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Будь ласка, використовуйте цей ідентифікатор, щоб цитувати або посилатися на цей матеріал: http://ds.knu.edu.ua/jspui/handle/123456789/9316
Назва: Development and validation of an adaptive learning readiness assessment framework for Moodle courses
Автори: Semerikov, Serhiy
Nechypurenko, Pavlo
Vakaliuk, Tetiana
Mintii, Iryna
Fadieieva, Liliia
Ключові слова: Adaptive learning
Moodle
Readiness assessment
Framework development
Higher education
Student outcomes
Content validity
Synthetic expert panel
Large language models
Дата публікації: 16-чер-2026
Видавництво: Springer
Бібліографічний опис: Development 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-w
Короткий огляд (реферат): Purpose 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.
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URI (Уніфікований ідентифікатор ресурсу): https://www.doi.org/10.1007/s44322-026-00069-w
http://ds.knu.edu.ua/jspui/handle/123456789/9316
ISSN: 2254-7339
Розташовується у зібраннях:Кафедра професійної та соціально-гуманітарної освіти

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