Why is it so Hard to do a Good Thing? The Challenges of Using Reflection to Help Sustain a Commitment to Learning
Bibliographic record
Abstract
Our service learning research includes assessment of reflective assignments done by students who apply theoretical knowledge in practical, real-life contexts by working with actual clients. For many of these students our classes are a departure from traditional forms of learning, and a challenge to their ability to apply what they know in often very unpredictable situations. The reflective assignments, which include field notes and journal entries, are designed to 1) train the professional competencies of client case management, writing and recording and 2) foster a sustained commitment to learning and professional development. This paper will describe several teaching and learning issues related to reflective writing which we have encountered in our students’ work, and outline our plans to address them as we continue to promote critical thinking and reflection as important skills for our students to master.
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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 | 0.081 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.024 | 0.015 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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".