A review of the meta‐analysis by Tingir and colleagues (2017) on the effects of mobile devices on learning
Bibliographic record
Abstract
Abstract Tingir et al. (2017) concluded from their meta‐analysis that the subject areas taught through mobile devices had significantly higher achievement scores ( d = 0.48) than the ones taught with traditional teaching methods. Given the relatively high positive effect of mobile devices on student achievement, we carefully analysed the selected research in this meta‐analysis. We reviewed Tingir et al.’s (2017) meta‐analysis based on analysis of the methodology of the selected research, while drawing on the work of Slavin (2003), Cheung and Slavin (2016), and Sung et al. (2019). Twelve of the 14 (86%) studies included in the meta‐analysis done by Tingir and his team (2017) present such major methodological flaws that they should not have been included. Our analysis leads us to believe that the conclusion of Tingir et al. (2017) is not justified. It is recognized that duration of experiment is negatively correlated with effect size: the shorter the duration, the higher the effect (Burston, 2015; Slavin & Lake, 2009). Although demanding more effort, the field of education must raise the bar if it is to have knowledge of acceptable value.
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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 | 0.016 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.021 |
| Bibliometrics | 0.008 | 0.008 |
| 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) | 0.005 | 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".