Universal design for learning: an integrative literature review and integrated model for organizational training and development
Bibliographic record
Abstract
Learning disabilities are common among employees, and they have important training and development-related implications for human resource development. However, there is limited knowledge in the field of human resource development to guide theory and practice in this area. In contrast, the field of education has made significant theoretical and practical strides to address the needs of learners with learning disabilities. Universal design for learning (UDL) is one of the most prominent developments in this area. Recognizing the need for more work on the inclusion of employees with learning disabilities and acknowledging the significant developments on this topic in the field of education, in this study, we conducted an integrative literature review of UDL research in education (N = 41). Using our findings from research conducted in the field of education, we proposed a new integrative model of UDL for organisational training and development. Our model identifies human resource development professionals, leaders, and supervisors, co-workers, and employees with learning disabilities as key actors in the UDL. Our model also details the types of inputs, activities, and products needed by organisations to achieve desirable short, medium, and long-term outcomes.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".