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Harshal Pawar
Independent Researcher
Maharashtra, India
Abstract
Adaptive learning tools leverage data-driven algorithms to tailor educational experiences to individual needs, offering significant potential for personalized teacher development. This manuscript explores the design, implementation, and impact of adaptive learning systems in professional development contexts for educators. An initial survey of 250 in-service teachers across urban and rural schools assessed their perceptions, usability experiences, and the effectiveness of adaptive modules in enhancing pedagogical skills. Findings indicate that adaptive tools significantly improve content mastery, self-efficacy, and instructional innovation. Participants reported deeper engagement with material due to real-time adjustments based on their performance, which fostered a sense of ownership over their learning journeys. The adaptive platform’s analytics features enabled administrators to identify common areas of difficulty, informing targeted group interventions and peer-collaboration activities. Moreover, contextual customization—such as language options, offline access for low-connectivity regions, and culturally relevant examples—enhanced relevance and uptake among diverse teacher populations. Key factors influencing success include system usability, quality of feedback, and seamless integration with existing professional learning communities. The study also highlights challenges such as initial resistance to technology adoption, the need for robust technical support, and the importance of continuous instructional design updates to maintain alignment with evolving pedagogical standards. Implications for scalable deployment, ongoing support structures, and future research directions are discussed to inform policy and practice in educator development programs.
Keywords
Adaptive learning tools; personalized teacher development; professional learning; survey research; educational technology
References
- https://www.researchgate.net/publication/344648191/figure/fig1/AS:946476834377782@1602669070825/The-structure-of-the-adaptive-learning-system.png
- https://i.pinimg.com/736x/69/8a/da/698adaa3eab95baf31aa42f6d3ddaf03.jpg
- Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. The Journal of the Learning Sciences, 4(2), 167–207.
- Brooke, J. (1996). SUS: A “quick and dirty” usability scale. In P. W. Jordan, B. Thomas, B. A. Weerdmeester, & A. L. McClelland (Eds.), Usability Evaluation in Industry (pp. 189–194). London, UK: Taylor & Francis.
- Desimone, L. M. (2011). A primer on effective professional development. Phi Delta Kappan, 92(6), 68–71.
- Dede, C. (2006). Online teacher professional development: Emerging models and methods. Harvard Education Press.
- Kalyuga, S. (2006). Instructional design for effective learning in computing. Idea Group Publishing.
- Kulik, J. A., & Fletcher, J. D. (2016). Effectiveness of intelligent tutoring systems: A meta‑analytic review. Review of Educational Research, 86(1), 42–78.
- Merrill, M. D. (2002). First principles of instruction. Educational Technology Research and Development, 50(3), 43–59.
- Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2014). Evaluation of evidence‑based practices in online learning: A meta‑analysis and review of online learning studies. U.S. Department of Education.
- Piech, C., Bassen, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L. J., & Sohl‑Dickstein, J. (2015). Deep knowledge tracing. In Advances in Neural Information Processing Systems (Vol. 28, pp. 505–513).
- Piech, C., Huang, J., & Sahami, M. (2015). Bayesian knowledge tracing using deep learner models. arXiv preprint arXiv:1506.02301.
- Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582–599.
- Ruiz‑Primo, M. A., & Furtak, E. M. (2006). Informal formative assessment and scientific inquiry: Exploring teachers’ practices and student learning. Educational Assessment, 11(3–4), 205–234.
- Schmid, R. F., Bernard, R. M., Borokhovski, E., Tamim, R., Abrami, P. C., Surkes, M., & Woods, J. (2014). The effects of technology use in postsecondary education: A meta‑analysis of classroom applications. Computers & Education, 72, 271–291.
- Sweller, J. (2011). Cognitive load theory. Psychology of Learning and Motivation, 55, 37–76.
- VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221.
