More than accessibility: Universal design and universal design for learning in a STEM Laboratory
Bibliographic record
Abstract
Accessible teaching laboratories are limited as the number of students enrolling in STEM courses is lower (Sukhai et al, 2014). Universal Design (UD) and Universal Design for Learning (UDL) are frameworks to support the creation of learning environments, face-to-face and online, and curricula to accompany the learning. The environment and curriculum should be flexible and effective in meeting outcomes while maintaining the integrity of the course goals (CAST Inc., 2020). This project will focus on implementing or presenting possible implementations of UD and UDL to address students with sensory (hearing and visual impairments) and motor (mobility) impairment's ability to actively participate in the STEM laboratory. In addition, the project will concentrate on how to shift the focus from teaching to teaching with fewer barriers in the physical environment and presentation of content. CAST Inc. (2020). The UDL guidelines. Center for Applied Special Technology. http://udlguidelines.cast.org/?utm_medium=web&utm_campaign=none&utm_source=cast-about-udl Sukhai, M. A., Mohler, C. E., Doyle, T., Carson, E., Nieder, C., Levy-Pinto, D., Duffet, E., and Smith, F. (2014). Creating an accessible science laboratory environment for students with disabilities. Council of Ontario Universities 1-28
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".