Repenser la définition des aides techniques en éducation / Rethinking the definition of assistive technology in education
Bibliographic record
Abstract
En éducation, un nombre croissant d’élèves avec incapacités ont recours aux aides techniques. Parallèlement, une littérature récente mais de plus en plus abondante étudie ce concept. En dépit de cet intérêt grandissant tant au niveau de la recherche que de la pratique liée à ces technologies, plusieurs indices soulignent la difficulté de distinguer entre ce concept et celui de technologies de l’information et de la communication (TIC) notamment en éducation. Les aides techniques sont-elles un concept distinct ou une variante des TIC? Quelles sont les conséquences d’une telle confusion conceptuelle ? Comment peut-on différencier ces deux concepts ? L’objectif du présent article est d’apporter des éléments de réponse à ces questions. In education, an increasing number of students with disabilities make use of assistive technologies (AT). Meanwhile, a recent but growing literature studies this concept. Despite this interest both in research and practice related to these technologies, several clues point to the difficulty of distinguishing it from and the information and communication technology (ICT), particularly in education. Are AT a distinct concept or a variation of ICT? What are the consequences of such a confusion? How can we differentiate these two concepts? The purpose of this article is to provide some answers to these questions.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".