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Development and validation of an adaptive learning readiness assessment framework for Moodle courses.

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Title: Development and validation of an adaptive learning readiness assessment framework for Moodle courses.
Authors: Semerikov, Serhiy1,2 (AUTHOR) semerikov@gmail.com, Nechypurenko, Pavlo1,3 (AUTHOR) acinonyxleo@gmail.com, Vakaliuk, Tetiana4,5 (AUTHOR) tetianavakaliuk@gmail.com, Mintii, Iryna6,7 (AUTHOR) irina.mintiy@gmail.com, Fadieieva, Liliia1 (AUTHOR) fadeyevaliliya@gmail.com
Source: Journal of New Approaches in Educational Research. 6/16/2026, Vol. 15 Issue 1, p1-34. 34p.
Subject Terms: *Preparedness, *Individualized instruction, *Educational evaluation, *Educational technology, *Artificial intelligence, *Test validity, *Instructional systems
Reviews & Products: Moodle (Computer software)
Abstract: 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. [ABSTRACT FROM AUTHOR]
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Database: Education Research Complete
Description
Abstract: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. [ABSTRACT FROM AUTHOR]
ISSN:22547339
DOI:10.1007/s44322-026-00069-w