A Biophilic Approach To Post-Secondary Learning Strategies: Facilitating Learning Through Intentional Time In Nature
Bibliographic record
Abstract
Many post-secondary students report increasing difficulties with concentration, persistence, stress and anxiety. Focused listening in lectures, effective collaboration, efficient studying, and thoughtful time management contribute to post-secondary achievement, as do self-efficacy, tenacity, resilience, and perseverance. Together, these foundational skills and abilities, which undergird all academic disciplines, assist students in accomplishing expected tasks, facing challenges, and recovering from setbacks. Learning strategists work with students to bolster academic skills and abilities, typically through paper- or web-based techniques. Learning strategies are frequently taught and engaged with indoors; inside labs and studios, in front of computers and projectors, and practiced at home or at library desks. This narrative inquiry investigates how learning strategists may facilitate biophilic experiences to improve student achievement. Findings of this study indicate that Ryerson University's Portage program, offered to students with learning exceptionalities, may help to foster the very skills learning strategists seek to scaffold and support. Additionally, the findings from this study may be of benefit to the general student population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".