Bibliographic record
Abstract
This article is intended to provide students and clinicians aspiring to perform educational research with some background information pertaining to many of the issues inherent in performing research within this domain. It is not intended to provide a comprehensive review of the quantitative methods one might adopt, nor will it fully reflect all of the debate that currently exists within the educational research community. Rather, it is intended to offer an overview of issues and controversies within the field that will hopefully provide a starting point from which interested individuals can begin to engage in the study of educational effectiveness. Using investigations of the efficacy of problem-based learning as background, the article represents an attempt to guide new researchers through the process of generating and refining scientific research questions, identifying appropriate outcome measures, and selecting or adapting the optimal research design for the questions to be addressed. The article focuses on quantitative methods in general with particular attention paid to experimental designs.
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 | 0.646 | 0.763 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.028 | 0.033 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.021 | 0.024 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".