Research on Distance Education: In defense of field experiments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it