Evaluation of online learning platforms based on probabilistic linguistic term sets with self-confidence multiple attribute group decision making method

Abstract

Online education addresses the limitations of traditional classrooms in terms of time and location. Moreover, online learning platforms provide learners with one-stop self-learning services. The organic combination of online self-learning, traditional classroom teaching and classroom experiment is a persistent task in realizing the development of diversified teaching modes in colleges and universities. Thus, selecting an appropriate online learning platform for students to improve ability and quality can be viewed as a complicated multiple attribute group decision making (MAGDM) problem. This study aims to introduce a new method for evaluation of online learning platforms based on MAGDM. First, the evaluation index system of online learning platforms is constructed. The index contains three dimensions and six indicators based on the ‘1 + 4′ multi-dimensional Cooperation-Acknowledgement-Modulation-Perseverance (CAMP) ability training systems. On the basis of assessment information using probabilistic linguistic term sets with self-confidence (PLTS-SC), we then introduce certain basic operational laws and aggregation operators for PLTS-SC. To aggregate individual evaluation information into a collective result, we further propose a weight determination method of experts that combines levels of external trust with levels of internal self-confidence. Afterward, we put forward an extended Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for MAGDM using the PLTS-SC data and apply it to the evaluation and selection of online learning platforms. Finally, the feasibility and effectiveness of the proposed method is verified by robustness test and comparative analysis with relative methods.