Towards a conceptual-based, student-centred pedagogy
Bibliographic record
Abstract
Purpose The purpose of this paper is to report on the approaches teacher candidates (TCs) took to craft the object of learning and the critical aspects when applying variation theory, in planning science lessons. It addresses the difficulty TCs often face in determining what students could learn when crafting the objects of learning and the critical aspects. Design/methodology/approach A total of 27 science TCs from an initial teacher education (ITE) were introduced to variation theory and collaborated in groups of four to five to plan lessons. Data included recordings of the lesson planning session and fieldnotes, TCs’ lesson plans and individual semi-structured interviews. Phenomenography as a methodology was employed to capture the variation in the TCs’ approaches in crafting the objects of learning and the critical aspects, resulting in categories of description that illustrated the complexity of the lesson planning process the TCs engaged with. Findings Arranged in hierarchical order, the three categories of description include TCs: analysing content knowledge in order to promote conceptual learning; reflecting on personal experiences and beliefs about teaching and learning; and drawing on external resources to develop knowledge about students. Practical implications The ways the TCs engaged with lesson planning constituted valuable resources to build their capacity to differentiate between superficial content engagement and deep conceptual learning. Originality/value The study reports on the potential of framing ITE discourse using variation theory to support TCs’ engaging with science teaching that is more conceptual-based, while increasing their sensitivity to students’ common conceptions that constrained the learning of canonical science.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| 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".