A Meta-Analysis of Three Types of Interaction Treatments in Distance Education
Bibliographic record
Abstract
This meta-analysis of the experimental literature of distance education (DE) compares different types of interaction treatments (ITs) with other DE instructional treatments. ITs are the instructional and/or media conditions designed into DE courses, which are intended to facilitate student–student (SS), student–teacher (ST), or student–content (SC) interactions. Seventy-four DE versus DE studies that contained at least one IT are included in the meta-analysis, which yield 74 achievement effects. The effect size valences are structured so that the IT or the stronger IT (i.e., in the case of two ITs) serve as the experimental condition and the other treatment, the control condition. Effects are categorized as SS, ST, or SC. After adjustment for methodological quality, the overall weighted average effect size for achievement is 0.38 and is heterogeneous. Overall, the results support the importance of the three types of ITs and strength of ITs is found to be associated with increasing achievement outcomes. A strong association is found between strength and achievement for asynchronous DE courses compared to courses containing mediated synchronous or face-to-face interaction. The results are interpreted in terms of increased cognitive engagement that is presumed to be promoted by strengthening ITs in DE courses.
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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 | 0.019 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.035 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".