Session Information
02 SES 07 C, Learning III: Resources and Skills
Paper Session
Contribution
The development of vocational knowledge and skills depends to a great part on the learning resources and opportunities that learners have in their companies and at school (Messmann & Mulder, 2015). Apprentices’ active participation in the workplace and at school and the provision of training environments conducive to learning are core elements in dual initial VET (Lüthi & Stalder, 2018). Learning resources are shaped by many factors, such as the learning culture of organisations and schools, the instruction and guidance learners receive, or the interaction with trainers, teachers and more experienced co-workers. The opportunities to learn also depend on the tasks, learners engage in (Stalder & Nägele, 2011), the level of task demands, and the autonomy learners have regarding their work and learning in the workplace and at school (Taris & Feij, 2004).
While learning at school is highly structured and accompanied by trained teachers, learning at the workplace is situated in everyday work-processed, real tasks and teams of co-workers, who are not necessarily trained as tutor. Research on workplace learning has shown that learning is most effective when job demands at conducive workplaces are high, and when the individual is actively and autonomously involved in a work task (De Witte, Verhofstadt, & Omey, 2007; Karasek, 1979; Taris & Feij, 2004).
Learning at school can be demanding in terms of the workload, the need to study during weekends, troubles to keep up with the fast-changing subjects of lessons and the compelexity of learning contents. Autonomy at school is limited by curricula and tight lesson plans. Research suggests that learning is most effective when both resources and demands are high, and when an individual is actively and autonomously involved in a work task (De Witte et al., 2007; Taris & Feij, 2004). This can be explained by the Job Demands-Resources (JD-R) model (Bakker & Demerouti, 2007), which highlights that resources (e.g., support, autonomy, feedback) fulfil basic human needs (Ryan & Deci, 2000) and have a motivational potential that leads to increased learning effort, engagement and successful goal achievement (Bakker, Demerouti, & Ten Brummelhuis, 2012).
Research found that boundary crossing between workplaces and schools is essential for learners’ vocational development (Schaap, Baartman, & de Bruijn, 2011; Stalder & Lüthi, in press) and individuals are co-responsible for positive learning processes and outcomes (Billett, 2001). However, empirical evidence on workplace and school-based learning and the transfer from one to the other in the context of vocational education is still rare (Mikkonen, Pylväs, Rintala, Nokelainen, & Postareff, 2017; Schaap et al., 2011; Tanggaard, 2007).
In the present contribution we aim to get a broader understanding on work-based and school-related resources learners get on both learning venues during their apprenticeship. Drawing from the JD-R model, we explore first, whether different homogenous profiles of learning resources during the apprenticeship can be found. Second, we analyse transitions into the second and third VET year, to examine whether apprentices stay in the same resources profiles or change.
The questions arise, if apprentices in lower resources profile change into higher resources environments? Do apprentices with high resourced learning venues stay in the same condition until the end of their apprenticeship? To what extent are learning resources and their facets flexible over time or to what extent might facets such as autonomy, demands and learning opportunities be rather stable? Third, we analyse differences between groups, to find out, whether certain profiles are linked to specific vocational occupational fields, and how resources impact apprentices’ educational satisfaction and their occupational commitment.
Method
For our investigation we explore work-related and school-related resources of Swiss ap-prentices’ learning environments and the development of resources over three VET years (N=1185, 49.6% female). Apprentices were surveyed annually (self-reported). Mean age of apprentices in their first VET year was 15.5 years (SD = .65). Studying three waves of the Swiss longitudinal youth dataset TREE (Transitions from Educatich ich das ion to Employment) (Stalder, Meyer, & Hupka-Brunner, 2011), we first examine the existence of homogenous subgroups of learning resources in all three waves applying latent profile analysis (LPA) (Nylund, Asparouhov, & Muthén, 2007). LPA is a person-oriented clustering approach that proves to be more consistent compared to variable-oriented cluster-analysis (Vermunt, Magidson, Hagenaars, & McCutcheon, 2002). We then analyse learning development trajectories to explore resources and changes of resources patterns during the whole apprenticeship, applying latent transition analysis (LTA). LTA is a longitudinal method first described by Graham, Collins, Wugalter, Chung, and Hansen (1991) enabling an understanding of e.g., developmental movement of apprentices between latent profiles over a given period (Colllins & Lanza, 2010). Resources during VET were assessed with six indicators for each learning venue, the workplace and school, including autonomy, instruction quality, learning opportunities, climate and qualitative (task complexity) and quantitative (workload) demands (Stalder et al., 2011).
Expected Outcomes
The person-oriented approach of the LPA proved to be highly suited to investigate the motivational potential of the JD-R model and the construct of learning resources (Bakker & Demerouti, 2007). We found four homogenous latent profiles that are characterised by different patterns and levels of work- and school-related resources, including autonomy, instruction quality, learning opportunities, climate and demands: (1) Generally high resources, (2) average resources, (3) high work – low school resources and (4) low work – high school resources (Lüthi & Stalder, in press). The groups mainly differed in terms of overall levels of resources (low – high) and with regard to different resources provided at both learning environments. These first results allow us to get an integrated view about variations in learning environments and underline the importance of boundary crossing between learning places (Stalder & Lüthi, in press). In sum, our study highlights the importance of providing apprentices with high learning resources, challenging, empowering and supportive work and school environments to ensure that the requirements of both learning environments are well adapted to apprentices needs (Ryan & Deci, 2000), their learning and vocational competence development. Latent transition analysis will be run to explore apprentices’ resources profile movements. Results will be discussed and further elaborated with respect to previous findings on workplace learning of young worker, learners in apprenticeships and learners in school-based VET as well as in the sense of lifelong learning.
References
Bakker, A. B., & Demerouti, E. (2007). The job demands-resources model: State of the art. Journal of Managerial Psychology, 22(3), 309-328. doi:10.1108/02683940710733115 Bakker, A. B., Demerouti, E., & Ten Brummelhuis, L. L. (2012). Work engagement, performance, and active learning: The role of conscientiousness. Journal of Vocational Behavior, 80(2), 555-564. doi:10.1016/j.jvb.2011.08.008 Billett, S. (2001). Learning through work: Workplace affordances and individual engagement. Journal of Workplace Learning, 13(5), 09-214. Colllins, L. M., & Lanza, S. T. (2010). Latent class and latent transition analysis: With applications in the social, behavioral and health sciences. New Jersey: Johl Wiley & Sons. De Witte, H., Verhofstadt, E., & Omey, E. (2007). Testing Karasek's learning and strain hypotheses on young workers in their first job. Work & Stress, 21(2), 131-141. doi:10.1080/02678370701405866 Graham, J. W., Collins, L. M., Wugalter, S. E., Chung, N. K. J., & Hansen, W. B. (1991). Modeling Transitions in Latent Stage-Sequential Processes: A Substance Use Prevention Example. Journal of Consulting and Clinical Psvchology, 59(1), 48-57. doi:10.1037/0022-006X.59.1.48 Karasek, R. A. (1979). Job demands, job decision latitude, and mental strain: implications for job redesign. Administrative Science Quarterly, 24(2), 285-308. Lüthi, F., & Stalder, B. E. (2018). Situational and Individual Resources Predict Learning Opportunities and Career Outcomes in VET. In C. Nägele & B. E. Stalder (Eds.), Trends in vocational education and training research. Proceedings of the European Conference of Educational Research (ECER), Vocational Education and Training Network (VETNET) (pp. 226-237). Bern, CH: VETNET. Messmann, G., & Mulder, R. H. (2015). Conditions for apprentices’ learning activities at work. Journal of Vocational Education & Training, 67(4), 578-596. doi:10.1080/13636820.2015.1094745 Mikkonen, S., Pylväs, L., Rintala, H., Nokelainen, P., & Postareff, L. (2017). Guiding workplace learning in vocational education and training: a literature review. Empirical Research in Vocational Education and Training, 9(1). doi:10.1186/s40461-017-0053-4 Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A monte carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 535-569. doi:10.1080/10705510701575396 Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68-78. doi:10.1037/0003-066X.55.1.68 Schaap, H., Baartman, L., & de Bruijn, E. (2011). Students’ learning processes during school-based learning and workplace learning in vocational education: A review. Vocations and Learning, 5(2), 99-117. doi:10.1007/s12186-011-9069-2
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