Introduction

Across Europe, adult and continuing education (ACE) plays a central role in lifelong learning, labour market integration, and social participation (OECD, 2021; Cedefop, 2025). European policy documents emphasise adult educators’ key role in securing educational quality. Over the past two decades they have been framed as a critical pillar of adult education systems (Ioannou, 2023). At the same time, however, their initial education and continuing professional development have received limited systematic policy attention, resulting in weakly structured career pathways and insufficient professional recognition.

Employment structures in ACE remain fragmented in many European contexts. Research points to heterogeneous qualification routes, limited career prospects, and a high prevalence of freelance or part-time work (Milana et al., 2018). In several countries, adult educators combine teaching with other professional activities and lack stable institutional affiliation (Ioannou, 2023). Similar patterns are visible in Germany, where a substantial share of ACE staff work on a free basis (Martin & Schrader, 2021).

Despite these structurally insecure conditions, empirical studies—particularly from Germany—indicate that adult education is often a consciously chosen profession. Educators report strong intrinsic motivations, especially the desire to share knowledge and support learners, alongside high job satisfaction as an indicator of high professional well-being (Autorengruppe wb-personalmonitor, 2016). This constellation reflects a paradoxical professional context: precarious employment coexists with strong commitment. Yet, the interplay between working conditions, motivational processes, and instructional practice remains underexplored.

The Job Demands–Resources (JD–R) model (Bakker & Demerouti, 2007, 2017) provides a suitable framework to analyse such ambivalent contexts. By distinguishing between health-impairment processes and motivational processes, it explains how job and personal resources foster work engagement, which in turn predicts well-being and performance outcomes. Work engagement—usually defined as vigour, dedication, and absorption (Schaufeli et al., 2002)—has been linked to commitment, satisfaction and job performance in schools (Hakanen et al., 2006; Admiraal & Kittelsen Røberg, 2023), but has rarely been examined in adult education or connected to instructional quality (e.g. Reiter & Kuper, 2024).

Against this backdrop, this study applies the motivational pathway of the JD–R model to ACE. We examine how work-related resources (e.g., cooperation, feedback, career prospects) and personal resources (e.g., self-efficacy, enthusiasm) are associated with work engagement, and how engagement, in turn, relates to job satisfaction and cognitive activation as a core dimension of instructional quality and an indicator of job performance.

Instructional quality is commonly conceptualised as a multidimensional construct comprising classroom management, socio-emotional support, and cognitive activation (Klieme et al., 2009). Given the voluntary and heterogeneous nature of adult learning contexts, we argue that cognitively demanding and reflective learning opportunities are central (Desimone & Garet, 2015). For this proposal, we therefore focus on cognitive activation while acknowledging the broader multidimensional framework of instructional quality.

Using data from the German TAEPS study and structural equation modelling, we address two questions: (RQ 1) How are work-related and personal resources associated with work engagement among ACE teaching staff? (RQ 2) To what extent does work engagement mediate the relationship between these resources and (a) job satisfaction and (b) cognitive activation?

Sample Analysis is based on data from the second wave of the German TAEPS study (N = 2,831 teachers; 62% female; median age= 57; median teaching experience =15 years). Measures Work resources were operationalised with three items each for cooperation opportunities and career advancement opportunities and one item for feedback. Personal resources comprised self-efficacy (12 items) and enthusiasm for teaching (4 items). Work engagement was operationalised using four AVEM subscales (Schaarschmidt & Fischer, 1997): subjective significance of work, career ambitions, exertion, and perfectionism (four items each). Job satisfaction was measured with four items and cognitive activation with 11 items. Analysis Methods To test our research questions, structural equation modelling (SEM) was conducted using robust maximum likelihood. Latent variables were specified for all multi-item constructs, and measurement models were estimated prior to testing structural relations. We computed separate models for job satisfaction and cognitive activation. Additional models for classroom management and socio-emotional support were estimated to examine whether the motivational pathway generalizes across instructional quality dimensions; these analyses are not reported here. Results Our measurement model shows moderate to predominantly high and significant factor loadings of our items (λ ≥ .6), indicating a reliable operationalization of the latent constructs. Only the items for career advancement opportunities show low factor loadings on the work resources factor (λ < .4). For the model predicting job satisfaction, model fit indices indicate an acceptable fit [RMSEA = .049, CFI = .717; TLI = .700]. In line with RQ 1, work resources positively predict engagement (β=.15,p<.001) and personal resources (β=.12,p<.001). Personal resources are the strongest predictor of work engagement (β=.32,p<.001). Addressing RQ 2, work engagement significantly predicts job satisfaction (β=.28,p<.001) and it acts as a mediator, linking work and personal resources to job satisfaction, with a small indirect effect for work resources (β = .04) and personal resources (β = .09). For the model predicting cognitive activation [RMSEA = .041, CFI = .722, TLI = .709], the effect sizes and direction of the relations of work resources with engagement and with personal resources, as well as between personal resources and engagement are identical to the previous model. Engagement significantly predicts cognitive activation, though the effect size is smaller than for job satisfaction (β=.11, p<.001) and it acts as a mediating mechanism linking work and personal resources to cognitive activation. Yet, indirect effects sizes are modest (work resources, β = .02, personal resources, β = .04).

Conclusions The findings provide empirical support for the motivational pathway of the JD–R model in the ACE context. Both work-related and personal resources are positively associated with work engagement, with personal resources emerging as the stronger predictor. Engagement, in turn, is significantly related to both job satisfaction and cognitive activation, confirming its role as a central motivational mechanism linking resources to well-being and performance-related outcomes. Yet, the association between engagement and cognitive activation is weaker than for job satisfaction. This suggests that while motivational processes contribute to instructional quality, the design of cognitively activating learning may depend on additional factors. Particularly in adult education, structural demands, contextual constraints, or pedagogical competences may play an important complementary role (Martin & Schrader, 2021). By transferring the JD–R framework to adult education and linking it to a core dimension of instructional quality, the study contributes to bridging occupational psychology and adult education research. It highlights that even within structurally insecure employment contexts, motivational mechanisms remain relevant for both teachers’ well-being and instructional quality. Several limitations should be acknowledged. Work engagement was operationalised using AVEM subscales rather than the widely applied UWES measure (Schaufeli et al., 2002), which may limit comparability with other JD–R studies. Moreover, the analyses focus exclusively on the motivational pathway; the interplay between job demands and resources cannot yet be examined and will be addressed with future waves of the TAEPS study. In addition, work-related resources were captured at a relatively aggregated level and could be differentiated more precisely. Finally, instructional quality was assessed via self-reports rather than independent classroom observations. Future studies should therefore integrate job demands, refine the measurement of resources, and include additional data sources on instructional quality to more comprehensively understand how working conditions shape professional well-being and teaching quality in adult education.

References Admiraal, W., & Kittelsen Røberg, K.-I. (2023). Teachers' job demands, resources and their job satisfaction: Satisfaction with school, career choice and teaching profession of teachers in different career stages. Teaching and Teacher Education, 125, 1–10. Autorengruppe wb-personalmonitor (Ed.) (2016). Das Personal in der Weiterbildung. Arbeits- und Beschäftigungsbedingungen, Qualifikationen, Einstellungen zu Arbeit und Beruf. W. Bertelsmann. Bakker, A. B. & Demerouti, E. (2007). The Job Demands-Resources Model: State of the Art. Journal of Managerial Psychology, 22(3), 309–328. Bakker, A. B., & Demerouti, E. (2017). Job demands–resources theory: Taking stock and looking forward. Journal of Occupational Health Psychology, 22(3), 273–285. Cedefop. (2025). Shaping learning and skills for Europe: A time for commitment (TI-01-25-054-EN-N). Publications Office of the European Union. https://data.europa.eu/doi/10.2801/2783450 Desimone, L. M., & Garet, M. S. (2015). Best practices in teachers’ professional development in the United States. Psychology, Society & Education, 7(3), 252–263. Hakanen, J. J., Bakker, A. B., & Schaufeli, W. B. (2006). Burnout and work engagement among teachers. Journal of School Psychology, 43(6), 495–513. Ioannou, N. (2023). Professional development of adult educators in European policy documents: An overview of conceptualisations and challenges. International Review of Education. https://doi.org/10.1007/s11159-023-10014-0 Klieme, E., Pauli, C., & Reusser, K. (2009). The Pythagoras study: Investigating effects of teaching and learning in Swiss and German mathematics classrooms. In T. Janík & T. Seidel (Eds.), The power of video studies in investigating teaching and learning in the classroom (pp. 137–160). Waxmann. Milana, M., Webb, S., Holford, J., Waller, R., & Jarvis, P. (Eds.). (2018). The Palgrave international handbook on adult and lifelong education and learning. Palgrave. Martin, A. & Schrader, J. (2021). Das Personal in der Weiterbildung. In S. Widany, E. Reichart, J. Christ & N. Echarti (Eds.), Trends der Weiterbildung. DIE-Trendanalyse 2021 (pp. 179–208). Bertelsmann. https://www.die-bonn.de/id/41438 OECD. (2021). Adult learning and COVID-19: How much informal and non-formal learning are workers missing? OECD Publishing. https://doi.org/10.1787/56a96569-en Reiter, S. & Kuper, H. (2024). Professional Commitment of Staff in Continuing Education - A Professional Characteristic Compared Across Different Working Conditions. In Internationales Jahrbuch der Erwachsenenbildung 2024. Coordination of Action in Adult Education Organizations (S. 77-90). wbv Publikation. https://doi.org/10.3278/I77017W005 Schaufeli, W. B., Salanova, M., González-Romá, V., & Bakker, A. B. (2002). The measurement of engagement and burnout: A two sample confirmatory factor analytic approach. Journal of Happiness Studies, 3(1), 71–92.