Is it all about individual effort? The effect of study conditions on student dropout intention

ABSTRACT

Understanding why students drop out from university has received much research attention. While the influence of students’ individual characteristics is well understood, the role of universities, however, has rather been neglected. This study draws attention to the effect of study conditions on individual dropout intention. On focus is the structure of the curriculum, achievement norms and the practical component of higher education programmes. A modified cost-benefit approach is introduced, systematising the most prominent individual dropout factors identified by prior research. It is assumed that these factors mediate the effect of study conditions on dropout intention. Analyses are conducted using the German Student Survey data (2000–2016), allowing an aggregated measurement of study conditions at the broad field of study level. Multilevel regression models support most of the theoretical assumptions: whereas a more highly structured curriculum improves students’ academic self-efficacy, and thus lowers their dropout intention, practical components have a similar effect by promising benefits in the form of good job preparation. On the other hand, achievement norms increase dropout intention by lowering performance or by leading to high psychological burdens. The results emphasise that study conditions play a significant role in student success by driving the main individual dropout factors.

KEYWORDS:

• University dropout
• student success
• study conditions
• higher education programmes
• Europe

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data are available via the Research Data Centre for Higher Education Research and Science Studies, DOI:10.21249/DZHW:stsu8316:1.0.0

Notes

1 In this respect, I follow the existing evidence of previous research (Aymans and Kauffeld 2015; Blüthmann, Lepa, and Thiel 2008; Kliegl and Müller 2012; Pohlenz and Tinsner 2004; Schiefele, Streblow, and Brinkmann 2007), which shows that structure and practical component decrease dropout and that achievement norms increase it. I provide a theoretical explanation for why these study conditions affect dropout in these particular ways.

2 Although self-efficacy is a relatively stable psychological trait, this study focuses on academic self-efficacy, which might be more prone to context effects: e.g. students might question their own capability to master academic situations after moving from secondary schools to more demanding universities, or they might reinforce their capability through studying subjects at the university that meet their interests, in contrast to studying a wide range of subjects at schools.

3 It is worth noticing that these study conditions might affect both performance and academic self-efficacy, since these are highly correlated. However, there are some predominant assumptions in play here. Programmes with high achievement norms are highly selective, and thereby integrate students with high grades and high academic self-efficacy. Due to the high performance pressure, achievement norms should thus lower the grades in the first step, which might lead to higher student self-doubt and lower self-efficacy in the second step. Structure, on the other hand, gives orientation for individuals acting to achieve the required educational records (choosing particular modules fitting certain interests, submitting papers on time, meeting examination requirements, etc.). Students successfully meeting these goals might thus consider themselves highly academic efficient. This might boost their self-confidence and result in higher grades in a second step. I test these assumptions by considering both self-efficacy and grades when addressing the effect of achievement norms and structure in the findings section.

4 Most student surveys, e.g. the Irish Survey of Student Engagement (ISSE), the US Beginning Postsecondary Students or the National Student Surveys in the Netherlands (NSE) or UK (NSS), etc., collect very limited or no information on study conditions. To my knowledge, only two German surveys do focus on these dimensions: the Student Survey and the National Educational Panel Study (NEPS).

5 NEPS data offers relatively low case numbers per university and thus the aggregations of study conditions might not result from normally distributed samples (>10; see e.g. Schaeper 2020). By contrast, the higher case numbers in the Student Survey provide reliable information for aggregations (>30). Additionally, the survey contains more items on study conditions than NEPS and its long-term design provides information for different survey waves, and thus allows higher variation at the aggregated level.

6 Two additional dimensions were identified within the factor analysis: a teaching dimension and social climate. However, I excluded these dimensions from the main analysis because they did not provide robust results and consider them only as control variables since they correlate with the other study conditions at the aggregated level.

7 A common solution to these regression problems is to provide Average Marginal Effects (AMEs). However, this applies less for ordered logistic regressions since AMEs need to be computed for all categories of the dependent variable separately (and presented for each independent variable). This, in turn, requires complex interpretations since each category is compared with the remaining ones, which is more convenient to do when analysing dummy variables in the binary logistic regression.

8 This is thus not a mediating effect of high job expectations but a moderating one (interaction effect).

9 This effect represents the difference in the mean values of the dropout intention between broad FoS with the lowest and the highest achievement norms (3.2 vs 5.8: see Appendix Table A4) when considering the coefficient of M2 in Table 2.

Additional information

Funding

This work was supported by the German Federal Ministry of Education and Research under Grant M531000.

Notes on contributors

Anna Marczuk

Anna Marczuk is a higher education researcher since 2011. She worked in several research institutions in Germany and abroad, including the Berlin Social Science Center (WZB), University of Hannover and University of Trento (Italy). Since 2018, she is coordinating the Research Group on Higher Education at the University of Konstanz.