Source: European Journal of Teacher Education, 44:4, 520-537
(Reviewed by the Portal Team)
The authors want to tie in the existing literature on students’ persistence and contribute to it.
They do so by being one of the first investigating the social support networks of student teachers and their effect on students’ dropout intention.
Based on the social cognitive career theory (SCCT), they further examine the influence of supports and barriers on students’ dropout intention.
Based on a literature review, the authors formulate the following hypotheses:
1: Social support is negatively correlated with the disapproval of influential others towards the chosen career path.
2: The disapproval of influential others towards the chosen career path is positively correlated with student teachers’ intention to drop out.
3: The effect of social support on student teachers’ dropout intention is mediated by the disapproval of influential others towards their chosen career path.
Participants and procedure
The participants are student teachers enrolled in a two-subject-bachelor with a standard period of study of six semesters.
This degree programme is modular; thus, students must accumulate 180 credits to complete it.
Besides the subject-specific skills, students acquire skills in key qualification modules (e.g. intercultural competence).
Furthermore, internships are part of the curriculum to help students gain practical skills.
A master’s degree can be completed afterwards.
This study analyses data of a longitudinal online survey (Fit+) at a public university in Germany.
It is part of the national ‘Qualitätsoffensive Lehrerbildung’ (‘Quality Campaign Teacher Education’) which aims to improve the quality of teaching in the field of teacher training.
Fit+ accompanies the students every three months during their studies, from the beginning of their first academic semester onwards.
In this study, the authors analyse data from the first measurement point, at the beginning of the students’ first academic semester (T1), and the third six months later, after they had taken their first university examinations (T2).
Of the approximately 270 enrolled students in their first semester, 232 (85.9%) completed the relevant scales at T1 (e.g. provided an anonymous code for matching with later measurement points).
The final data sample consists of N = 165 students.
Social network analysis Network variables & composition
A social support network questionnaire was applied to analyse the support networks.
The participants were asked to list up to 20 people who had supported them in the last six months.
The mean network size was 8.1 (SD = 3.86) in this study.
Furthermore, participants answered demographic questions about each supporter (alter) they listed before.
These questions included the gender, age, and the kind of relationship to the participant.
Altogether, 1330 alters were listed up.
Participants also reported whether the alters know each other and, if so, how strong the relationship is on a scale from 1–3.
The mean density was 0.59 in this study.
Perceived disapproval of influential others
A 2-item scale was created for this study to measure the perceived disapproval of influential others towards the chosen career path.
Social support quality
After the social support network questionnaire, the social support quality was measured using a single-item measure.
To measure students’ intention to drop out of university, participants chose one of five statements (Aymans and Kauffeld 2015) to indicate which of them resembled their dropout intention the best.
As German students must choose their major before they start their studies, the dropout intention was measured at the beginning of their first academic semester (T1) as well as six months later (T2).
Results and discussion
Based on SCCT, the authors examined the relationship between social support and barriers and their effect on persistence among German student teachers at the beginning of their studies and half a year later. Specifically, they predicted a negative relationship between social support and barriers; that is, the higher the social support quality is, the lower is the perceived disapproval of influential others towards the chosen career path.
In line with the results of past studies (e.g. Lent et al. 2003, 2005), this prediction was supported.
Furthermore, they predicted a positive relationship between barriers and students’ intention to drop out.
More precisely, they expected that a higher perception of disapproval would lead to a stronger intention to drop out of university.
This prediction was also supported.
The results did not yield a significant direct effect between social support and students’ intention to drop out of university.
This is in line with previous research (e.g. Lent et al. 2003, 2005; Restubog, Florentino, and Garcia 2010), supporting the authors’ assumption that the positive effect of social support is likely to be mediated through other variables.
Finally, they predicted that the perceived disapproval of influential others mediates the effect of social support quality on students’ intention to drop out.
That is, high perceived social support evolves its buffering effect on students’ intention to dropout only through a lower perception of barriers.
The results supported this prediction.
Although the indirect effect of social support was fairly weak, the results indicate that barriers seem to have a much stronger influence on students’ decision to discontinue their studies.
Thus, this study identifies perceived barriers as an essential boundary condition that mediates the influence of social support on students’ dropout intention.
Theoretical and practical implications
From a theoretical perspective, the authors found evidence for the maladaptive nature of perceived disapproval of influential others in the context of university dropout decisions.
Their findings suggest that the perceived disapproval of influential others restricts students’ intention to persist at university.
Furthermore, they shed light on how social support and barriers relate to each other and how this influences their effect on students’ intention to drop out of university.
Specifically, the perception of social support diminishes the perception of barriers and affects through this path the persistence intention of students.
They found that neither the social support quality nor the network size – as a measure of social support quantity – was directly linked to students’ dropout intention, whereas barriers significantly predicted this intention.
This indicates that a shift in focus from social support to barriers might enhance our understanding of the underlying mechanisms of students’ dropout intention and should, therefore, be regarded in theories concerned with students’ development.
The authors note that their results do not deny the positive effect of social support.
However, concerning the dropout intention of students, resources such as social support seem less important than the negative effect evoked through barriers.
The authors note that the results of their study also have practical implications for teacher education.
They may be useful for counsellors working with student teachers who want to drop out of university.
While it is common to concentrate on students’ personal factors (such as self-efficacy; Bandura 1977) and organisational factors at university, for instance, the information provided by the university before and during the first semester (e.g. Aymans and Kauffeld 2015; Wikan and Bugge 2014), their results add a new perspective for career counsellors.
Focusing on how students perceive their supporting environment, explicitly concerning how they perceive possible pressure to behave in a particular manner to please this supporting environment, might lead to new interventions aimed to help students to persist at university and to achieve their academic degree.
The findings might help counsellors to design more effective interventions to reduce students’ drop out of university.
Aymans, S. C., and S. Kauffeld. 2015. “To Leave or Not to Leave? Critical Factors for University Dropout among First Generation Students”. Zeitschrift für Hochschulentwicklung. 10:23–43.
Bandura, A. 1977. “Self-Efficacy: Toward a Unifying Theory of Behavioral Change.” Psychological Review. 84(2):191–215.
Lent, R. W., S. D. Brown, L. Nota, and S. Soresi. 2003. “Testing Social Cognitive Interest and Choice Hypotheses across Holland Types in Italian High School Students.” Journal of Vocational Behavior. 62(1):101–118.
Lent, R. W., S. D. Brown, H. B. Sheu, J. Schmidt, B. R. Brenner, C. S. Gloster, G. Wilkins, et al. 2005. “Social Cognitive Predictors of Academic Interests and Goals in Engineering: Utility for Women and Students at Historically Black Universities.” Journal of Counseling Psychology. 52(1):84–92.
Restubog, S. L. D., A. R. Florentino, and P. R. J. M. Garcia. 2010. “The Mediating Roles of Career Self-Efficacy and Career Decidedness in the Relationship between Contextual Support and Persistence.” Journal of Vocational Behavior. 77(2):186–195.
Wikan, G., and L. S. Bugge. 2014. “Student Performance in Teacher Education in Norway: The Impact of Student, Institutional and Structural Factors.” European Journal of Teacher Education. 37 (4):442–452.