Evaluation of e-Learning Systems in Cloud Environments using DEA and Fuzzy Data Set

E-learning systems have emerged as popular training and educational tools in academic as well as industrial institutions. Subsequently, evaluation becomes a key issue in investments to e-learning practice. While most investigations have focused on system features and outcomes, we are interested in the efficiency of implementing e-learning systems in the Internet and cloud environments. In contrast to previous studies that adopt results-oriented approaches, we examine the relative efficiency of the system usage by means of data envelopment analysis (DEA). Usually the data set for input and output factors in DEA are assumed to be crisp. However, when uncertainties are involved in input or output estimation, an enhanced DEA model will be necessary for evaluating the performance. This study develops an efficient formulation for DEA with fuzzy data set. Empirical investigations are presented to demonstrate the feasibility for applying the proposed method.