Experiential Learning through Community-based Experiences: A Graduate Student Perspective
Bibliographic record
Abstract
Experiential learning (EL) has become essential for graduate students to meet the demanding nature of professional environments, equipping them with skills in leadership, problem solving, and civic consciousness. Community based learning (CBL), as an identified EL strategy, involves a collaborative learning model emphasizing group membership and community engagement. CBL not only enhances graduate skills, but also places graduate student research within a larger social context, and encourages deeper understanding of their discipline. This paper discusses a 90-minute workshop that focused on a graduate student experience with CBL and proposes a framework for integrating CBL into graduate studies. The framework proposes the use of positionality and mindful inquiry methods to identify learner-specific EL activities. Workshop participants reflected on their positionalities, and discussed how positionality can be used to guide mindful inquiry in seeking CBL activities. Further, we report on participant identified contextual and administrative barriers to integration of CBL into graduate curricula. As EL becomes an important mandate for postsecondary institutions to incorporate into student learning, this paper provides a valuable graduate student perspective that can add insight into the practicality of applying CBL in graduate 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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".