How conceptualisations of curriculum in higher education influence student-staff co-creation in and of the curriculum
Bibliographic record
Abstract
There is a wide range of activity taking place under the banner of ‘co-created curriculum’ within higher education. Some of this variety is due to the different ways people think about ‘co-creation’, but significant variation is also due to the ways in which higher education curriculum is conceptualised, and how these conceptualisations position the student in relation to the curriculum. In addition, little attention is paid to the differences between co-creation of the curriculum and co-creation in the curriculum. This paper addresses this gap by examining four theoretical frameworks used to inform higher education curriculum design. We examine how each framework considers the position of the learner and how this might influence the kinds of curricular co-creation likely to be enacted. We conclude by calling for more discussion of curriculum and curriculum theories in higher education—and for these discussions to include students. We argue that more clarity is needed from scholars and practitioners as to how they are defining curriculum, and whether they are focused on co-creation of the curriculum or co-creation in the curriculum. Finally, we suggest that paying greater attention to curriculum theories and their assumptions about the learner, offers enhanced understanding of curricular intentions and the extent to which collaboration is possible within any particular context.
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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 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.021 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".