Service-Learning: Boldly Going Where EAL Students Have Not Gone Before
Bibliographic record
Abstract
Service-learning is an experiential pedagogy which integrates curriculum and volunteer service through ongoing reflection. Research suggests that service-learning offers notable benefits for post-secondary English-as-an-additional-language (EAL) students. However, most of the researchers have studied EAL students within the United States; far fewer have examined EAL students in the Canadian context. This paper reports on a study of the impact of service-learning on EAL students at a Canadian university in British Columbia. A first-year service-learning elective has been offered at the university since Fall 2009. This course is taught by faculty from the ESL Department who have a Master’s degree or equivalent in a related field. This study investigated the impact of the elective on EAL students’ English proficiency. Data were collected from students through surveys, interviews, and journals. Additionally, the grade point averages (GPA’s) of EAL students in first-year university English composition were examined, comparing those EAL students who took the service-learning elective (Group A) with those who did not (Group B). Grade analysis showed whereas Group B had a GPA of 2.15 on a 4.33 scale for first-year English composition, the subset of Group A who took first-year English composition in the semester immediately following service-learning achieved a GPA of 2.55. The results supported service-learning as an effective pedagogy for EAL students.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".