Evaluation of MOOCs based on multigranular unbalanced hesitant fuzzy linguistic MABAC method

被引：14
作者：
Rong, Lili [1 ]
Wang, Lei [2 ]
Liu, Peide [3 ]
Zhu, Baoying [3 ]
机构：
[1] Shandong Management Univ, Sch Business, Jinan 250357, Shandong, Peoples R China
[2] Shandong Univ Finance & Econ, Sch Int Educ, Jinan, Shandong, Peoples R China
[3] Shandong Univ Finance & Econ, Sch Management Sci & Engn, Jinan 250014, Shandong, Peoples R China
来源：
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS | 2021年 / 36卷 / 10期
基金：
中国国家自然科学基金;
关键词：
MABAC method; MOOCs evaluation; multigranular hesitant fuzzy linguistic term set; multiple attribute group decision-making; unbalanced linguistic term set; NUMERICAL SCALE; REPRESENTATION MODEL; SELECTION; OPERATORS; HIERARCHY; DESIGN;
D O I：
10.1002/int.22526
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：5670 / 5713
页数：44
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