The Effect of Digital Stories on Academic Achievement: A Meta-Analysis
Bibliographic record
Abstract
The purpose of this study is to determine the effect of digital stories on academic achievement. In order to achieve this purpose, meta analysis method was used in the study. Within the scope of the study, ERIC, Google Academic, YÖK Thesis Center, ProQuest, Science Direct and ULAKBİM databases were scanned and 23 studies (10 theses, 13 articles) were included in the meta-analysis using the criteria determined by the researchers. Cochran’s X2 (Q = 285,155, p < .05) test was conducted to test whether the studies included in the study were heterogeneous. Random effects model was used to calculate effect size since heterogeneity was determined between studies. At the end of the study, it was seen that the general effect size (Hedge’s g = 1.081) regarding the effect of digital stories on academic achievement was strong, that there was a positive effect in all areas according to the lessons which was higher for Science. It was also observed that the effect was positive in all dimensions according to the education level, there was a difference between the education levels and the highest effect occurred at university and middle school levels.
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.
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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.049 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".