Bibliographic record
Abstract
An accessible learning environment, ideally, is an environment in which every student has equal access and is assessed on an equal basis.Given the diversity of the student population this ideal may never be fully achieved.However, recognizing that the accessibility of learning, from the student perspective, is a function of many factors including cultural factors, learning styles, and learning disabilities, can aid us in developing learning environments that are more accessible to a broader range of students.The design of accessible environments has roots in Universal Design (UD), a concept largely developed in architecture by Ron Mace for the design of public spaces to be accessible for the broadest range of users to the greatest degree possible 1,2 .The underlying concept in UD is to incorporate accessibility into the design process from the start, rather than a posteriori.The goal of this approach is to increase the usability and potentially the functionality of a design for a diverse population.In addition to promoting usability, it has the potential to increase inclusivity for a greater number of users as well.UD in engineering is now generally widespread: ramps originally intended for wheelchair users are also usable by strollers and delivery-people, and textmessaging on cellphones assist both the hearing-impaired and those who want a silent conversation, and so forth.Additionally, the importance of this approach has played a role in the development of legislation (Americans with Disabilities Act 3 , Telecommunications Act 4 ) to mandate increased accessibility for a greater number of users.The principles of universal design advocate flexibility in use, intuitive design, and among others, equitability 1 .The applicability of UD in educational settings has also been explored to some degree.Universal Design in Education (UDE) is an offshoot that aims to develop a learning environment that is inclusive and accessible for a broad range of students.Various authors 5,6,7 have discussed the implications of UDE in terms of standardization, testing, creation of social relationships, and effective learning, among other ideas.Although it may be easy to see how UD benefits physical spaces, the application and effectiveness of UDE is more difficult to assess.In an engineering design we seek to achieve measurable objectives; in UDE however, the ever-changing diversity of the learning population demands that the learning environment be dynamic and inclusive.The challenge is to create a flexible environment while maintaining the integrity of the learning objectives, and measure the effectiveness of changes in terms of achieving accessibility.Engineering education is also a designed systemit follows a design process, has a specified user-group, contextualizes problems into manageable quantities, etc.and thus, it should also benefit from a universal design approach to increase inclusivity.The applicability of such pedagogy in instruction has been investigated in the fields of higher education and disability studies, but not in an engineering context 8 .The importance of understanding UDE in engineering is high because educators routinely contextualize problems to increase authenticity for the purposes of instruction and assessment.If the context of an engineering problem is not clear to the students, then it renders the instruction inaccessible.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".