Roles for software technologies in advancing research and theory in educational psychology
Bibliographic record
Abstract
While reviews abound on theoretical topics in educational psychology, it is rare that we examine our field's instrumentation development, and what effects this has on educational psychology's evolution. To repair this gap, this paper investigates and reveals the implications of software technologies for researching and theorizing about core issues in educational psychology. From a set of approximately 1,500 articles published between 1999 and 2004, we sampled illustrative studies and organized them into four broad themes: (a) innovative ways to operationalize variables, (b) the changing nature of instructional interventions, (c) new fields of research in educational psychology, and (d) new constructs to be examined. In each area, we identify novel uses of these technologies and suggest how they may advance, and, in some instances, reshape theory and methodology. Overall, we demonstrate that software technologies hold significant potential to elaborate research in the field.
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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 | 0.026 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".