22 SES 16 C, Stratification and Participation in Higher Education
Participation in European Higher Education (HE) has expanded (e.g. Chowdry et al., 2013; OECD, 2017, Reimer & Pollak, 2010; Vignoles & Murray, 2016). Not only HE enrollment, but also the share of people graduating from HE has increased substantially (e.g. Eurostat, 2017). This, however, does not necessarily imply a democratization of HE (Groenez, 2010; Reimer & Pollak, 2010). As indicated by Quinn (2013), the democratization of HE should involve a widening of participation and not only massification, or the uptake of HE by a larger share of people. Widened participation should reflect the diversity present in the population in terms of equal access, representation and study success in HE. Monitoring and promotion of student diversity is, however, limited and therefore one of the key priorities of the European Union's Europe 2020 goals (European Commission/EACEA/Eurydice, 2015).
Empirical evidence shows that democratization in HE is not yet accomplished: inequality is manifested both horizontally (e.g., subject choice) and vertically (e.g., enrollment rates, performance and completion) (OECD, 2017; Quinn, 2013; Reimer & Pollak, 2010; Riddell & Weedon, 2014). These educational inequalities are related to different student background characteristics such as social and ethnic background and gender (Chowdry et al., 2013; Groenez, 2010; OECD, 2017; Quinn, 2013; Riddell & Weedon, 2014; Vignoles & Murray, 2016) and result in inequities in life after HE, such as occupational segregation and returns such as earnings, occupational status and health (Wößmann, 2008). Research indicates that female students tend to enroll for relatively lower levels of HE and less rewarding (academic) fields of study such as education and humanities (e.g. Reimer & Pollak, 2010). Male students, in turn, are less likely to graduate than female students (e.g. Alon & Gelbgiser, 2011). Students from a more disadvantaged social background – e.g. lower parental education level and/or occupational status – are less likely to enroll in higher levels of HE (e.g. Reimer & Pollak, 2010), more likely to enroll in less rewarding fields of study (e.g., Vergolini & Vlach, 2017) and less likely to graduate (e.g., Giani, 2015). Lastly, ethnic minority status coincides with lower social status as the latter mostly explains the choice for lower levels of HE, lower likelihood of degree completion, and choice of less rewarding fields of study (Rodgers, 2013).
Thus, horizontal and vertical stratification both operate in HE (Reimer & Pollak, 2010). However, given the horizontal stratification of students across field of study, it is unclear whether student background relates differently with study success in these different fields of study. A study of Glorieux and colleagues (2015) indicates that student background intersects with program characteristics such as program size and gender composition (Glorieux, Laurijssen, & Sobczyk, 2015). The focus of the research of Glorieux et al. (2015) was on program characteristics, but did not investigate fields of study. Nonetheless, these program characteristics could provide a basis for understanding performance differences in the fields of study. The main goal of our study is twofold. First, we investigate horizontal stratification in HE in Flanders (Belgium) by looking at the background characteristics of students entering different fields of study. Second, we examine the interplay of horizontal and vertical stratification by investigating how student background characteristics are differentially associated to study success in different fields of study.
The following research questions are central:
RQ1: To what extent do fields of study differ in terms of student background composition in Flanders (Belgium)?
RQ2: To what extent does student background associate differently with first-year study success in different fields of study?
Our analyses are based on the Flemish Higher Education database, an administrative database that tracks students' progress through HE. The analyses are focused on the 2008-2011 first-entry students who graduated from Flemish secondary education (SE) and enrolled for an academic bachelor program in one of the five main Flemish universities. The research population consists of 69916 students. Most students are female (52%), have a mother with a tertiary education degree (61%), speak mostly Dutch at home (95%), have an academically oriented SE degree (89%) and did not receive government financial support (76%). In a first stage of the analyses, we focus on differences between the type of students attracted by the different study fields in universities. In order to increase international comparability, enrollments in academic bachelor programs were reclassified according to the ISCED2013 Fields of Education (UNESCO Institute for Statistics, 2014). The analysis itself is descriptive in nature, comparing the proportion of students in the different fields of education to the average proportion of students in universities. The second stage of the analysis consists of a stepwise construction of a linear regression model with the relative number of credits obtained in the first year of enrollment as the indicator of study success. Model 1 adds student background characteristics as predictors: gender (reference: female), mother's education (reference: higher education), language background (reference: mostly native tongue Dutch), SE degree (reference: academically oriented) and financial support (reference: no support). Results are further controlled for enrolment intensity, i.e. the number of credits enrolled for in the first year (reference: fulltime enrollment, 60 credits). Model 2 adds the field of education students enrolled in (reference: natural sciences, mathematics and statistics). In Model 3, we add interactions of student background and field of education to gain more insight in how different type of students perform in the different fields of education. Finally, in Model 4 we add program characteristics to evaluate their effect on the interaction effects: program size (the number of students enrolled in the field of education by year and institution), gender composition (the percentage of female students in the field of education), social composition (the percentage of students with a mother with no secondary education degree), language composition (the percentage of students with a foreign language background), secondary education composition (the percentage of students with a non-academically oriented secondary education degree), economic background composition (the percentage of students who received financial support).
Descriptive results indicate that all fields of study are horizontally stratified by gender. Fields that tend to be female-dominated are Education (94%), Social sciences, journalism and information (70%), Agriculture, forestry, fisheries and veterinary (69%), Arts and humanities (64%), and Health and welfare (63%). ICT (94%), Engineering, manufacturing and construction (73%), and Natural sciences, mathematics and statistics (66%) tend to be male-dominated. Only Business, administration and law appears to be rather balanced (47% female students). Horizontal stratification by language background is observed in Education (2%) and Agriculture (2%): both attract few students with a foreign language background. Finally, non-academically trained students are more likely to choose Engineering (22%), Agriculture (14%), and Arts (13%) and less likely to choose Education (3%) and Business (6%). Thus, fields of study differ in terms of student background characteristics (gender, language background, SE degree) (RQ1). Regression results show that students who are male, with foreign language background, who are non-academically prepared, with financial support and whose mothers do not hold a HE degree, are less successful in the first year (Model 1). Model 2 results indicate that only Education and Engineering students have a higher first-year study success than Natural sciences students. Interaction effects (Model 3) indicate that particularly gender interacts with field of study with male students underperforming in the fields of Education, Arts, Social sciences, and Engineering. Model 4 shows that program characteristic (size, gender, language and SE composition) do not explain the differential study success. Thus, controlled for structural program characteristics, student background relates differently with study success in different fields of study (RQ2). Our findings confirm horizontal and vertical stratification in HE based on student characteristics which translates into inequities in life after HE. Attention to diverse student needs within different fields of study is vital in realizing a democratization of HE.
Alon, S., & Gelbgiser, D. (2011). The Female advantage in college academic achievements and horizontal sex segregation, Social Science Research, 40, 107-119. Chowdry, H., Crawford, C., Dearden, L., Goodman, A., Vignoles, A. (2013). Widening participation in higher education: analysis using linked administrative data. Journal of the Royal Statistical Society, 176, part 2, 431-457. European Commission/EACEA/Eurydice. (2015). The European Higher Education Area in 2015: Bologna Process Implementation Report. Luxembourg: Publications Office of the EU. Eurostat. (2017). Ever greater share of persons aged 40 to 34 with tertiary educational attainment in the EU. Giani, M. S. (2015). The Postsecondary Resource Trinity Model: Exploring the Interaction Between Socioeconomic, Academic, and Institutional Resources. Research in Higher Education, 56(2), 105–126. Glorieux, I., I. Laurijssen, and O. Sobczyk. (2015). Study succes in first-year higher education in Flanders: an analysis of the impact of student and programme characteristics [Studiesucces in het eerste jaar Hoger Onderwijs in Vlaanderen: Een analyse van de impact van kenmerken van studenten en van opleidingen]. Leuven: Steunpunt Studie- en Schoolloopbanen. Groenez, S. (2010). Expansion and democratization of education in Flanders [Onderwijsexpansie en -democratisering in Vlaanderen]. Tijdschrift voor Sociologie, 3(4), 199-238. OECD. (2017). Education at a Glance 2017: OECD Indicators. Paris: OECD Publishing. Quinn, J. (2013). Drop-out and Completion in Higher Education in Europe: Among students from under-represented groups. Report for the European Commission by the Network of Experts in Social aspects of Education and Training. Reimer, D., & Pollak, R. (2009). Educational Expansion and Its Consequences for Vertical and Horizontal Inequalities in Access to Higher Education in West Germany. European Sociological Review, 26(4), 415-430. Riddell, S., & Weedon, E. (2014). European Higher education, the inclusion of students from under-represented groups and the Bologna Process. International Journal of Lifelong Education, 33(1), 26-44. Rodgers, T. (2013). Should high non-completion rates amongst ethnic minority students be seen as an ethnicity issue? Evidence from a case study of a student cohort from a British University. Higher Education, 66(5), 535–550. UNESCO Institute for Statistics (2014). ISCED Fields of Education and Training 2013 (ISCED-F 2013). Montreal: UNESCO Institute for Statistics. Vergolini, L., & Vlach, E. (2017). Family background and educational path of Italian graduates. Higher Education, 73(2), 245–259. Vignoles, A., & Murrey, N. (2016). Widening Participation in Higher Education. Educational Sciences, 6(2), 1-4. Wößmann, L. (2008). Efficiency and Equity of European Education and Training Policies. International Tax and Public Finance, 15(2), 199–230.
00. Central Events (Keynotes, EERA-Panel, EERJ Round Table, Invited Sessions)
Network 1. Continuing Professional Development: Learning for Individuals, Leaders, and Organisations
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Network 3. Curriculum Innovation
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Network 5. Children and Youth at Risk and Urban Education
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