"It will never be my first choice to do an online course": Examining experiences of Indigenous learners online in Canadian post-secondary educational institutions
Bibliographic record
Abstract
In the era of Truth and Reconciliation (TRC), educational administrators have a responsibility to answer the Calls to Action to transform post-secondary education, to increase access for Indigenous learners and decreasing education disparity between Indigenous and non-Indigenous learners (TRC, 2015a). If distance education is an option for expanding educational opportunities, online learning environments should be scrutinized to ensure learner engagement and meaningful support for Indigenous students. This thesis uses a Community of Inquiry (CoI) (Garrison, Anderson & Archer, 2000) framework to examine existing literature and to frame the voices of 21 Indigenous participants about their experiences of supports, preferences, and online best practices. By exploring, understanding and incorporating what may be unique preferences, cultures, languages, worldviews, and ways of knowing, mechanisms to transform distance learning environments to improve engagement for Indigenous students can be identified. With the aim of synthesizing potential findings with online best practices, it may be possible to transform online delivery and development to provide a rich educational experience for students.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.036 | 0.018 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".