THE IMPACT OF DIGITAL LEARNING MATERIALS ON STUDENTS' ATTENTION: A NEURODIDACTIC ANALYSIS
DOI:
https://doi.org/10.46991/educ-21st-century.v8.i1.169Keywords:
neuro-pedagogy, learning analytics, cognitive load, learning management systems (LMS), digital educational content, mixed methods researchAbstract
The problem of students' cognitive resource depletion in digital learning management systems (LMS) remains a critical barrier to the effectiveness of higher education. The purpose of this study is to examine the impact of digital educational content architecture on extraneous cognitive load and academic achievement of students, based on the integration of learning analytics and neuropedagogy. An explanatory sequential mixed-methods research design was employed. At the quantitative stage, objective digital traces (Moodle LMS and Google Classroom log files) and survey data (F. Paas's scale) of 150 undergraduate students were analyzed. The qualitative stage included a thematic analysis of semi-structured interviews (n = 14, where n represents the number of students).
Quantitative results demonstrated that neurodidactic content optimization significantly reduces extraneous cognitive load (from 7.45 to 3.21 points, p < 0.001), while parallely increasing sustained attention span from 8.4 to 22.3 minutes. Multiple regression analysis confirmed that extraneous cognitive load is the strongest negative predictor of academic achievement (β = -.41 ).
Qualitative analysis revealed the phenomena of "navigational chaos" and cognitive claustrophobia, proving that strategies of visual chunking, guiding communication, and micromodular dopamine reinforcement are necessary to overcome them. The scientific novelty of the study lies in bridging the gap between cognitive load theory and learning analytics. An empirically substantiated conceptual model is proposed, proving that digital content architecture is not an aesthetic but a fundamental neuropedagogical tool. The practical significance of the work consists in the development of evidence-based standards for designing educational environments that ensure the cognitive resilience of students in data-driven learning conditions.
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References
Aleksanyan, A. (2025). Teachers views on pedagogical challenges during post-war return in Mosul. In Teacher and School Resilience in an Era of Uncertainty. Springer. https://doi.org/10.1007/978-3-031-93961-7
Anderson, J. R. (2009). Cognitive psychology and its implications (7th ed.). Worth Publishers.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Chen, C. M., & Wu, C. H. (2015). Effects of different video lecture types on sustained attention, emotion, cognitive load, and learning performance. Computers & Education, 80, 108–121. https://doi.org/10.1016/j.compedu.2014.08.015
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.
Dawson, S., Gašević, D., Siemens, G., & Joksimovic, S. (2014). Current state and future trends: A citation network analysis of the learning analytics field. In Proceedings of the First International Conference on Learning Analytics and Knowledge (pp. 231–240). Association for Computing Machinery. https://doi.org/10.1145/2567574.2567585
Gevorgyan, S., & Asatryan, S. (2025). Enhancing educational efficiency through data-driven learning: A case study of the Armenian State Pedagogical University (ASPU). Scientia Paedagogica Experimentalis. https://doi.org/10.57028/s62-269-z1091
Kahu, E. R. (2013). Framing student engagement in higher education. Studies in Higher Education, 38(5), 758–773. https://doi.org/10.1080/03075079.2011.598505
Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75–86. https://doi.org/10.1207/s15326985ep4102_1
Mayer, R. E. (2014). The Cambridge handbook of multimedia learning (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9781139547369
Paas, F. G. W. C. (1992). Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. Journal of Educational Psychology, 84(4), 429–434. https://doi.org/10.1037/0022-0663.84.4.429
Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380–1400. https://doi.org/10.1177/0002764213498851
Skulmowski, A., & Xu, K. M. (2021). Understanding cognitive load in digital and online learning: A new perspective on extraneous cognitive load. Educational Psychology Review, 34, 171–196. https://doi.org/10.1007/s10648-021-09624-7
Sweller, J. (2020). Cognitive load theory and educational technology. Educational Technology Research and Development, 68(1), 1–16. https://doi.org/10.1007/s11423-019-09701-3
Thomas, M. S. C., Ansari, D., & Knowland, V. C. P. (2019). Annual research review: Educational neuroscience: Progress and prospects. Journal of Child Psychology and Psychiatry, 60(4), 477–492. https://doi.org/10.1111/jcpp.12973
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