Significant Learning in a Collaborative Online International Learning Platform
Bibliographic record
Abstract
Collaborative Online International Learning (COIL) holds a lot of promise as an experiential tool to enhance student learning. As management educators explore pedagogies for sustainability education, research is needed that examines whether COIL can serve as an effective tool to enhance students’ significant learning in this context. This paper presents a study that utilized a mixed-method approach to evaluate the effectiveness of a COIL experience for educating business students on the role of business innovations for sustainability as described by the UN Sustainable Development Goals. As a collaboration of instructors from four different institutions across North America, Europe and Southeast Asia, this COIL incorporated Fink’s taxonomy of significant learning. Findings from our survey-based research suggest that COIL students obtained superior student learning outcomes over non-COIL students on three of the six dimensions- integration, caring, and learning how to learn. Our qualitative study shows that the experience improved the students´ learning along several of Fink´s dimensions of significant learning.
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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 | 0.005 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".