Learning Theory and Educational Intervention: Producing Meaningful Evidence of Impact Through Layered Analysis
Bibliographic record
Abstract
Like evidence-based medicine, evidence-based education seeks to produce sound evidence of impact that can be used to intervene successfully in the future. The function of educational innovations, however, is much less well understood than the physical mechanisms of action of medical treatments. This makes production, interpretation, and use of educational impact evidence difficult. Critiques of medical education experiments highlight a need for such studies to do a better job of deepening understanding of learning in context; conclusions that "it worked" often precede scrutiny of what "it" was. The authors unpack the problem of representing educational innovation in a conceptually meaningful way. The more fundamental questions of "What is the intended intervention?" and "Did that intervention, in fact, occur?" are proposed as an alternative to the ubiquitous evaluative question of "Did it work?" The authors excavate the layers of intervention-techniques at the surface, principle in the middle, and philosophy at the core-and propose layered analysis as a way of examining an innovation's intended function in context. The authors then use problem-based learning to illustrate how layered analysis can promote meaningful understanding of impact through specification of what was tried, under what circumstances, and what happened as a result. Layered analysis should support innovation design and evaluation by illuminating what principled adaptation of educational technique to local context could look like. It also promotes theory development by enabling more precise description of the learning conditions at work in a given implementation and how they may evolve with broader adoption.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".