Discourses on Learning in Education:Making Sense of a Landscape of Difference
Bibliographic record
Abstract
There are hundreds, perhaps thousands, of “theories of learning” at play in the field of education. Given scant agreements on the meaning of “learning” and the purpose of “theory,” such quantity is perhaps unsurprising. Arguably, however, this situation is indefensible and debilitating in an academic domain so focused on interpreting and influencing learning. We describe our own efforts to come to terms with this matter. Oriented by Conceptual Metaphor Theory and network theory, we are attempting to “map” contemporary treatments of learning—whether implicit or explicit, written or spoken, descriptive or prescriptive, formal or informal, scientific or folk. We report on our iterative process, evolving design, and emergent insights. We discuss the potential relevance of this and similar efforts for the future of educational research and practice.
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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 | 0.031 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.084 |
| Scholarly communication | 0.023 | 0.043 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".