Meeting the Challenge: E-Learning in Aboriginal Communities
Notice bibliographique
Résumé
One of the most significant challenges facing the education system in Canada is the provision of even a basic high school program to students in small isolated aboriginal communities scattered across remote areas of the provinces and territories of the country. Traditional forms of program delivery are often not possible in such locations due to available resources, lack of specialized teachers, and small numbers of students. Also, the use of residential schools that displace youth from their home communities is no longer acceptable. The situation is further exacerbated by well documented evidence of poor school performance (and thus low graduation rates) of aboriginal students when compared to the rest of Canada’s school age population. What has emerged as a delivery mode for high school students is a growing reliance on distance web-based education in the form of e-learning. Many proponents of this maintain that it has the potential to meet the needs of students in these small remote communities, but caution that student success is potentially challenged by numerous issues.A study was conducted in 2010 to examine how the various educational jurisdictions across Canada were addressing these issues associated with aboriginal student e-learning. Funded by the Social Sciences and Humanities Council of Canada through a Community University Research Alliance program, 25 key educators directly involved with the organization and delivery of e-learning to students in aboriginal communities were identified and extensively interviewed. All provinces and territories were represented in the study. Questions were based on the results of an initial set of interviews conducted on site with students, parents and educators residing in a group of small isolated aboriginal communities in Labrador, Canada where students were engaged in e-learning. Respondents across Canada were asked to identify the issues and challenges they faced and to describe how they were currently (or planning) to address these. Several key themes emerged from an analysis of the data that provided information on the best practices that had the potential to improve student success in terms of completing high school courses for graduation purposes. The challenges experienced in different locations were being addressed in a variety of ways depending on the e-learning policy in place, the resources available, and the actual e-learning mode being used (for example, a synchronous or asynchronous delivery or a blended learning approach). Some solutions were contextually based and designed to address a specific set of local circumstances. However it became apparent that a common set of underlying principles needed to be considered to maximise student success regardless of local circumstance. These will be discussed in the presentation from the perspective of how best practices are impacted by e-learning organization and delivery systems, student motivation, and effective communication among stakeholders.
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 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,028 | 0,010 |
| Communication savante | 0,009 | 0,007 |
| Science ouverte | 0,002 | 0,012 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».