Action Research for Graduate Program Improvements: A Response to Curriculum Mapping and Review
Bibliographic record
Abstract
There is a global trend toward improving programs and student experiences in higher education through curriculum review and mapping of degree programs. This paper describes an action research approach to program improvement for a course-based MEd degree. The driver for continual program improvement came from actions and recommendations that arose from an institutionally mandated, year-long, faculty led curriculum review of professional graduate programs in education. Study findings reveal instructors’ perceptions about how they enacted the recommendations for program improvement, including (1) developing a visual conceptualization of the program; (2) improved connections between the courses; (3) articulation of coherence in goals and expectations for students and instructors; (4) an increased focus on action research; (5) increased ethics support and scaffolding for students; and (6) the fostering of communities of practice. Study findings highlight strengths of the current program and course designs, action items, and research needed for continual program improvement.
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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 | 0.453 | 0.640 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.013 | 0.022 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.011 | 0.016 |
| Research integrity | 0.028 | 0.036 |
| 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".