Designing Inclusive and Adaptive Content in Moodle: A Framework and a Case Study from Jordanian Higher Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it