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Social Network Learning Analytics: identification of students at risk of early school leaving.

caterina bembich
2019
  • journal article

Periodico
GIORNALE ITALIANO DELLA RICERCA EDUCATIVA
Abstract
Social Network Analysis (SNA) is gaining increasing attention in educational research as branch of Learning Analytics. This contribution offers an overview of Social Network Analysis, describing the origins of this methodology, the implications of its use for educational research, and its application in the study of early school leaving. This paper presents a case study of the application of SNA in a group of students attending a vocational school. The study analyzes the external and internal relationships of the students in the school. The results highlight that students at risk of dropping out of school, tend to have less dense and less cohesive social networks, and exhibit a higher probability of establishing relationships with classmates with similar tendencies to leave school early. SNA draws attention to the relational structures that are established during school activities, and can help teachers and schools improve learning processes and learning environments more broadly. This study shows how a relational approach can be used to explore the phenomenon of early school leaving and highlights dysfunctional relational structures that accentuate the risk situation.
DOI
10.7346/SIRD-2S2019-P176
Archivio
http://hdl.handle.net/11368/2998935
https://ojs.pensamultimedia.it/index.php/sird/article/download/3453/3359/13711
Diritti
open access
license:copyright editore
FVG url
https://arts.units.it/bitstream/11368/2998935/1/IJEduR 2019.pdf
Soggetti
  • Social Network Analys...

  • Learning Analytic

  • Early school leaving

  • Interdependence

  • Identification of ris...

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