Reflective practice in service learning: possibilities and limitations
Bibliographic record
Abstract
Purpose The paper seeks to promote the integration of reflective learning within a broader service learning pedagogy at the undergraduate university level. Furthermore, it aims to illustrate various models for service learning that span multiple academic disciplines. Design/methodology/approach The paper illustrates three ways in which reflective learning can be used to enhance the learning potential of service learning pedagogy. The subjective experiences inform its own stories that it presents as examples, supported as they are by the findings of prior empirical studies. Findings The paper believes that multi‐dimensional learning has been achieved in each of the three examples presented. Service learning extends the academic learning of students and allows for personal and societal learning to occur, not simply as a result of having a service experience, but of spending time reflecting on it. Practical implications Practical implications are particular to the students themselves, as the service experiences it describes have, in some instances, helped to clarify individual students' values and vocational interests. Social implications Given the range of service learning models contained in these examples, the social implications likewise range, yet share common dimensions of increasing students' social consciousness, appreciation of diversity, and their own capacities to contribute. Originality/value The originality of this paper lies in the linking of reflective learning to service learning best practices to highlight the particular role of reflection in the production of multi‐dimensional learning.
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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 | 0.341 | 0.471 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.026 | 0.036 |
| Open science | 0.011 | 0.022 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".