Designing Inclusive and Adaptive Content in Moodle: A Framework and a Case Study from Jordanian Higher Education
Bibliographic record
Abstract
Blended learning has introduced a more accessible and flexible teaching environment in higher education. However, ensuring that content is inclusive, particularly for students with learning difficulties, remains a challenge. This paper explores how Moodle, a widely adopted learning management system (LMS), can support inclusive and adaptive learning based on Universal Design for Learning (UDL) principles. A 16-week descriptive exploratory study was conducted with 70 undergraduate students during a software engineering fundamentals course at Philadelphia University in Jordan. The research combined weekly iterative focus groups, teaching reflections, and interviews with 16 educators to identify and address inclusion barriers. The findings highlight that the students responded positively to features such as conditional activities, flexible quizzes, and multimodal content. A UDL-based framework was developed to guide the design of inclusive Moodle content, and it was validated by experienced educators. To our knowledge, this is the first UDL-based framework designed for Moodle in Middle Eastern computing and engineering education. The findings indicate that Moodle features, such as conditional activities and flexible deadlines, can facilitate inclusive practices, but adoption remains hindered by institutional and workload constraints. This study contributes a replicable design model for inclusive blended learning and emphasizes the need for structured training, intentional course planning, and technological support for implementing inclusivity in blended learning environments. Moreover, this study provides a novel weekly iterative focus group methodology, which enables continuous course refinement based on evolving students’ feedback. Future work will look into generalizing the research findings and transferring the findings to other contexts. It will also explore AI-driven adaptive learning pathways within LMS platforms. This is an empirical study grounded in weekly student focus groups, educator interviews, and reflective teaching practice, offering evidence-based insights on the application of UDL in a real-world higher education setting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".