Research on Distance Education: In defense of field experiments
Bibliographic record
Abstract
This article extends the issues and arguments raised in Bernard, Abrami, Lou, and Borokhovski (Distance Education, 25(2), 175–198, 2004 Bernard, R. M., Abrami, P. C., Lou, Y. and Borokhovski, E. 2004a. A methodological morass? How we can improve the quality of quantitative research in distance education. Distance Education, 25(2): 175–198. [Taylor & Francis Online] , [Google Scholar]) regarding the design of quantitative, particularly experimental research in distance education. A single experimental, study from the distance education literature is examined from six different perspectives to show the differences between preexperiments, true experiments, and quasi‐experiments in terms of their impact on interpretability and generalizability (i.e., internal and external validity). Arguments for and against experimentation are discussed and the article ends with a description of meta‐analysis, the quantitative synthesis of experimental research, and its potential for providing answers to questions that no single study can adequately address.
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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 | 0.518 | 0.685 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.036 |
| Scholarly communication | 0.009 | 0.025 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".