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

Authors

  • Caterina Bembich UNIVERSTY OF TRIESTE

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.

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Published

2019-10-16

How to Cite

Bembich, C. (2019). Social Network Learning Analytics: identification of students at risk of early school leaving. ITALIAN JOURNAL OF EDUCATIONAL RESEARCH, 174–186. Retrieved from https://ojs.pensamultimedia.it/index.php/sird/article/view/3453

Issue

Special Issue

Section

Papers

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