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Charu Pillai
Independent Researcher
Kerala, India
Abstract
Blended learning, an instructional approach that combines face-to-face and online pedagogies, has gained prominence in teacher education programs worldwide, particularly in the wake of global shifts toward digital instruction. This study investigates the readiness levels of newly recruited school teachers to implement blended learning in K–12 settings, examining both their attitudinal dispositions and practical competencies. Drawing on the Technology Acceptance Model (TAM) and the Substitution, Augmentation, Modification, and Redefinition (SAMR) framework, we surveyed 250 teachers across urban and rural districts using a validated Blended Learning Readiness Scale (BLRS). Quantitative analyses reveal moderate-to-high readiness scores overall (M = 3.8/5), with significant differences based on prior digital experience (t(248) = 5.12, p < .001) and access to institutional support (F(2,247) = 8.45, p < .001). Qualitative responses highlight perceived benefits—flexibility, improved learner engagement, and personalized pacing—and barriers—limited infrastructure, time constraints, and professional development gaps. In addition, teachers expressed a strong desire for mentorship and peer collaboration to navigate the complexities of blended lesson planning. Through mixed-methods integration, this study not only quantifies readiness but also surfaces contextual factors—such as leadership commitment and community-of-practice dynamics—that shape novices’ confidence and capacity. Findings suggest that while enthusiasm for blended learning is widespread among new teachers, targeted mentoring, ongoing technical training, scaffolded lesson-design templates, and context-sensitive resource allocation are critical to translate readiness into effective practice. Implications for teacher education programs and policymakers include embedding blended pedagogy modules within induction curricula, establishing structured peer-coaching networks, deploying on-demand micro-credentials for digital tools, and investing in robust, school-based digital ecosystems to foster sustainable, scalable innovation in instruction.
Keywords
Blended learning readiness; teacher induction; digital pedagogy; professional development; Technology Acceptance Model
References
- https://fda.fsu.edu/sites/g/files/upcbnu636/files/Media/Files/Academic%20Leadership%20Toolkit/profdevelopmentleavev2.jpg
- https://www.researchgate.net/publication/344453238/figure/fig1/AS:942198405214208@1601649013552/Source-Technology-Acceptance-Model-TAM.ppm
- Al-Azawei, A., Serenelli, F., & Lundqvist, K. (2016). Universal design for learning (UDL): A content analysis of peer‐reviewed journal papers from 2012 to 2015. Journal of the Scholarship of Teaching and Learning, 16(3), 39–56. https://doi.org/10.14434/josotl.v16i3.19295
- Bonk, C. J., & Graham, C. R. (Eds.). (2006). The handbook of blended learning: Global perspectives, local designs. Pfeiffer.
- Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
- Creswell, J. W., & Plano Clark, V. L. (2011). Designing and conducting mixed methods research (2nd ed.). SAGE.
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
- Ertmer, P. A., & Ottenbreit‑Leftwich, A. T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255–284. https://doi.org/10.1080/15391523.2010.10782551
- Graham, C. R. (2006). Blended learning systems: Definition, current trends, and future directions. In C. J. Bonk & C. R. Graham (Eds.), The handbook of blended learning (pp. 3–21). Pfeiffer.
- Graham, C. R., Woodfield, W., & Harrison, J. B. (2013). A framework for institutional adoption and implementation of blended learning in higher education. The Internet and Higher Education, 18, 4–14. https://doi.org/10.1016/j.iheduc.2012.09.003
- Halverson, L. R., Graham, C. R., Spring, K. J., Drysdale, J. S., & Henrie, C. R. (2014). A thematic analysis of the most highly cited scholarship in the first decade of blended learning research. The Internet and Higher Education, 20, 20–34. https://doi.org/10.1016/j.iheduc.2013.09.004
- Hung, M.-L., Chou, C., Chen, C.-H., & Own, Z.-Y. (2012). Learner readiness for online learning: Scale development and student perceptions. Computers & Education, 58(2), 838–852. https://doi.org/10.1016/j.compedu.2011.10.010
- Inan, F. A., & Lowther, D. L. (2010). Factors affecting technology integration in K–12 classrooms: A path model. Educational Technology Research and Development, 58(2), 137–154. https://doi.org/10.1007/s11423-009-9132-y
- Kitchenham, B., Budgen, D., & Brereton, O. P. (2009). Evidence‐based software engineering and systematic review. Proceedings of the 33rd International Conference on Software Engineering, 273–282. https://doi.org/10.1109/ICSE.2011.5768008
- Lim, C. P., & Chai, C. S. (2008). Teachers’ pedagogical beliefs and their planning and conduct of computer‐mediated classroom lessons. British Journal of Educational Technology, 39(5), 807–828. https://doi.org/10.1111/j.1467-8535.2007.00759.x
- Marshall, S., Shannon, D., & Goebel, V. (2017). Teaching, technology and teacher education during the COVID-19 pandemic: Stories from the field. Pacific Northwest National Laboratory.
- Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x
- Picciano, A. G. (2009). Blending with purpose: The multimodal model. Journal of the Research Center for Educational Technology, 5(1), 4–14.
- Puentedura, R. R. (2013). SAMR and TPCK: Intro to advanced practice. Ruben R. Puentedura. http://hippasus.com/blog/
- Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432–2440. https://doi.org/10.1016/j.compedu.2011.06.008
