Discussing Global Citizenship through Collaborative Online International Learning (COIL) - Virtual Exchange (VE) in Language Learning and Teaching
Bibliographic record
Abstract
This edited collection of papers addressing curriculum internationalisation through internalisation at home, using Collaborative Online International Learning (COIL) Virtual Exchange (COIL-VE), offers compelling reading in supporting the preparation of students for societal issues locally and globally at personal and professional levels.Bringing together established researchers as well as early career researchers from language teacher education and language learning, the collection illustrates the potential of COIL-VE as a means of engaging students from across different cultural backgrounds to consider contemporary critical issues and in ways not typically accounted for and/or presented.As a 'networked' pedagogical approach, such exchanges also present an inclusive intercultural dimension to teaching and research and with wider community partners.Using synchronous and asynchronous means via various online platforms, and as part of integrated or at time optional study, COIL-VE, also known as telecollaboration (Helm, 2013), is explored as a pedagogical approach for valuing epistemological diversity.For example, the papers include critiquing immigration and nationalism/patriotism; creating global communities of practice to support linguistic diversity; enhancing international ELT programmes' response to changing global environments; translation strategies; pedagogical translanguaging; and challenging 'native-speakerism' ideology in ELT.Whilst its impact on HE is still taking place, and despite research in the field over the last decade plus, we continue to appreciate the opportunities COIL-VE presents in addressing equity, diversity and inclusion agendas, new forms of collaboration and partnership, alternate approaches to assessment, ways to address sustainable development and global citizenship education.We also recognise COIL-VE is not without its challenges, especially as COILs can bring forth struggles and tensions which can be experienced as uncomfortable and disquieting, bringing into question issues of power, privilege, and intersectionality, and for which critical interrogation of the underpinning ideologies is required (Wimpenny et al. 2023).Indeed, it can take time for learners to digest and reflect upon the cross-cultural learning taking place.Yet being open to share and or reorientate thinking often requires uncomfortable introspection and deliberation, which only adds to the richness and benefits to be gained from engaging in COIL-VE.As such, themes arising from the papers bring nuanced perspectives which will be of interest to diverse disciplines including the languages, education strategy and leadership.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".