Towards an Inclusive Pedagogy: Applying the Universal Design for Learning in an Introduction to History of Global Art Course in Ghana
Bibliographic record
Abstract
This convergent parallel mixed methods study was aimed at addressing the lack of empirical studies in the implementation of Universal Design for Learning (UDL) as an inclusive pedagogy in the Ghanaian higher education context. The overarching objective was to find out whether UDL has the potential in improving the learning processes and learning outcomes of the diverse students reading a History of Global Art course. Quantitative and qualitative data sets were garnered from 122 conveniently sampled students using an adapted version of the Inclusive Teaching Strategies Inventory-Students (ITSI-S) survey instrument. The findings of the study revealed that the UDL principles of multiple means of representation, multiple means of engagement and multiple means of action and expression impacted positively on students’ learning processes and outcomes. UDL assisted greatly in the development of collaborative, problem-solving, good time management and critical thinking skills, while increasing learners’ level of motivation. The study contends that though the UDL as an inclusive pedagogical approach requires a lot of dedication on the part of the instructor as well as a great deal of time and material resources, the accrued benefits of its implementation on the students’ learning processes and learning outcomes are far-reaching.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".