Transformative Learning: The Impact of Deeper Learning Approaches in Enhancing the Transversal Competencies
Bibliographic record
Abstract
Curriculum policy statements advocate the essential nature of well-developed 21st century or transversal skills for our students’ success in today’s world. Future employment, enjoyment, empowerment, and effective participation in society require these skills. The essential skills of digital literacy, communication, collaboration, critical and creative thinking are aspects of the deeper learning approaches such as guided inquiry learning that are explored in this study and are recognized as significant literacies in the information/knowledge economy and align with both the Australian and Victorian Curriculum (Australian Curriculum Assessment and Reporting Authority, 2022; Victorian Curriculum and Assessment Authority, 2018). It is hoped that my study, informed by the narrative of student voice on the experience of guided inquiry learning, will further inform teaching practice for educators on how to design meaningful and powerful learning experiences that both engage students and improve student achievement by enhancing their critical and creative thinking.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| 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".