Digital inclusion of disabled students in Open and Distance e-Learning
Bibliographic record
Abstract
The digital inclusion of students with learning disabilities in Open and Distance e-Learning (ODeL) requires more than just providing; it must also focus on empowering these students. This paper argues that true inclusion enables students to actively engage in processes that address their unique needs and foster responsibility for participation and success in a digital learning environment. The paper emphasises that empowerment goes beyond providing the necessary technology and internet connectivity. It involves training students in digital technologies, mentoring them to build confidence, encouraging self-advocacy, exercising agency in their learning, involving them in plans and processes that aim to address their needs, and providing accessible information and inclusive pedagogy. Using the Capability Approach, a human development framework that emphasises providing people with alternative practical opportunities to achieve valued outcomes, this conceptual paper offers a better and more nuanced understanding of empowerment in the context of disability and digital inclusion. The Capability Approach allows an assessment of how well a person can function and achieve meaningful outcomes with the available resources, which is critical to empowering students with learning disabilities and addressing their specific needs. This research seeks to address technological, social, and practical barriers and offers practical recommendations for ODeL institutions to enhance the digital inclusion of students with learning disabilities. The study is significant in its potential to inspire positive change in how well ODeL institutions support students with learning disabilities to ensure that they are not only included, but also empowered to succeed in their studies. Keywords: access, digital inclusion, empowerment, inclusion, learning disabilities, Open and Distance e-Learning L'inclusion numerique des etudiants en situation de handicap d'apprentissage dans la formation ouverte et a distance (FOAD) : Aller au-dela de l'acces vers l'autonomisation Resume : L'inclusion numerique des etudiants en situation de handicap d'apprentissage dans la formation ouverte et a distance (FOAD) ne se limite pas a leur donner acces a des ressources numeriques; elle doit egalement viser leur autonomisation. Cet article soutient qu'une veritable inclusion permet aux etudiants de s'engager activement dans des processus repondant a leurs besoins specifiques, tout en leur conferant la responsabilite de leur participation et de leur reussite dans un environnement d'apprentissage numerique. L'autonomisation va au-dela de la mise a disposition des technologies et de la connectivite Internet. Elle implique la formation aux technologies numeriques, le mentorat pour renforcer la confiance en soi, l'encouragement a l'auto-representation, le developpement de l'agentivite dans l'apprentissage, l'implication des etudiants dans l'elaboration des plans et processus visant a repondre a leurs besoins, ainsi que l'acces a une information accessible et a des approches pedagogiques inclusives. S'appuyant sur l'Approche par les Capabilites, un cadre de developpement humain mettant l'accent sur la mise a disposition d'opportunites pratiques alternatives pour atteindre des resultats valorises, cet article conceptuel propose une comprehension plus nuancee de l'autonomisation dans le contexte du handicap et de l'inclusion numerique. Cette approche permet d'evaluer dans quelle mesure une personne peut fonctionner et atteindre des resultats significatifs avec les ressources disponibles, un aspect crucial pour autonomiser les etudiants en situation de handicap d'apprentissage et repondre a leurs besoins specifiques. Cette recherche vise a identifier les obstacles technologiques, sociaux et pratiques et a formuler des recommandations concretes pour les institutions de formation ouverte et a distance (FOAD) afin d'ameliorer l'inclusion numerique des etudiants en situation de handicap d'apprentissage. Cette etude est particulierement importante en raison de son potentiel a inspirer des changements positifs dans la maniere dont ces institutions soutiennent ces etudiants, en veillant a ce qu'ils ne soient pas seulement inclus, mais egalement rendus plus autonomes pour reussir leurs etudes. Mots-cles : acces, inclusion numerique, autonomisation, inclusion, handicap d'apprentissage, formation ouverte et a distance (FOAD).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".