Learner agency and the curriculum: a critical realist perspective
Bibliographic record
Abstract
Abstract Agency, understood as the capacity to act independently and to make one's own choices, is considered central to children's development. Thus, education, and hence education curricula, have a role in the development of learner agency. While curriculum development is a key focus for educational theory, research, policy, and classroom practice, the potential implications of curriculum content selections for learner agency remain underexplored. Theoretically, this paper engages with critical realism, explaining how it can provide theoretical foundation for a more comprehensive view of learner agency and, by implication, more balanced curricula. Empirically, the paper draws on the findings from a content analysis of the national curriculum documents of four countries with relatively high scores in international comparative tables, England, Australia, Hong‐Kong, and Canada, to develop a new typology of primary curricula. Based on the extent of emphasis placed on knowledge versus skills, values, and attitudes, three types of curricula were identified: knowledge‐based, skills‐oriented, and learner‐centred. Due to its significant theoretical and practical influence globally, we focus on the knowledge‐based model and its likely impact on students’ agency. We conclude by highlighting the importance of making learner agency a key orientation of the curriculum and suggesting directions for future research.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.070 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".