Improving Student Reflection in Experiential Learning Reports in Post-Secondary Institutions
Bibliographic record
Abstract
Work-integrated learning options—or experiential learning—(such as co-operative education, practicum placements, and community service learning/volunteer placements) offer much scope for enhancing educational opportunities for post-secondary students to learn about the workplace and to develop skills that may contribute to their future employability. However, community service learning (CSL) placements and co-operative education (co-op) programs, among other forms of experiential learning, offer so much more than the practical outcomes of skills-development and résumé-building. They provide a space for reflexivity on the student’s positionality in relation to privilege and national and/or global citizenship identity-formation; for critical reflection on ethical issues; for the promotion of social justice; and for praxis (the application of knowledge). The research presented in this article is an evaluation of two sets of experiential learning reflection assignments: co-op work-term reports (from 2nd, 3rd, 4th year and graduate students) and CSL papers (assignments submitted for a fourth year class I taught in winter 2016 on experiential learning). I examine the common themes and differences between these two sets of assignments with particular attention to the preparation and facilitation of learning in both instances, and the difference this preparation makes in terms of the student’s critical reflection.
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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 | 0.037 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".