more important nowadays? One of the essential factors is that e-learning provides expediency for learners to study courses or learn professional knowledge without the constraint of time and space, especially in an asynchronous distance e-learning system. The other important factor is that e-learning may save internal training cost for some enterprises organizations in a long-term strategy. Also, the e-learning can be used as an alternative self-training for assisting or improving the traditional classroom teaching. Therefore many schools and businesses in Taiwan invest manpower and money in e-learning to enhance their hardware facilities and software contents. As a result various e-learning materials in different scientific areas were produced by teachers, multimedia material designers, or consultant companies. Many schools offer a variety of e-learning courses to students. In addition, the e-learning program has also become a strategy to recruit new students in night schools for some private universities or colleges. Weather or not the e-learning indeed reaches the goal of teaching or learning relies on many factors (e.g. Govindasamy, 2002, Ong et al., 2004, Selim, 2007, Sun et al., 2008, Tzeng et al., 2007, Wang, 2003 and Wang et al., 2007). For example: Wang (2003) developed a general instrument for measuring student satisfaction in an e-learning system, in which a 17-item instrument was summarized purified from a 24-item instrument (with 2 global items excluded). This 17-item was grouped into four main categories: (1) learner interface, (2) learning community, (3) content, and (4) personalization. Wang’s assessment was used in an asynchronous electronic learning system for commercial companies. Tzeng et al. (2007) also summarized a total of 58 criteria grouped into nine major factors for an empirical e-learning program used in commercial companies and the nine factors were (1) personal characteristics and system instruction, (2) participant motivation and system interaction, (3) range of instruction materials and accuracy, (4) webpage design and display of instruction materials, (5) e-learning environment, (6) webpage connection, (7) course quality and work influence, (8) learning records, and (9) instruction materials. Most researchers use statistical methods or linear models to determine and evaluate the affecting factors and e-learning effectiveness. Fuzzy set theory has been developed for decades since Zadeh (1965) and was widely used in various scientific fields. Tzeng et al. (2007) developed a hybrid multi-criteria decision making (MCDM) to evaluate the relations and weights of the factors. In MCDM, the factor analysis, decision making trial and evaluation laboratory (DEMATEL), fuzzy integral, and analytic hierarchy process (AHP) (Saaty, 1980) were employed for factor analysis and e-learning evaluation. The entire hybrid MCDM procedure seems a little complicated in computation. Evaluation of an e-learning effectiveness primarily contains four portions: (1) determination of the affecting factors, (2) questionnaire collection and statistical analysis, (3) weighting these factors, and (4) evaluation of the entire performance according to these weighted factors. For the first portion (determination of the affecting factors), these factors have been studied by many researchers and they are generally similar with some differences based on the different requirements of e-learning practice in different organizations or universities. Therefore, for (1) determination of the affecting factors, authors of this paper suggest that either use those methods presented by previous researchers to obtain the affecting factors or directly and carefully use those factors with some modifications according to the real practice in his/her organization or university. After the factors determined and questionnaires collected, the weights of these factors can be computed by using any suitable method. Finally, the entire e-learning effectiveness can be evaluated according to these weights. Weighting the factors is important. This study mainly concentrates on the third portion, i.e., the methodology of the weight-rating for each affecting factor. The practices in each organization or university can be different. In addition, frequently, the time, cost, manpower, software, hardware, and etc. can not be satisfied totally. In such case, weighting the factors of an e-learning and a fast easy weighting and evaluation method is crucial because it can be quickly and objectively shown the priority of each factor. The rating results of the distance e-learning can provide significant information to teachers and decision-maker in schools for improving e-learning programs in the future practice.

This paper presents a method to evaluate the factors (or criteria) in a distance e-learning system which includes synchronous and asynchronous learning. This selected method is the consistent fuzzy preference relations (CFPR). Using CFPR in AHP structures can easily establish the multi-criteria decision-making matrices and successfully rate the weight of each criterion of the distance web e-learning systems. According to these weights, the e-learning performance can be directly evaluated with the scores given by the evaluator to the main criteria and computed by using the expected value. The procedure of computation is easy and simple. The rating result can provide important information to teachers and decision-makers of schools for distance e-learning in the future practice.