Virtual Global Classrooms without Walls: Collaborative Opportunities for Higher Learning Engagement
Bibliographic record
Abstract
Educational research demonstrates that conventional pedagogies are no longer effective for actively engaging learners and produces learning isolation. Alternative interpretative approaches that foster learning and inquiry from multiple perspectives and contexts, while emanating from lived experiences engages and empowers students to explore and increase their understanding about sensitive topics (such as palliative care, leadership challenges, and practicing within vulnerable environments). Encouraging students to associate their personal experiences with evidenced-based knowledge and best practices in positive learning spaces supports innovative advancement of health care with a focus on culturally safe practices internationally. This can be accomplished via virtual global classrooms by using synchronous communication implementing video conferencing. The University of Calgary Qatar (UCQ), Doha has been active in this learning approach in both their undergraduate and graduate programs. A shared teaching/ learning experience was facilitated for the undergraduate Bachelor of Science Nursing (BScN) students in Doha, Qatar and the Rankin School of Nursing, Saint Francis Xavier University, Nova Scotia, Canada. This experience focused on building understanding of community nursing practices in both countries. In the UCQ master of nursing program, a palliative care course was offered for three spring sessions and a leadership course was offered for one spring session with synchronous communication via video conferencing between Nova Scotia and the Middle East. These virtual learning opportunities fostered relational and professional learning engagements that would not have been otherwise been possible. The authors contend that this work provides not only an opportunity for future higher learning engagements, but also a foundation for future global collaborative research and practice partnerships.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".