Meta-analysis to estimate the relative effectiveness of TBLT programs: Are we there yet?
Bibliographic record
Abstract
Bryfonski and McKay published a meta-analytic review of reports on the effectiveness of task-based programs relative to other types of programs. The aggregated effect in support of task-based programs was substantial. However, Boers et al. re-examined the 27 comparative studies from which this effect size was calculated and argued that, with one exception, they did not serve the intended purpose of the meta-analysis well. However, in a recent publication, Xuan et al. argue that a good number of the primary studies used by Bryfonski and McKay are in fact suitable for a meta-analysis if the programs they assess are re-defined as task-supported rather than task-based programs. Xuan et al. then conducted a meta-analysis of 16 of the 27 comparative studies that were originally included in Bryfonski and McKay’s analysis, confirming the benefits of programs that use tasks, albeit with a smaller aggregated effect size. The present article revisits these 16 studies and, in addition, examines a handful more recent ones published since Bryfonski and McKay’s original meta-analysis. The conclusion remains that the field is not ripe yet for a meaningful meta-analysis of the relative effectiveness of either task-based or task-supported programs. Interpreting the outcomes of the meta-analytic endeavours reported so far is especially difficult because of a lack of clarity in both the primary study reports and the meta-analyses of what constitutes a task-supported program and of what is understood by ‘task’.
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How this classification was reachedexpand
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 | 0.024 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads 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".