Meeting the Challenge: E-Learning in Aboriginal Communities
Bibliographic record
Abstract
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.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.010 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".