Phenomenon-based Learning for Age 5.0 Mindsets: Industry, society, and Education
Bibliographic record
Abstract
Sustainable education requires developing transformative competencies for rehumanizing education at the age of smart machines. This article will examine how a next-generation learning model that manifests through smart technologies may thrive within the Age 5.0 educational framework. To realize this practice, the converging phenomenon of sustainability has been addressed within its subset smart power grid by piloting processing learning style and systems thinking pedagogy that incorporates student active participation in tasks like design modules and real-world projects facilitated by guidance, feedback, and critique. The process utilizes the University of Ottawa campus buildings as a “real-space sustainability lab” for developing learning content and collecting data for projects as part of teaching a fourth-year undergraduate course on power systems. This not only facilitates a practical and experiential approach but also provides a great exposure to real entities thereby minimizing the gap between industry and academia. Gathered data from the questionnaires, interviews and observation clearly show that unleashing engaging activities into phenomena-and project-based learning may significantly improve student analytical thinking, knowledge creation, reflective judgment, self-efficacy, and importantly graduate employability.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".