Students' challenges in service learning : a non-typical "good news" story
Bibliographic record
Abstract
Community Service-Learning (CSL) provides students with an opportunity to serve communities as well as to realize their specific academic learning objectives. While there are proven and consensual benefits of service learning for students, some outcomes, challenges and concerns that students may encounter during the learning process have seldom been discussed. Especially for international students who participate in service-learning programs in Canada, their voice is missing in current literature. The goal of this paper is to explore what are the challenges that students may experience within the CSL programs, and how do these discomfort moments serve as learning opportunities. The paper presents a concise introduction to CSL, an overview of literature about challenges of service learning associated with students, and an autobiography of the author’s CSL learning experience. The author uses Boler’s theory of “ pedagogy of discomfort” to analyze the journals and to conduct critical reflection. Although the conclusions are specific to the case reported here, the learning process may apply to other international students enrolled in CSL courses in Canada. Thus this paper will potentially contribute to filling the gap in existing literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.022 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.017 | 0.024 |
| Insufficient payload (model declined to judge) | 0.002 | 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".