22 SES 02 C, Paper Session
Major reforms on the European level initiated by the Bologna declaration in 1999 led to significant changes in German higher education. Most notably, the introduction of a sequential degree structure, consisting of a first cycle degree (bachelor) and second cycle degree (master), led to a harmonized degree type structure. Within the scope of these reforms, degree programmes at the two main German higher education institutions – the universities and the universities of applied sciences (Fachhochschulen) – have converged in many aspects by both adapting to the new degree types and programme structures and partially aligning programme durations. However, despite these tendencies towards conversion along many aspects of degree programme setup, the two institutions still attract different types of students: students at the universities of applied sciences often dispose of alternative access qualifications and enter higher education more often via non-traditional educational routes. Furthermore, student numbers in German higher education have increased tremendously during the past decades partially because higher education has formally opened to formerly excluded social groups, especially to students not following the traditional route via the Gymnasium (upper secondary schooling). Thus, the student population has become more heterogeneous as enrolment rates of students from all social strata have increased (Shavit et al, 2007; Wolter, 2015).
Against this background, it is of interest if and in how far the trajectories of universities of applied sciences students differ from those of university students. While these two higher education institution types still attract different student populations, they have become more alike in the organization of their degree programmes. Thus, it remains unclear whether this also included an alignment in students’ trajectories. Consequently, this study aims at comparing students’ trajectories at these two higher education types based on a bachelor student cohort that enrolled in higher education after the Bologna reforms.
The international research context already recognized that students’ higher education trajectories deserve further investigation (Haas & Hadjar, 2019). With an increasing number of non-traditional students accessing higher education through alternative routes and diversifying patterns of study-work or study-family arrangements, students’ trajectories through higher education have also become more complex. In the US, contemporary enrolment patterns are characterized by temporary interruptions, institutional transfers, delayed degree completion, low-intensity or multi-institutional enrolment (Adelman, 2005; Goldrick-Rab, 2006). However, while the flexible organization of higher education is generally appreciated – catering for the diversity of today’s students’ living situations – several studies found that such non-traditional enrolment patterns and trajectories through higher education come at a higher risk of degree non-completion (Milesi, 2010).
Yet, despite an ongoing higher education policy discourse on how to decrease long study durations and dropout, our knowledge on students’ trajectories beyond institutionalized transition points is limited – partly because adequate largescale panel data on students’ progress through higher education has been lacking in the past, partly because the prime interest was on transitions into and out of higher education. Indicators at hand tracing students’ paths through higher education, such as attrition rates based on cohort estimates, are insufficient (Klein & Stocké, 2016).
Thus, this study fills this gap by providing a systematic overview on bachelor students’ trajectories in German higher education as temporal patterns in higher education. Particularly in focus are the differences in higher education trajectories of students at universities and universities of applied as degree programmes at these two higher education institutions types have become alike during the past decades while their student bodies remain different in terms of background and paths into higher education.
The analysis is based on the student cohort (cohort 5) of the National Educational Panel Study (NEPS), a representative large-scale panel data set on students in Germany (Blossfeld et al, 2011). Students were surveyed for the first time during their first academic semester in Winter 2010/2011 and were followed throughout their higher education career by surveying approximately every six months. In order to have full information on students’ trajectories, the analytical sample is restricted to wave 10 and/or wave 12 participation allowing for a reconstruction of trajectories between September 2010 and March 2015.The final sample consists of 7.249 students. As a methodological contribution, this paper applies sequence analysis as a holistic method to reconstruct bachelor students’ trajectories in higher education. Sequence analysis is an explorative-descriptive approach that identifies patterns in time-ordered spell data (Cornwell, 2015). Transformation costs are set in such a way that equal order of states is prioritized over equal length. As the standard study duration of bachelor degree programmes varies, particularly at the universities of applied sciences, sequences are standardized in length. Based on the sequence dissimilarity matrix, sequences are clustered into six trajectory groups using Ward’s clustering. The core of this paper addresses the question whether students at universities of applied sciences proceed similarly through higher education as their fellow students at universities. While standard descriptive approaches may identify such differences, the source of origin of these differences would remain unclear. In order to distinguish differences in higher education trajectories that occur from differences in students’ background characteristics vs. organizational and environmental differences in the two institutions. Thus, propensity score reweighting is applied to balance differences in student characteristics at the two institutions, making it possible to compensate for (self-)selection effects into these two institutions. Consequently, by doing so, the “pure” difference in students’ trajectories of studying at a university vs. studying at a university of applied sciences can be extracted.
The sequence and subsequent cluster analysis yield six distinct trajectory types: two smooth trajectory clusters of students who remain in their initial bachelor programme until graduation. These two groups only differ by the duration until they graduate. The other groups are characterized by discontinuities: three clusters consist of trajectories signified by different patterns of degree programme changes. Another cluster contains trajectories of students who left higher education without finishing a degree. Compared to students’ trajectories in other higher education systems, double enrolments or interruptions play a minor role in German higher education while exceeding the standard duration of study seems very common. Most students follow smooth trajectories. A remarkable finding refers to differences in the trajectories of students at universities of applied sciences vs. university students: the former follow more often a smooth trajectory and stick more often to the standard duration of study. Instead, they are less likely to belong to any of the degree programme changer clusters. The focus for the next steps will be on the multivariate analysis using multinomial logistic regression and propensity score reweighting to investigate whether the identified differences in trajectories are merely the consequence of prior effects of (self-)selection into the respective higher education institutions or attributable to the organization of study programmes at the respective higher education institutions. Furthermore, as sequence analysis is a method that has become very popular in other areas but has been rarely used to study educational careers, this presentation will also provide some reflections and insights on the pros and cons of this method.
Adelman, C. (2005). Executive summary: The toolbox revisited. Paths to degree completion from high school through college. Journal for Vocational Special Needs Education, 28(1), 23-30. Blossfeld, H.-P., von Maurice, J., & Schneider, T. (2011). The National Educational Panel Study: need, main features, and research potential. Zeitschrift für Erziehungswissenschaft, 14(2), 5-17. Cornwell, B. (2015). Social sequence analysis: Methods and applications. New York, Cambridge University Press. Haas, C. and H. Van De Werfhorst (2017). "Ahead of the pack? Explaining the unequal distribution of scholarships in Germany." British Journal of Sociology of Education 38(5): 705-720. Goldrick-Rab, S. (2006). Following their every move: An investigation of social-class differences in college pathways. Sociology of Education, 79(1), 67-79. Klein, D., & Stocké, V. (2016). Studienabbruchquoten als Evaluationskriterium und Steuerungsinstrument der Qualitätssicherung im Hochschulbereich. In T. Wolbring & D. Großmann (Eds.), Evaluation von Studium und Lehre (pp. 323-365). Wiesbaden: Springer. Milesi, C. (2010). Do all roads lead to Rome? Effect of educational trajectories on educational transitions. Research in Social Stratification and Mobility, 28(1), 23-44. Shavit, Y., Arum, R., & Gamoran, A. (2007). Stratification in higher education. A comparative study. Stanford: Stanford University Press. Wolter, A. (2015). Massification and diversity: has the expansion of higher education led to a changing composition of the student body? In: European and German evidence Higher Education Reform: Looking back – Looking forward. Frankfurt am Main Peter Lang
00. Central Events (Keynotes, EERA-Panel, EERJ Round Table, Invited Sessions)
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