2013 3M Student Fellows Feature Article - Exploring the role of the university student as an experiential learner: Thoughts and reflections from the 2013 cohort of 3M National Student Fellows
Bibliographic record
Abstract
In recent years, there has been a dynamic shift in the role of the university student through the creation and promotion of experiential learning opportunities on campuses across the country. Many post-secondary programs now include co-op placements, practicums, or internships where students can apply theoretical knowledge to real-world settings. However, in this article, we have chosen to focus on more “altruistic” forms of experiential learning – volunteerism, development work, and service-learning – which have gained increased focus in recent years but are often used, we feel, without appropriately reflecting on their meaning. In this article, we draw upon our experiences as student leaders to define each of these roles, outline what we see as the benefits of experiential learning for students, and provide recommendations for how these learning opportunities can continue to be improved. Moreover, we identify privilege, ethics, and responsibility as complexities related to experiential learning and discuss each of these topics in more detail. We end our discussion by addressing the role of experiential learning in helping to define the value of a post-secondary education.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| 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".