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Educational Research and Evaluation
An International Journal on Theory and Practice
Volume 22, 2016 - Issue 1-2
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Articles

The role of social capital in students’ perceptions of progress in higher education

Pages 65-85 | Received 19 Dec 2014, Accepted 17 May 2016, Published online: 10 Jun 2016
 

ABSTRACT

This paper aims to analyse the effects of students’ social relationships at university on students’ success. Specifically, whether a student with heterogeneous relationships obtains better academic results than a student whose relationships are mostly with classmates. Further, the research examines whether students’ social relationships make up for their parents’ lack of human capital. A survey was answered by a sample of 867 students from universities in the metropolitan region of Barcelona. The findings suggest that the effects of social relationships are not always positive. The benefits of social networks at university interact with the type of degree studied, the student’s dedication to studies, and the student’s social class. Perceptions of progress for each category of students is different: It is more positive for those with frequent heterogeneous relations, who do not work or do so for a few hours, and for those who are enrolled in “hard” science courses.

Acknowledgements

This piece of research is part of the “National Plan for Scientific Research, Technological Development and Research” (CSO2008-02812) funded by the Spanish Ministry of Science and Innovation, which is entitled “Students in Front of New University Reform”. The authors are members of the GRET, the Education and Employment Research Group at the Universitat Autònoma de Barcelona. The written version of this article has been re-drafted and discussed with the rest of the GRET members taking part in this research: Marina Elias, Josep Maria Masjuan, Albert Sanchez-Gelabert, and Helena Troinao.

Notes on contributor

Lidia Daza is a postdoctoral fellow at University of Barcelona, Spain. She holds a PhD in Sociology (University of Barcelona, Spain). Her research areas and interests are inequalities among higher education students in terms of access, academic performance, and joining the labour market. In 2014, she was awarded a 3-month fellowship from the Consortium for Advanced Studies in Barcelona for advanced training at Stanford Graduate School of Education, studying “Social Composition and Democratization in Higher Education”. Since 2009, she has been a member of the research group on Education and Work (GRET) at the Autonomous University of Barcelona.

Notes

1. College is a term used in English-speaking countries to refer to higher education institutions, although there are some differences in its use between the United Kingdom and the United States. This research refers to the American context, where college is a term specifically used for institutions that offer both bachelor and postgraduate studies.

2. In order to understand the different analytical models, see Pascarella and Terenzini (Citation2005).

4. Coleman (Citation1988) defines human capital in terms of educational level, for example, defined, among other variables, as years of schooling (as opposed to dropout) or academic achievement.

5. The research used two degrees from each area of knowledge along the “hard-soft” and “pure-applied” lines (Becher & Trowler, Citation2001): Architecture, Telecommunications Engineering, Chemistry, Biology, Social Education, Business Studies, Pharmacy, Nursing, Translation and Interpretation, and Humanities.

6. Due to the large amount of information contained in this analysis, specific data are excluded in this paper.

7. The variable was dichotomized because one of the three categories had a very low number of cases.

8. This variable was determined from the classification by Goldthorpe (Citation1995) and was taken as a proxy variable for family’s human capital (intermediate variable in the Coleman model [Citation1988]).

9. Due to the small samples for each social class, it was considered unwise to perform an explanatory analysis controlling for other variables.

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