02 SES 06 B, Career Choice and Career Competences
With 24% (BMBF, 2016) and 29% (Heublein et al., 2017), respectively, dropout rates for apprenticeships and higher educational studies are still high in Germany. One of the reasons former apprentices and students name for not finishing a vocational training or studies are false expectations regarding the actual day-to-day routines and requirements of a job (cf. BMBF, 2016). These false or unmet expectations can be explained by a lack of knowledge about the world of work. Both, occupational information and awareness of vocational prerequisites, as part of the knowledge about the world of work are career-related competencies (Crites & Savickas, 1996; Driesel-Lange, Hany, Kracke, & Schindler, 2010).
The development of career-related competencies plays an important role in the context of adolescent identity formation as they represent existential tools in the process of a life-long career development (Dreher & Dreher, 1985; Erikson, 1968; Savickas et al., 2009).
In Germany, career education mainly takes place in schools. There, career education measures are delivered to build up career choice competencies. So, career education in schools is one of the influencing variables on successful school-to-work/university transitions. Considering the mentioned dropout rates, it can be concluded that existing career education programs currently do not support all pupils equally well in transitioning from secondary school to further studies or formal training.
To address this problem four points should be considered, 1) groups with homogenous developmental stages of career choice competencies need to be identified, 2) more information on the short and mid-term development of career choice competencies is needed, 3) effects of different career education measures on these homogenous groups need to be analysed to 4) apply this new knowledge by tailoring career education programs in schools to the specific need of the students. As a result, future career interventions could be adapted to career choice competencies that students need to develop to successfully master the current stage of their individual career path.
Several previous studies have covered the first step and have identified distinct profiles of career development (Hirschi & Valero, 2015; Kaak, Heinrichs, Lipowski, Wuttke, & Kracke, 2015; Ohlemann & Driesel-Lange, 2017). These profiles did not differ in their competence patterns but rather in the overall level of competence.
Few longitudinal studies exist that analyse the development of career-related competencies over time. In our study we therefore focus on point 2) and aim to explore the trajectories of and between profiles over time. To identify groups of pupils who started with a low profile in career choice competency and who either did not or did progress during their career education in school is important for further analysis on effective career education interventions (point 3).
We divided our research question about patterns in the transitional development of students’ career choice competencies into three hypotheses: pupils change between profile groups of career choice competencies over time (H1), all students experience a positive development of their competencies over time (H2), students starting their career orientation process follow different trajectories in terms of timing and level of competence (H3).
The analyses are based on data from a longitudinal study in North Rhine-Westphalia in Germany (c.f. Driesel-Lange & Kracke, 2017). The main goal of this study was an empirical assessment of the effects of a one-day career orientation intervention called analysis of students’ potential. At the beginning of the study students were in grade nine either in a secondary school or a comprehensive school. The survey with four measurement points over three school years included sociodemographic data as well variables on career orientation activities, career choice competencies and teacher support. In this analysis we only included the first three waves due to the relatively small sample sizes (Nt1=309, Nt2=337, Nt3=302, Nt1-3=220) and the complexity of the analysis method which is described below. Students self-evaluated their career choice competencies using the diagnostic questionnaire of career choice competencies of Kaak, Driesel-Lange, Kracke, and Hany (2013). They answered 93 items on a four-point Likert scale (1 = strongly disagree, 4 = strongly agree). Their answers reflected their self-evaluation on three dimensions of career-related competencies: knowledge, motivation and action. The first dimension of knowledge included four different facets of competency: self-efficacy, occupational information, awareness of vocational prerequisites and planning and decision-making competencies (c.f. Crites & Savickas, 1996). The motivational dimension combined four more competency facets: career concern, career control, career openness and career confidence (cf. Savickas & Porfeli, 2011). The third dimension (action) sums up the four facets exploration, self-regulation, problem-solving and stress-management (c.f. Driesel-Lange et al., 2010). Missing data were estimated by using the default full information likelihood (Graham, 2012) in Mplus. To identify patterns in the development of career choice competencies among German high school students we performed a latent transition analysis (LTA) following the method of Nylund-Gibson, Grimm, Quirk, and Furlong (2014). This three-step approach combines latent class / profile analysis (LCA) and growth mixture models (GMM) and allows to better understand the trajectories of subjects between latent profiles over time (Nylund-Gibson et al., 2014). The latent profile analysis and latent class / profile analysis models as well as the GMM were performed using the Software Mplus version 8. For all other calculations we used the Software IBM SPSS Statistics 25.
Using the latent profile analysis (LPA) we identified three distinct profiles of career choice competency among students. As seen in previous studies, the three profiles showed a similar form and variated only on the level of development of the twelve facets of career choice competency. The outcomes of the GMM as well as the results of the latent transition model will be presented and discussed. The results of this study are especially important to identify groups with initially low competency levels that are at risk of not progressing in this area without any further assistance (c.f. Hirschi & Valero, 2015). Future empirical work could focus on these at-risk groups and career orientation measures that showed significant effects on their career choice competence development. In the joint debate of researchers, schools and providers of career orientation interventions, the awareness of different developmental growth paths could raise awareness of the importance of individualized career education as a measure of effective inclusion.
Bundesministerium für Bildung und Forschung. (2016). Berufsbildungsbericht 2016. Berlin. Retrieved from: https://www.bmbf.de/pub/Berufsbildungsbericht_2016.pdf Crites, J. O., & Savickas, M. L. (1996). Revision of the Career Maturity Inventory. 2, 4, 131-138. Dreher, E., & Dreher, M. (1985). Wahrnehmung und Bewältigung von Entwicklungsaufgaben im Jugendalter: Fragen, Ergebnisse und Hypothesen zum Konzept einer Entwicklungs- und Pädagogischen Psychologie des Jugendalters. In R. Oerter (Ed.), Lebensbewältigung im Jugendalter (pp. 30-61). Weinheim: Edition Psychologie, VCH. Driesel-Lange, K., Hany, E., Kracke, B. & Schindler, N. (2010). Ein Kompetenzentwicklungsmodell für die schulische Berufsorientierung. In U. Sauer-Schiffer & T. Brüggemann (Eds.), Der Übergang Schule - Beruf. Beratung als pädagogische Intervention (pp. 157-175). Münster: Waxmann. Driesel-Lange, K., & Kracke, B. (2017). Potentialanalysen als Instrumente der Förderung in der Berufs- und Studienorientierung. Besondere Herausforderungen der Begleitung von Jugendlichen mit Hochschulzugangsberechtigung. In T. Brüggemann, K. Driesel-Lange, & C. Weyer (Eds.). Münster: Waxmann. Erikson, E. H. (1968). Identity : youth and crisis. New York : Norton. Graham, J. W. (2012). Missing data : analysis and design. New York : Springer. Heublein, U., Ebert, J., Hutzsch, C., Isleib, S., König, R., Richter, J., & Woisch, A. (2017). Zwischen Studienerwartungen und Studienwirklichkeit - Ursachen des Studienabbruchs, beruflicher Verbleib der Studienabbrecherinnen und Studienabbrecher und Entwicklung der Studienabbruchquote an deutschen Hochschulen. Retrieved from http://www.dzhw.eu/pdf/pub_fh/fh-201701.pdf Hirschi, A., & Valero, D. (2015). Career adaptability profiles and their relationship to adaptivity and adapting. Journal of Vocational Behavior, 88, 220-229. doi:10.1016/j.jvb.2015.03.010 Kaak, S., Driesel-Lange, K., Kracke, B., & Hany, E. (2013). Diagnostik und Förderung der Berufswahlkompetenz Jugendlicher. bwp@ Berufs- und Wirtschaftspädagogik – Online, Spezial 6 (pp. 1-13). Kaak, S., Heinrichs, K., Lipowski, K., Wuttke, E., & Kracke, B. (2015). Der Fragebogen zur Berufswahlkompetenz Ein Instrument zur Individualisierung der Berufsorientierung? Presentation at 3.Tagung der Gesellschaft für Empirische Bildungsforschung, Bochum. Nylund-Gibson, K., Grimm, R., Quirk, M., & Furlong, M. (2014). A Latent Transition Mixture Model Using the Three-Step Specification. Structural Equation Modeling: A Multidisciplinary Journal, 21(3), 439-454. doi:10.1080/10705511.2014.915375 Ohlemann, S., & Driesel-Lange, K. (2017). Individuelle Begleitung beruflicher Entwicklung: Kompetenzförderung anhand von Lernstilen. In S. Seeber, J. Seifried, & B. Ziegler (Eds.), Jahrbuch der berufs- und wirtschaftspädagogischen Forschung 2016. Opladen: Budrich. Savickas, M., Nota, L., Rossier, J., Dauwalder, J.-P., Eduarda Duarte, M., Guichard, J., . . . van Vianen, A. (2009). Life designing: A paradigm for career construction in the 21st century (Vol. 75). Savickas, M., & Porfeli, E. (2011). Revision of the Career Maturity Inventory: The Adaptability Form. 19, 4, 355-374. doi:10.1177/1069072711409342
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