Key Student Nodes Mining in the In-Class Social Network Based on Combined Weighted GRA-TOPSIS Method
In this paper, a key node mining algorithm of entropy-CRITIC combined weighted GRA-TOPSIS method is proposed, which is based on the network structure features. First, the method obtained multi-dimensional data of students' identities, seating relationships, social relationships, and so on to bu...
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| Veröffentlicht in: | International journal of information and communication technology education 2023-01, Vol.19 (1), p.1-19 |
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| creator | Shou, Zhaoyu Tang, Mengxue Wen, Hui Liu, Jinghua Mo, Jianwen Zhang, Huibing |
| description | In this paper, a key node mining algorithm of entropy-CRITIC combined weighted GRA-TOPSIS method is proposed, which is based on the network structure features. First, the method obtained multi-dimensional data of students' identities, seating relationships, social relationships, and so on to build a database. Then, the seating similarity among students was used to construct the in-class social networks and analyze the structural characteristics of them. Finally, the CRITIC and entropy weight method was introduced for obtaining the combined weight values and the GRA-TOPSIS multi-decision fusion algorithm to mine the key student nodes that have negative impact. The experiments showed that the algorithm of this paper can evaluate students objectively based on their classroom social networks, providing technical support for process-oriented comprehensive quality education evaluation. |
| doi_str_mv | 10.4018/IJICTE.322773 |
| format | Article |
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First, the method obtained multi-dimensional data of students' identities, seating relationships, social relationships, and so on to build a database. Then, the seating similarity among students was used to construct the in-class social networks and analyze the structural characteristics of them. Finally, the CRITIC and entropy weight method was introduced for obtaining the combined weight values and the GRA-TOPSIS multi-decision fusion algorithm to mine the key student nodes that have negative impact. The experiments showed that the algorithm of this paper can evaluate students objectively based on their classroom social networks, providing technical support for process-oriented comprehensive quality education evaluation.</description><identifier>ISSN: 1550-1876</identifier><identifier>EISSN: 1550-1337</identifier><identifier>DOI: 10.4018/IJICTE.322773</identifier><language>eng</language><publisher>Hershey: IGI Global</publisher><subject>Algorithms ; Analysis ; Classroom Environment ; Computation ; Data Analysis ; Decision Making ; Educational evaluation ; Entropy (statistics) ; Evaluation Methods ; Methods ; Mineral industry ; Mining industry ; Multidimensional data ; Network Analysis ; Nodes ; Social Networks ; Student Evaluation ; Students ; Technical services ; Technical Support ; Weighting methods</subject><ispartof>International journal of information and communication technology education, 2023-01, Vol.19 (1), p.1-19</ispartof><rights>COPYRIGHT 2023 IGI Global</rights><rights>2023. 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| ispartof | International journal of information and communication technology education, 2023-01, Vol.19 (1), p.1-19 |
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| subjects | Algorithms Analysis Classroom Environment Computation Data Analysis Decision Making Educational evaluation Entropy (statistics) Evaluation Methods Methods Mineral industry Mining industry Multidimensional data Network Analysis Nodes Social Networks Student Evaluation Students Technical services Technical Support Weighting methods |
| title | Key Student Nodes Mining in the In-Class Social Network Based on Combined Weighted GRA-TOPSIS Method |
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