Promotion of reflective learning, teaching and assessment through curriculum design
Bibliographic record
Abstract
In this chapter I argue that if reflection is to be a central tenet of learning, teaching and assessment it is necessary to embed it at a curriculum design level. By implementing a whole curriculum approach (Schuell, 1986) links are forged between the elements so that both process and outcome are considered. The aim should be to encourage student engagement by providing a framework to facilitate development of students‟ reflective capability. Although considerable attention has been paid to models and frameworks that support reflective teaching and learning (see for example, Kember et al, 2001; Moon, 1999; Brookfield, 1995; Johns, 1995; Boud,\nKeogh & Walker, 1985) and to issues surrounding the assessment of reflective capability (Brockbank & McGill, 2007; Clouder, 2004; Moon, 2001; Hinett & Knight,1996) scant attention has been paid to its integration at a curriculum design stage and throughout the entire learning experience. My intention in this chapter is to encourage academics involved in course design or redesign to consider the implementation of two curriculum concepts used in tandem to provide a framework that promotes congruence between reflective learning, teaching and assessment.\nThe two concepts are constructive alignment (Biggs, 1996) and the spiral curriculum approach (Bruner, 1960).\n
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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 | 0.030 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".