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 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 | 0.130 | 0.229 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.043 | 0.074 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".