Going Global: A Binational Approach to Innovative Teaching and Learning with Technology
Bibliographic record
Abstract
Postsecondary institutions across the world have rapidly adapted to the need for online teaching and learning modalities as a result of the global pandemic that began in early 2020. A new dynamic has emerged with technology being the platform of the new virtual classroom, providing significant opportunities to explore internationalization. This article explores a collaborative venture between two institutions, Niagara College in Canada and Munster Technological University in Ireland, who leveraged a strong binational partnership to deliver a digital learning experience through a series of workshops delivered to human resources students. Through an action research lens, it was found that this innovative approach to teaching and learning supported students’ development as human resources professionals, helped them to gain a deeper understanding of biases and how neuroscience influences decision-making, supported the development of cross-cultural competencies, and provided students with an opportunity for global mobility through a digital learning experience.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.045 |
| Scholarly communication | 0.023 | 0.015 |
| Open science | 0.003 | 0.031 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".