Blended Learning and International Partnerships: Enhancing Education for a Globalised Future
Bibliographic record
Abstract
In this paper, we explore the transformative potential of blended teaching and learning and international short-term study trips, emphasising their role in fostering student engagement and preparing learners for global challenges in an interconnected world.By integrating traditional methods with digital technologies, blended learning creates dynamic educational environments that address diverse learner needs while promoting collaboration and autonomy.The paper highlights the added value of short-term faculty-led study trips, which provide immersive, real-world experiences, enhancing cultural competence and interdisciplinary learning.Based on a literature review, the paper identifies strategies for combining blended learning with international educational programmes.These include leveraging interactive digital tools, fostering learner-centric environments, and employing diverse assessment methods to promote critical thinking and adaptability.Technology integration emerges as a cornerstone of this model, enabling accessible, flexible, and engaging learning experiences.However, the digital divide, financial barriers, and institutional coordination require strategic attention.The case study of the Irish-Canadian academic partnership between Atlantic Technological University (ATU) and Niagara College (NC) illustrates the practical implementation of these approaches.Initiatives like the "Spaces, Places, and Relationships for Learning, Wellbeing, and Collaboration" (SPRLWC) module demonstrate how blended learning and cross-border collaborations can deliver meaningful educational outcomes.The paper concludes with recommendations for addressing operational and logistical challenges, emphasising inclusivity, financial sustainability, and cultural integration.It advocates for continued innovation and collaboration to refine and expand blended learning and international educational programmes, ensuring they remain impactful and accessible in a rapidly evolving global landscape.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".