Quality Evaluation of Integrated Distance Course for Special Education Based on Group Decision-Making Algorithm.
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| Title: | Quality Evaluation of Integrated Distance Course for Special Education Based on Group Decision-Making Algorithm. |
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| Authors: | Wang, Wei1,2 (AUTHOR), Wang, Zhichao1 (AUTHOR) wangzc340@nenu.edu.cn, Wang, Jianfei3 (AUTHOR) |
| Source: | Mobile Networks & Applications. Dec2025, Vol. 30 Issue 5/6, p1050-1063. 14p. |
| Subjects: | Group decision making, Course evaluation (Education), K-means clustering, TOPSIS method, Learning analytics, Educational quality, Distance education, Special education |
| Abstract: | Due to the significant differences among normal and disabled students, the classical evaluation method leads to a lack of consensus and balance in the course quality evaluation, resulting to lower quality and accuracy. To solve this problem, this paper proposes a group decision-making algorithm for the quality evaluation of integrated distance courses. Firstly, the proposed algorithm uses the K-means algorithm to cluster and collect the learning behavior data of students into predefined K clusters based on feature similarity. Secondly, the proposed algorithm constructs evaluation indicators of course quality from multiple perspectives under the support of multiple evaluation principles. Finally, all personal preferences of each decision-maker are brought together into one collective preference, which is used to rank the decision-making algorithms. In this paper, students are regarded as decision-makers, the integrated courses are regarded as decision-making options, the learning behaviors and preferences of students are regarded as attribute decision variables. Thus, the quality evaluation of integrated distance courses can be regarded as a group decision-making process. Experimental results show that the proposed method performs well in both consistency ratio and comprehensive score. The consistency ratio of our method always remains above 0.9, and the TOPSIS comprehensive score reaches a maximum of 0.937. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Due to the significant differences among normal and disabled students, the classical evaluation method leads to a lack of consensus and balance in the course quality evaluation, resulting to lower quality and accuracy. To solve this problem, this paper proposes a group decision-making algorithm for the quality evaluation of integrated distance courses. Firstly, the proposed algorithm uses the K-means algorithm to cluster and collect the learning behavior data of students into predefined K clusters based on feature similarity. Secondly, the proposed algorithm constructs evaluation indicators of course quality from multiple perspectives under the support of multiple evaluation principles. Finally, all personal preferences of each decision-maker are brought together into one collective preference, which is used to rank the decision-making algorithms. In this paper, students are regarded as decision-makers, the integrated courses are regarded as decision-making options, the learning behaviors and preferences of students are regarded as attribute decision variables. Thus, the quality evaluation of integrated distance courses can be regarded as a group decision-making process. Experimental results show that the proposed method performs well in both consistency ratio and comprehensive score. The consistency ratio of our method always remains above 0.9, and the TOPSIS comprehensive score reaches a maximum of 0.937. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 1383469X |
| DOI: | 10.1007/s11036-024-02428-3 |
