Student learning through service learning: Effects on academic development, civic responsibility, interpersonal skills and practical skills
Bibliographic record
Abstract
Although anecdotal evidence and research alike espouse the benefits of service learning, some researchers have suggested that more rigorous testing is required in order to determine its true effect on students. This is particularly true in the case of academic development, which has been inconsistently linked to service learning. It has been proposed that this discrepancy is due to three complications: grades not reflecting higher order thinking skills, self-selection bias, and different grading methods. The study described in this article attempted to circumvent these complications using a test–retest methodology and measuring academic development in three ways: course grades, an assignment that directly tested course-specific comprehension, and self-reported improvement. In addition, improvements in civic responsibility, interpersonal skills, and practical skills were measured via self-report. Although students who participated in service learning self-reported greater improvement in civic responsibility, interpersonal skills, and academic development, they only demonstrated more academic development in terms of concrete course concepts, showing no differences in final examination marks or generation of detailed examples. These findings suggest that academic improvement through service learning may not be adequately assessed by typical methods used to evaluate academic development at universities.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".