Adopting open educational resources and Universal Design for Learning principles in undergraduate nursing education in mental health
Bibliographic record
Abstract
This study will evaluate the adoption of open educational resources (OERs) and Universal Design for Learning in an undergraduate nursing course in the collaborative BScN program at Western University. OERs, as defined by United Nations Educational Scientific and Cultural Organization (2019) are “learning, teaching and research materials in any format and medium that reside in the public domain or are under copyright… that permit no-cost access… by others” (para. 1). OERs offer learners a number of benefits, specifically reduced cost from commercial text resources and improved accessibility (for example, through electronic platforms that allow text to audio or adjustable font size) (Kotsopoulos, 2022; Nguyen-Truong et al., 2019). Further, Universal Design for Learning principles facilitate course design that offers options for learning materials and formats, with consideration to accessibility principles, to enhance belonging in the learning space by meeting the learning needs of diverse learners.\nThe study was conducted in two phases. First, a comprehensive search was completed to identify relevant learning materials. Both OERs and publicly accessible learning materials were integrated into this course due to the limited availability of OERs specific to mental health nursing content. OER were evaluated using two evaluation criteria rubrics, with focus given to OERs that met accessibility principles. At the completion of this course, all students in the course were invited to participate in a survey. The survey administered was a modified version of the PROJECT-OPEN survey, modified with permission from the survey author (Kotsopoulos, 2022). This study was approved by the institutional ethics board.\nPlease bring your own smart device to this session if you would be interested in interacting with the OER.\nKotsopoulos, D. (2022). Developing an undergraduate business course using open educational resources. The Canadian Journal for the Scholarship of Teaching and Learning, 13(1). https://doi.org/10.5206/cjsotlrcacea.2022.1.10992\nNguyen-Truong, C. K. Y., Graves, J. M., Enslow, E., & Williams-Gilbert, W. (2019). Academic and community–engaged approach to integrating open educational resources in population health course. Nurse Educator, 44(6), 300–303. https://doi.org/10.1097/NNE.0000000000000653\nUnited Nations Educational Scientific and Cultural Organization. (2019). Open educational resources. https://en.unesco.org/themes/building-knowledge-societies/oer
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".