Understanding Teacher Workload in Blended Learning: Insights Through the Job Demands-Resources Model

Zhao Cheng et al.

Electronic Journal of e-Learning2026https://doi.org/10.34190/ejel.24.1.4465article
AJG 1
Weight
0.37

What the paper says

Blended learning (BL) has become an increasingly prevalent instructional model in primary and secondary education, yet its implementation has intensified concerns about teacher workload and well-being. While prior research has documented workload pressures associated with digitalization and AI integration, there remains limited empirical insight into how teachers experience, interpret, and manage these demands in blended learning environments. Guided by the Job Demands-Resources (JD-R) model, this qualitative study investigates how BL reshapes teachers’ job demands and resources, and how educators respond to these changes in practice. Specifically, this study explores how BL influences teachers’ perceived workload (RQ1), the specific challenges they encounter during BL implementation (RQ2), and the strategies and resources they employ to manage these demands effectively (RQ3). Semi-structured interviews were conducted with ten primary and secondary schoolteachers in Flanders (Belgium) who had experience implementing blended learning. Data were analysed using a reflexive thematic analysis supported by NVivo, following a systematic and iterative coding process. The JD-R model informed the analytical lens, enabling the identification of workload-related demands, available resources, challenges and coping strategies within teachers’ everyday practice. Regarding RQ1, the findings demonstrate the dual nature of blended learning as both intensifying workload and providing supportive resources. Teachers reported increased demands from dual-mode lesson design, technological integration, expanded assessment requirements, and institutional platform mandates, leading to cognitive and emotional strain. Conversely, automated assessment, reusable digital materials, learning platforms, and inclusive tools reduced administrative effort and supported organization, partially decreasing these heightened demands. With regard to RQ2, workload pressures were intensified by challenges in digital classroom management, frequent technical disruptions, and the continuous need to learn and support new technologies. Teachers also reported emotional and organizational strain linked to ineffective collaboration, time constraints, infrastructural shortcomings, and resistance to pedagogical change, particularly during early stages of blended learning (BL) adoption. In response to RQ3, teachers described a range of coping strategies and supportive resources. These included reusing and adapting digital materials, employing AI-supported tools for automated assessment, developing digital skills through peer support, and implementing structured classroom routines. Institutional resources, such as reliable IT support, modular professional development, collaborative planning, clear BL guidelines, and leadership support, functioned as key job resources that buffered workload pressures and supported sustainable BL practices. This study contributes to the literature by applying the JD-R model to the K-12 blended learning context, offering a theoretically grounded account of how workload pressures and supports interact in teachers’ daily work. Beyond documenting challenges, the findings generate actionable insights for school leaders and policymakers, highlighting the need for systemic workload-sensitive BL implementation, structured collaborative planning, and sustained professional development aligned with instructional realities. By reframing blended learning through a job demands-resources perspective, this study advances understanding of sustainable technology integration in compulsory education.

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https://doi.org/https://doi.org/10.34190/ejel.24.1.4465

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@article{zhao2026,
  title        = {{Understanding Teacher Workload in Blended Learning: Insights Through the Job Demands-Resources Model}},
  author       = {Zhao Cheng et al.},
  journal      = {Electronic Journal of e-Learning},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.34190/ejel.24.1.4465},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.