A Universal Design for Success: A Mixed-methods Case Study of a First-year BScN Course
Bibliographic record
Abstract
No single universal learner type exists, however historically, pedagogical practices in higher education have focused on meeting the learning needs of an average or typical student. The purpose of this study was to describe the manner and extent in which a course, designed using Universal Design for Learning (UDL) principles, provided an inclusive learning environment to a diverse population of first-year baccalaureate nursing students. Co-instructors redesigned a large in-person and place-based course using theoretical and structural principles of UDL to remove potential learning barriers and promote authentic inclusion of all students. A convergent mixed methods descriptive case study design was used to gather qualitative and quantitative data. A purposive convenience sample was drawn from a class of 223 full and part time nursing students. Qualitative data were collected through an end of semester focus group interview (n=12) and research team meetings. Quantitative data collection involved using the Inclusive Teaching Strategies Inventory-Students survey questionnaire (Gawronski, et al., 2016) at course completion (n=32), and document review of final grades (n= 206). The use of UDL principles in the design and teaching supported the needs and abilities of learners with a variety of learning preferences and experience. Students experienced a more inclusive environment with fewer barriers to learning. Large in-person and place-based post-secondary courses designed using the key tenets of UDL successfully support inclusivity of the needs of learners with diverse backgrounds, abilities, and preferences, by proactively reducing barriers in the learning environment.
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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 | 0.042 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".