Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,014 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».