Key Student Nodes Mining in the In-Class Social Network Based on Combined Weighted GRA-TOPSIS Method

Autor/inn/en	Shou, Zhaoyu; Tang, Mengxue; Wen, Hui; Liu, Jinghua; Mo, Jianwen; Zhang, Huibing
Titel	Key Student Nodes Mining in the In-Class Social Network Based on Combined Weighted GRA-TOPSIS Method
Quelle	In: International Journal of Information and Communication Technology Education, 19 (2023) 1, (19 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Shou, Zhaoyu)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1550-1876
Schlagwörter	Social Networks; Algorithms; Network Analysis; Databases; Evaluation Methods; Student Evaluation; Classroom Environment; Computation; Decision Making; Technical Support; Data Analysis + Suchen Sie Ihr Suchwort? Social network; Soziales Netzwerk; Algorithm; Algorithmus; Netzplantechnik; Datenbank; Schulnote; Studentische Bewertung; Klassenklima; Unterrichtsklima; Decision-making; Entscheidungsfindung; Auswertung
Abstract	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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01