Seeing the “big picture” : exploring the impact of the duration of community service volunteer work and learning on university students
Bibliographic record
Abstract
Community service learning (CSL) is growing in higher education across Canada and has been a part of university programs in the US for decades. It is described by the Association of American Colleges and Universities as a “high impact” educational practice, along with academic learning communities, undergraduate research, study abroad, internships, and capstone courses or experiences (Kuh, 2008). Some of the service learning program characteristics that reportedly contribute to its impact include the quality of CSL placements, the quantity and quality of opportunities for student reflection, the application of the placement to academic content, and the duration and intensity of service (Eyler et al., 2001). This paper focuses on the question, what difference does the duration of service learning through volunteering and classroom activities make for student outcomes, drawing on data from a mixed methods study of students engaged in service learning at a Canadian university. Our previous analysis suggests that CSL is perceived very positively by most students who participate and that it contributes to their development in a variety of ways. The study found that even when students did not opt to engage in a community placement within a community service-learning course, they were positively impacted by peer learning. However, little research has examined the relationship between the intensity of service learning and students’ attitudes. This study provides a contribution to this insufficiently explored domain.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".