1. DSpace KNU
  2. Наукові публікації
  3. 5. Факультет інформаційних технологій
  4. Кафедра професійної та соціально-гуманітарної освіти
Будь ласка, використовуйте цей ідентифікатор, щоб цитувати або посилатися на цей матеріал: http://ds.knu.edu.ua/jspui/handle/123456789/9316
Повний запис метаданих
Поле DCЗначенняМова
dc.contributor.authorSemerikov, Serhiy-
dc.contributor.authorNechypurenko, Pavlo-
dc.contributor.authorVakaliuk, Tetiana-
dc.contributor.authorMintii, Iryna-
dc.contributor.authorFadieieva, Liliia-
dc.date.accessioned2026-07-12T10:51:12Z-
dc.date.available2026-07-12T10:51:12Z-
dc.date.issued2026-06-16-
dc.identifier.citationDevelopment and validation of an adaptive learning readiness assessment framework for Moodle courses / Serhiy Semerikov, Pavlo Nechypurenko, Tetiana Vakaliuk, Iryna Mintii, Liliia Fadieieva // Journal of New Approaches in Educational Research. – 2026. – Vol. 15. – Iss. 1. – Article 19. – DOI : https://www.doi.org/10.1007/s44322-026-00069-wuk_UA
dc.identifier.issn2254-7339-
dc.identifier.urihttps://www.doi.org/10.1007/s44322-026-00069-w-
dc.identifier.urihttp://ds.knu.edu.ua/jspui/handle/123456789/9316-
dc.descriptionAfini Normadhi, N. B., Shuib, L., Md Nasir, H. N., Bimba, A., Idris, N., & Balakrishnan, V. (2019). Identification of personal traits in adaptive learning environment: Systematic literature review. Computers and Education, 130, 168–190. https://doi.org/10.1016/j.compedu.2018.11.005 Aher, G. V., Arriaga, R. I., & Kalai, A. T. (2023). Using large language models to simulate multiple humans and replicate human subject studies. In: A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, & J. Scarlett (Eds.). International Conference on Machine Learning, ICML 2023, 23–29 July 2023, Honolulu, Hawaii, USA (pp. 337–371). Proceedings of Machine Learning Research, PMLR. https://proceedings.mlr.press/v202/aher23a.html Al-Rikabi, Y. K., & Montazer, G. A. (2024). Designing an E-learning readiness assessment model for Iraqi universities employing Fuzzy Delphi method. Education and Information Technologies, 29(2), 2217–2257. https://doi.org/10.1007/s10639-023-11889-0 Alajlani, N., Crabb, M., & Murray, I. (2024). A systematic review in understanding stakeholders’ role in developing adaptive learning systems. Journal of Computers in Education, 11(3), 901–920. https://doi.org/10.1007/s40692-023-00283-x Alwadei, F. H., Brown, B. P., Alwadei, S. H., Harris, I. B., & Alwadei, A. H. (2023). The utility of adaptive eLearning data in predicting dental students’ learning performance in a blended learning course. International Journal of Medical Education, 14, 137–144. https://doi.org/10.5116/ijme.64f6.e3db Argyle, L. P., Busby, E. C., Fulda, N., Gubler, J. R., Rytting, C., & Wingate, D. (2023). Out of one, many: Using language models to simulate human samples. Political Analysis, 31(3), 337–351. https://doi.org/10.1017/pan.2023.2 Badhe, V., Banerjee, G., & Dasgupta, C. (2021). Design guidelines for scaffolding self-regulation in personalized adaptive learning (PAL) systems: A systematic review. In: MMT. Rodrigo., Ed.). 29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings (Vol. 1, pp. 372–380). Asia-Pacific Society for Computers in Education. https://library.apsce.net/index.php/ICCE/article/view/4170 Bevan, R., Pacey, V., Fuller, J., Lawton, V., & Jones, T. (2023). Physiotherapy student perceptions of the feasibility, acceptability and appropriateness of continuous adaptive assessment. Assessment and Evaluation in Higher Education, 48(8), 1268–1282. https://doi.org/10.1080/02602938.2023.2201667 Bimba, A. T., Idris, N., Al-Hunaiyyan, A., Ibrahim, S. U., Mustafa, N., & Supa’at, I., et al. (2021). The effects of adaptive feedback on student’s learning gains. International Journal of Advanced Computer Science & Applications, 12(7), 68–80. https://doi.org/10.14569/IJACSA.2021.0120709 Bisbee, J., Clinton, J., Dorff, C., Kenkel, B., & Larson, J. M. (2024). Synthetic replacements for human survey data? The perils of large language models. Political Analysis, 32(4), 401–416. https://doi.org/10.1017/pan.2024.5 Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., & Oxley, E., et al. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21, 4. https://doi.org/10.1186/s41239-023-00436-z Cavanagh, T., Chen, B., Lahcen, R. A. M., & Paradiso, J. (2020). Constructing a design framework and pedagogical approach for adaptive learning in higher education: A practitioner’s perspective. The International Review of Research in Open and Distributed Learning, 21(1), 173–197. https://doi.org/10.19173/irrodl.v21i1.4557 Chi, M. T. H., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49(4), 219–243. https://doi.org/10.1080/00461520.2014.965823 Dainton, C., Winstone, N., Klaver, P., & Opitz, B. (2020). Utility of feedback has a greater impact on learning than ease of decoding. Mind, Brain, and Education, 14(2), 139–145. https://doi.org/10.1111/mbe.12227 Dillion, D., Tandon, N., Gu, Y., & Gray, K. (2023). Can AI language models replace human participants? Trends in Cognitive Sciences, 27(7), 597–600. https://doi.org/10.1016/j.tics.2023.04.008 Dziuban, C., Howlin, C., Moskal, P., Cj, C., Parker, L., & Campbell, M. (2018). Adaptive learning: A stabilizing influence across disciplines and universities. Online Learning Journal, 22(3), 7–39. https://doi.org/10.24059/olj.v22i3.1465 Ezzaim, A., Dahbi, A., Haidine, A., & Aqqal, A. (2024). The impact of implementing a Moodle plug-in as an AI-based adaptive learning solution on learning effectiveness: Case of Morocco. International Journal of Interactive Mobile Technologies, 18(1), 133–149. https://doi.org/10.3991/ijim.v18i01.46309 Fadieieva, L., & Semerikov, S. (2024). Exploring the interplay of Moodle tools and student learning outcomes: A composite-based structural equation modelling approach. In E. Faure, Y. Tryus, T. Vartiainen, O. Danchenko, M. Bondarenko, & C. Bazilo, et al. (Eds.), Information Technology for Education, Science, and Technics vol. 222 of Lecture Notes on Data Engineering and Communications Technologies (pp. 418–435). Cham: Springer Nature Switzerland. Fadieieva, L. O. (2021). Enhancing adaptive learning with Moodle’s machine learning. Educational Dimension, 5, 1–7. https://doi.org/10.31812/ed.625 Hämäläinen, P., Tavast, M., & Kunnari, A. (2023). Evaluating large language models in generating synthetic HCI research data: A case study. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems CHI’23 (pp. 433). Association for Computing Machinery, New York, NY, USA. Holthaus, M., Hirt, F., & Bergamin, P. (2018). Simple and effective: An adaptive instructional design for mathematics implemented in a standard learning management system. In Proceedings of the 2nd International Conference on Computer-Human Interaction Research and Applications - CHIRA INSTICC, SciTePress (pp. 116–126). Jordan, C. (2013). Comparison of international baccalaureate (IB) chemistry students’ preferred vs actual experience with a constructivist style of learning in a Moodle e–learning environment. International Journal for Lesson and Learning Studies, 2(2), 155–167. https://doi.org/10.1108/20468251311323397 Kabudi, T., Pappas, I., & Olsen, D. H. (2021). AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence, 2, 100017. https://doi.org/10.1016/j.caeai.2021.100017 Khalifeh, F., Santiago, R., & Palau, R. (2026). Redefining personalized learning in the artificial intelligence era: An updated systematic review from 2019 to 2025. Smart Learning Environments, 13, 19. https://doi.org/10.1186/s40561-026-00440-6 Khan, F. A., Shahzad, F., & Altaf, M. (2019). Fuzzy based approach for adaptivity evaluation of web based open source learning management systems. Cluster Computing, 22, 7099–7109. https://doi.org/10.1007/s10586-017-1036-8 Krahn, T., Kuo, R., & Chang, M. (2023). Personalized study guide: A Moodle plug-in generating personal learning Path for students. In C. Frasson, P. Mylonas, & C. Troussas (Eds.), Augmented intelligence and intelligent tutoring systems, vol. 13891 LNCS of lecture notes in computer science (pp. 333–341). Cham: Springer Nature Switzerland. Li, F., He, Y., & Xue, Q. (2021). Progress, challenges and countermeasures of adaptive learning: A systematic review. Educational Technology & Society, 24(3), 238–255. https://www.jstor.org/stable/27032868 Limongelli, C., Sciarrone, F., Temperini, M., & Vaste, G. (2010). A module for adaptive course configuration and assessment in Moodle. In MD. Lytras, P. Ordonez De Pablos, A. Ziderman, A. Roulstone, H. Maurer, & JB. Imber (Eds.), Knowledge management, information systems, E-learning, and sustainability research, vol. 111 CCIS of communications in computer and information science (pp. 267–276). Berlin, Heidelberg: Springer Berlin Heidelberg. Lubchak, V., Kupenko, O., & Kuzikov, B. (2012). Approach to dynamic assembling of individualized learning paths. Informatics in Education, 11(2), 213–225. https://doi.org/10.15388/infedu.2012.11 Lynn, M. R. (1986). Determination and quantification of content validity. Nursing Research, 35(6), 382–386. https://doi.org/10.1097/00006199-198611000-00017 Marienko, M. V., Markova, O. M., & Semerikov, S. O. (2026). AI literacy in secondary education: Framework, assessment, and professional development in the Ukrainian context. Computers and Education: Artificial Intelligence, 10, 100605. https://doi.org/10.1016/j.caeai.2026.100605 Martin, F., Chen, Y., Moore, R. L., & Westine, C. D. (2020). Systematic review of adaptive learning research designs, context, strategies, and technologies from 2009 to 2018. Educational Technology Research and Development, 68(4), 1903–1929. https://doi.org/10.1007/s11423-020-09793-2 Mejeh, M., Sarbach, L., & Hascher, T. (2024). Effects of adaptive feedback through a digital tool – a mixed-methods study on the course of self-regulated learning. Education and Information Technologies, 29(14), 1–43. https://doi.org/10.1007/s10639-024-12510-8 Mirata, V., & Bergamin, P. (2019). Developing an implementation framework for adaptive learning: A case study approach. In Proceedings of the European Conference on e-Learning, ECEL Vol. 2019-November, pp. 668–673. Mirata, V., Hirt, F., Bergamin, P., & van der Westhuizen, C. (2020). Challenges and contexts in establishing adaptive learning in higher education: Findings from a Delphi study. International Journal of Educational Technology in Higher Education, 17(1), 32. https://doi.org/10.1186/s41239-020-00209-y Moreno-Marcos, P. M., Barredo, J., Muñoz-Merino, P. J., & Delgado Kloos, C. (2023). Statoodle: A learning analytics tool to analyze Moodle students’ actions and prevent cheating. In O. Viberg, I. Jivet, P. Muñoz-Merino, M. Perifanou, & T. Papathoma (Eds.), Responsive and sustainable educational futures, vol. 14200 LNCS of lecture notes in computer science (pp. 736–741). Cham: Springer Nature Switzerland. Neumann, A. T., Yin, Y., Sowe, S., Decker, S., & Jarke, M. (2025). An LLM-driven chatbot in higher education for databases and Information systems. IEEE Transactions on Education, 68(1), 103–116. https://doi.org/10.1109/TE.2024.3467912 Park, J. S., Zou, C. Q., Kamphorst, J., Egan, N., Shaw, A., & Hill, B. M., et al. (2026). LLM agents grounded in self-reports enable general-purpose simulation of individuals. Penfield, R. D., & Giacobbi Jr, P. R. (2004). Applying a score confidence interval to Aiken’s item content-relevance index. Measurement in Physical Education and Exercise Science, 8(4), 213–225. https://doi.org/10.1207/s15327841mpee0804_3 Polit, D. F., & Beck, C. T. (2006). The content validity index: Are you sure you know what’s being reported? Critique and recommendations. Research in Nursing and Health, 29(5), 489–497. https://doi.org/10.1002/nur.20147 Prada Segura, J. A. (2026). Implementing adaptive learning in Moodle with artificial intelligence. In T. Guarda, F. Portela, MF. Augusto, & JR. Coronado-Hernández (Eds.), Advanced research in technologies, information, innovation and sustainability communications in computer and information science (pp. 268–282). Cham: Springer Nature Switzerland. Pukkhem, N., Evens, M. W., & Vatanawood, W. (2006). An estimation function for selecting the suitable learning objects in adaptive education systems. In EISTA 2006 - 4th Int. Conf. on Education and Information Systems: Technologies and Applications, Jointly with SOIC 2006 - 2nd Int. Conf. on SOIC and PISTA 2006 - 4th Int. Conf. on PISTA, Proceedings (Vol. 1, pp. 123–128). Rideout, C. A. (2018). Students’ choices and achievement in large undergraduate classes using a novel flexible assessment approach. Assessment and Evaluation in Higher Education, 43(1), 68–78. https://doi.org/10.1080/02602938.2017.1294144 Rivera Muñoz, J. L., Ojeda, F. M., Aparicio Jurado, D. L., Puga Peña, P. F., Martel Carranza, C. P., & Berríos, H. Q., et al. (2022). Systematic review of adaptive learning technology for learning in higher education. Eurasian Journal of Educational Research, 2022(98), 221–233. https://ejer.com.tr/manuscript/index.php/journal/article/view/707 Semerikov, S. O., Nechypurenko, P. P., Vakaliuk, T. A., Mintii, I. S., & Fadieieva, L. O. (2025). Differential effects of Moodle course design on student subpopulations: Advancing personalized learning in higher education. Smart Learning Environments, 12(1), 46. https://doi.org/10.1186/s40561-025-00400-6 Semerikov, S. O., Nechypurenko, P. P., Vakaliuk, T. A., Mintii, I. S., & Fadieieva, L. O. (2026). Multivariate analysis of Moodle components and grade distribution patterns for adaptive learning environments. Discover Education, 5(1), 15. https://doi.org/10.1007/s44217-025-01024-1 Smyrnova-Trybulska, E., Morze, N., & Varchenko-Trotsenko, L. (2022). Adaptive learning in university students’ opinions: Cross-border research. Education and Information Technologies, 27(5), 6787–6818. https://doi.org/10.1007/s10639-021-10830-7 Tamo-Vargas, G., Quispe-Pari, E., Bedregal-Alpaca, N., Guevara, K., Delgado-Barra, L., & Laura-Ochoa, L. (2023). Design and development of a Moodle Plugin for the adaptation of the teaching-learning process through graded grading constraints [Diseño y desarrollo de un Plugin en Moodle para la adaptación de proceso enseñanza-aprendizaje a través de restricciones de calificación escalonada]. RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao, 2023(E59), 338–351. https://dialnet.unirioja.es/servlet/articulo?codigo=10079702 Vásquez-Bermúdez, M., Aguirre-Munizaga, M., & Hidalgo-Larrea, J. (2023). Analysis of coI presence indicators in a Moodle forum using unsupervised learning techniques. In R. Valencia-García, M. Bucaram-Leverone, J. Del Cioppo-Morstadt, N. Vera-Lucio, & PH. Centanaro-Quiroz (Eds.), Technologies and innovation, vol. 1873 CCIS of communications in computer and information science (pp. 27–38). Cham: Springer Nature Switzerland. Vitez, A. (2022). Course module instances report. https://moodle.org/plugins/report_coursemodstats Wu, C. H., Chen, Y. S., & Chen, T. C. (2018). An adaptive e-learning system for enhancing learning performance: Based on dynamic scaffolding theory. Eurasia Journal of Mathematics, Science and Technology Education, 14(3), 903–913. https://doi.org/10.12973/ejmste/81061 Xie, H., Chu, H. C., Hwang, G. J., & Wang, C. C. (2019). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers and Education, 140, 103599. https://doi.org/10.1016/j.compedu.2019.103599 Zahorodko, P. V., & Semerikov, S. O. (2026, Feb). Integrating agile methodologies and AI-assisted learning in web programming education: A theoretical framework for CS curriculum transformation. Discover Education, 5(1), 166. https://doi.org/10.1007/s44217-026-01179-5 Zang, J. (2024). Design of students’ personalized learning paths under the integration and development of technology and basic education. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns-2024-1737 Zheng, L., Chiang, W., Sheng, Y., Zhuang, S., Wu, Z., & Zhuang, Y., et al (2023). Judging LLM-as-a-judge with MT-bench and chatbot arena. In: A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.). Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023. http://papers.nips.cc/paper_files/paper/2023/hash/91f18a1287b398d378ef22505bf41832-Abstract-Datasets_and_Benchmarks.htmluk_UA
dc.description.abstractPurpose While Moodle is widely adopted in higher education, institutions struggle to leverage its features for adaptive learning. This study develops and validates the Adaptive Learning Readiness Assessment Framework (ALRAF), a course-level diagnostic instrument for evaluating a Moodle course’s structural capability to support adaptive learning experiences. Design We employ a quantitative cross-sectional design coupled with a novel Multi-LLM Synthetic Expert Consensus (MLSEC) protocol for content-validity evidence. ALRAF was developed through literature synthesis grounded in the ICAP framework (Chi and Wylie, Chi, Educational Psychologist49, 2014) and validated by a 40-panelist synthetic expert panel constructed across eight large-language-model providers and five stratified expert personas using two pre-registered Delphi rounds with falsifiable decision rules (-CVI 0.78, -Aiken 0.70, modified ). The validated framework was applied to a Moodle 3.8.2 dataset of 985 courses delivered at Kryvyi Rih State Pedagogical University (Ukraine) across 2020–2022. Findings The synthetic panel converged on six dimensions: Content Variety, Interaction Diversity, Assessment Flexibility, Learning Path Personalization, Feedback Mechanisms, and the panel-proposed AI & Data-Driven Adaptivity Integration (ADAI). The six-dimensional correlated significantly – but negatively – with the proportion of high grades (): Pearson , and positively with low grades (both ). This inverse relationship runs counter to what would be expected if ALRAF directly indexed pedagogical quality, and reframes ALRAF as a measure of structural readiness rather than learning effectiveness; we interpret the sign in Sect. 6 in terms of course-difficulty and compensatory-engineering effects. Faculty differences were significant (ANOVA , , , ). A multiple-regression model controlling for educational level, form of education, and faculty achieved adjusted (, ). The framework reveals strong implementation of content variety but near-zero readiness in learning-path personalization and AI integration – itself a notable institutional finding. Contribution Methodologically, the study introduces MLSEC as a transparent AI-augmented approach to rubric content validation, with synthetic-panel limitations explicitly disclosed. Substantively, ALRAF provides a replicable structural-readiness index whose correlations with student outcomes are non-trivial in direction and magnitude; it helps institutions identify capability gaps (especially around AI integration) without claiming to forecast student success.uk_UA
dc.language.isoenuk_UA
dc.publisherSpringeruk_UA
dc.subjectAdaptive learninguk_UA
dc.subjectMoodleuk_UA
dc.subjectReadiness assessmentuk_UA
dc.subjectFramework developmentuk_UA
dc.subjectHigher educationuk_UA
dc.subjectStudent outcomesuk_UA
dc.subjectContent validityuk_UA
dc.subjectSynthetic expert paneluk_UA
dc.subjectLarge language modelsuk_UA
dc.titleDevelopment and validation of an adaptive learning readiness assessment framework for Moodle coursesuk_UA
dc.typeArticleuk_UA
dc.identifier.doihttps://www.doi.org/10.1007/s44322-026-00069-w-
local.submitter.emailsemerikov@ccjourn...uk_UA
Розташовується у зібраннях:Кафедра професійної та соціально-гуманітарної освіти

Файли цього матеріалу:
Файл Опис РозмірФормат 
s44322-026-00069-w.pdf3.98 MBAdobe PDFПереглянути/Відкрити
Показати базовий опис матеріалу Перегляд статистики


Усі матеріали в архіві електронних ресурсів захищені авторським правом, всі права збережені.