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] |
| Copyright of Mobile Networks & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 194576081 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Quality Evaluation of Integrated Distance Course for Special Education Based on Group Decision-Making Algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Wei%22">Wang, Wei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Zhichao%22">Wang, Zhichao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wangzc340@nenu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Jianfei%22">Wang, Jianfei</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Mobile+Networks+%26+Applications%22">Mobile Networks & Applications</searchLink>. Dec2025, Vol. 30 Issue 5/6, p1050-1063. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Group+decision+making%22">Group decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Course+evaluation+%28Education%29%22">Course evaluation (Education)</searchLink><br /><searchLink fieldCode="DE" term="%22K-means+clustering%22">K-means clustering</searchLink><br /><searchLink fieldCode="DE" term="%22TOPSIS+method%22">TOPSIS method</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+analytics%22">Learning analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+quality%22">Educational quality</searchLink><br /><searchLink fieldCode="DE" term="%22Distance+education%22">Distance education</searchLink><br /><searchLink fieldCode="DE" term="%22Special+education%22">Special education</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Mobile Networks & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11036-024-02428-3 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1050 Subjects: – SubjectFull: Group decision making Type: general – SubjectFull: Course evaluation (Education) Type: general – SubjectFull: K-means clustering Type: general – SubjectFull: TOPSIS method Type: general – SubjectFull: Learning analytics Type: general – SubjectFull: Educational quality Type: general – SubjectFull: Distance education Type: general – SubjectFull: Special education Type: general Titles: – TitleFull: Quality Evaluation of Integrated Distance Course for Special Education Based on Group Decision-Making Algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Wei – PersonEntity: Name: NameFull: Wang, Zhichao – PersonEntity: Name: NameFull: Wang, Jianfei IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 1383469X Numbering: – Type: volume Value: 30 – Type: issue Value: 5/6 Titles: – TitleFull: Mobile Networks & Applications Type: main |
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