Developing Universal Design for Learning Asynchronous Training in an Academic Library
Bibliographic record
Abstract
This paper explores the design and initial implementation of online training modules for Universal Design for Learning in the context of academic libraries. Academic libraries are shifting away from the provision of resources and toward actively providing instruction and engaging with learners. The COVID-19 pandemic saw a quick transition from many in-person resources to virtual resources. Ensuring librarians are equipped to support learners in this manner is crucial. The goal of this paper was to determine how best to assist academic librarians with developing effective online resources. To achieve this goal, we conducted interviews with academic librarians. After consulting the literature and collecting information from academic librarians, we identified four key concepts for providing valuable instruction and designing material. The four themes included making content accessible, usable, meaningful, and reliable. We then developed four online training modules using Articulate Rise. The modules provide a foundation for aiding academic librarians with their teaching practice and engaging with a broad range of learners. These modules quickly demonstrated their value in the library context, and future testing, assessing, and iterating will enable their continuous improvement via institutional and cross-institutional collaboration.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".