IMPROVING THE TECHNOLOGICAL COMPONENT OF A PEDAGOGICAL MODEL IN INFORMATICS EDUCATION THROUGH DIGITAL MANAGEMENT MECHANISMS: AN ADAPTIVE WEB-BASED PLATFORM APPROACH
DOI:
https://doi.org/10.55640/Keywords:
adaptive learning, digital pedagogy, informatics education, digital management mechanisms, automated assessment, competency-based education, personalized learning.Abstract
The rapid digital transformation of education necessitates the modernization of pedagogical models through the integration of intelligent technological components. This study focuses on improving the technological component of a pedagogical model in Informatics education by incorporating digital management mechanisms within an adaptive web-based platform. The proposed approach integrates adaptive task generation, real-time diagnostic monitoring, automated assessment systems, and individualized learning trajectory design into a unified digital environment.
A quasi-experimental research design was employed to evaluate the effectiveness of the enhanced model. The platform enables continuous performance tracking, competency-based assessment, and dynamic adjustment of learning content according to students’ cognitive progress. Comparative analysis between control and experimental groups demonstrates statistically significant improvements in academic performance, learner engagement, and algorithmic competency development.
The findings confirm that embedding digital management mechanisms into the technological component of pedagogical models substantially increases instructional efficiency and supports personalized learning in Informatics education. The study contributes to the advancement of adaptive digital pedagogy and offers a scalable framework for technology-enhanced learning environments in higher education.
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References
1. Zawacki-Richter, O., et al. (2019). Systematic review of research on artificial intelligence applications in higher education.
https://doi.org/10.1186/s41239-019-0171-0
2. Siemens, G. (2013). Learning analytics: The emergence of a discipline.
https://doi.org/10.18608/jla.2013.11.3
3. Hattie, J. (2012). Visible Learning for Teachers: Maximizing Impact on Learning.
https://doi.org/10.4324/9780203181522
4. Holmes, W., et al. (2019). Artificial Intelligence in Education: Challenges and Opportunities for Sustainable Development.
https://doi.org/10.1787/eedfee77-en
5. Ifenthaler, D., & Yau, J. Y. K. (2020). Utilising learning analytics for study success: Reflections on current empirical findings.
https://doi.org/10.1007/s12528-019-09239-y
6. Drachsler, H., & Greller, W. (2016). Privacy and analytics in educational systems.
https://doi.org/10.18608/jla.2016.32.6
7. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences.
https://doi.org/10.4324/9780203771587
8. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning.
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