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Massive open online courses (MOOCs) are very popular in China, and it is very important to evaluate and improve them. In this paper, a new evaluation method for MOOCs based on multi‐attribute group decision‐making is proposed. First, an evaluation index system of MOOCs is constructed, which contains six elements and 16 indicators, and multigranular unbalanced hesitant fuzzy linguistic term set (MGUHFLTS) is adopted to describe these indicators. Then based on MGUHFLTS, the aggregation operators are developed, including the multigranularity unbalanced hesitant fuzzy linguistic weighted averaging operator and the multigranularity unbalanced hesitant fuzzy linguistic order weighted averaging operator, moreover, a novel multi‐attributive border approximation area comparison model based on MGUHFLTS is proposed. This model is testified validity and superiority by comparison with other three methods and is applied in evaluation of MOOCs. After ranking five MOOCs, each indicator is analyzed to show how they influenced each element and suggestions are given. [ABSTRACT FROM AUTHOR]

T-spherical fuzzy numbers (FNs), which add an abstinence degree based on membership and non-membership degrees, can express neutral information conveniently and have a considerable large range of information expression. The normal FNs (NFNs) are very available to characterize normal distribution phenomenon widely existing in social life. In this paper, we first define the normal T-SFNs (NT-SFNs) which can combine the advantages of T-SFNs and NFNs. Then, we define their operational laws, score value, and accuracy value. By considering the interrelationship among multi-input parameters, we propose the Maclaurin symmetric mean operator with NT-SFNs (NT-SFMSM) and its weighted form (NT-SFWMSM). Furthermore, we study some characteristics and special cases of them. Based on the NT-SFWMSM operator, we put forward a novel multi-attribute decision-making (MADM) approach. Finally, some numerical examples are conducted to prove that the proposed approach is valid and superior to some other existing methods. [ABSTRACT FROM AUTHOR]

As the development and extension of hesitant fuzzy linguistic term sets (HFLTSs), free double hierarchy hesitant fuzzy linguistic term sets (FDHHFLTSs) can describe the evaluation information in more detail. In addition, in practical multiple attribute decision-making (MADM) problems, priority relations and interaction relations usually exist among attributes, and the prioritized interactive Choquet (PIC) operator based on generalized prioritized measure-guided aggregation (GPMGA) operator and Choquet integral (CI) operator is an effective tool for dealing with decision-making problems with priority relations and interaction relations. Under the above contexts, we improve the PIC operator and propose the FDHHFLPIC operator which can aggregate the evaluation information under free double hesitant fuzzy linguistic environment and take both priority and interaction relations into account. Furthermore, we propose an approach based on the FDHHFLPIC operators to address the real MADM problem, and the proposed method considering the different relations between attributes in decision-making process. Finally, two numerical examples are given to demonstrate the effectiveness and advantages of the proposed method which can consider priority relations and interaction relations under the FDHHFLTSs. [ABSTRACT FROM AUTHOR]