Facilitating Learning Resource Retrieval for Students with Disabilities through an Ontology-Driven and Disability-Aware Virtual Learning Environment
Bibliographic record
Abstract
Existing virtual learning environments (VLEs) in educational institutions are not designed with the expectation that students with disabilities will use them. Consequently, retrieving relevant information by some students with disabilities is a challenging task. The focus of this study was to propose the design of VLEs to incorporate ontologies that facilitate information retrieval by students with disabilities in their learning, thus serving as a semantic web-based assistive technology in education. An Ontology-Driven Disability-Aware Personalised E-Learning System (ONTODAPS) was designed and then used to recommend specific learning materials to learners based on their learning goal and disability type. Preliminary results of the evaluation of ONTODAPS, by 30 students with disabilities, indicate that 70% of the participants found ONTODAPS to offer a better personalisation, better access to learning materials (68%) and is easier to use (63%) in retrieving learning materials than Sakai. Thus ONTODAPS serves as an assistive tool in their education through retrieval of relevant learning materials in a suitable format which is compatible with their disability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".