Undergraduate Students’ Experiences of a Community-Engaged Learning Course: A Mixed-Methods Study
Bibliographic record
Abstract
Background Undergraduate student engagement increases the quality of education. Community-engaged learning (CEL) courses are one way to promote engagement and involve students collaborating with community partners to achieve a common goal by applying course knowledge to real-world issues. Purpose This study evaluated: (a) the relationship between CEL-related student learning outcomes (SLOs) and attitudes toward CEL courses before taking one; (b) CEL-related SLOs among undergraduate students before versus after taking a CEL course; and (c) the lived experiences of students who participated in their first CEL course. Methodology Pre- and post-course surveys and focus group data were collected. Survey data were analyzed via correlations and dependent groups t -tests, while inductive content analysis was employed to analyze focus group data. Findings Findings revealed a significant correlation between students’ opinions toward the benefits of taking a CEL course and their CEL-related SLOs and a statistically significant positive difference between student growth and achievement before compared to after completing a CEL course ( t = 2.6778, p = .0123). Students also expressed the benefits of taking CEL courses, including community impacts, conduciveness to learning preferences, and skill development. Implications CEL courses are a means to improve students’ motivation, achievement, and skill acquisition for future career preparedness.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".