The impact of educational technology: a radical reappraisal of research methods
Bibliographic record
Abstract
The practical problem which motivates this paper is that of deciding - on the basis of published research - whether to adopt some new device, procedure or paradigm thought likely to improve education. What models, methods or media are likely to be most useful? From the invention of the printing press to multimedia software, educators have adopted unproven aids and fads. Researchers usually claim each new device or procedure to be at least as effective as its predecessor. How valid is all this research? How to decide? A typical view is: Design an experiment to observe the effects of your treatment. Any book on research design and statistics will show you how. But will it? What essential aspects of educational measurement and research must we consider?DOI:10.1080/0968776970050108
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 | 0.310 | 0.343 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.015 | 0.007 |
| Science and technology studies | 0.005 | 0.107 |
| Scholarly communication | 0.026 | 0.053 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.011 | 0.027 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".