COIL PROJECTS AS A MEANS TO FOSTER COLLABORATIVE WORK IN THE PROFESSIONS
Bibliographic record
Abstract
Collaborative Online International Learning (COIL) projects aim at achieving a common goal through teamwork and cooperation.Collaborative skills are among the top soft skills employers want from their employees.COIL as a teaching and learning method develops reflexivity skills since at the core of a COIL experience students are asked to examine their own reactions and motives to face specific course contents (such as ways to feel about and act to find solutions for social and environmental issues).Likewise, instructors face the challenge of imparting awareness leading to cultural shifts creating spaces where students discuss and analyse how different groups think or act in the same situation.This particular COIL experience brought together two universities, one located in Canada and the other in Spain, both including students from different ethnic and cultural provenance.COIL is a learning process that integrates different skills and attitudes to succeed in the workplace of the future.Among these skills, problem-solving, intercultural skills and collaboration skills are key to COIL projects.This paper reports on a COIL project focusing on the United Nations' Sustainable Development Goal 13 (SDG13) with two student cohorts, one focused on criminology and security studies and the other on sociology and critical thinking.This paper shows how 21st-century skills are developed in online collaboration and how they may be seen as a step towards achieving intercultural and soft skills in a professional collaborative environment.The project engaged students in discussions and debates about contemporary environmental and community issues, climate change/climate crisis, food security/urban development, the relationship between environmental problems and marginalization/exclusion, and racial discrimination.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".