Technology integration in postsecondary education: A summary of findings from a set of related meta-analyses
Bibliographic record
Abstract
Although the overall research literature on the application of educational technologies to classroom instruction tends to favor their use over their non-use, these results vary considerably depending on what kind of technology is used, who it is used with and, more importantly, under what circumstances and for what instructional purposes it is used. Relatively recent, but well-developed and powerful methodology of systematic reviews, particularly quantitative syntheses (also known as meta-analyses) is especially suitable for addressing questions of that type by systematically summarizing research evidence in given areas of interest in social sciences.This meta-analysis summarizes data from 674 independent primary studies that compared higher degrees of technology use in the experimental condition with less technology in the control condition, in terms of their effects on student learning outcomes in postsecondary education. The result was an overall average weighted effect size of = 0.27 (k = 879, p < .01), indicating low but significant positive effect of technology integration on learning. The follow-up analyses revealed the influence of educational technology used for cognitive support and blended learning instructional settings designed interaction treatments, and technology integration in teacher training, especially when student-centered pedagogical frameworks are used. These findings are of potentially high interest and applied value for educational practitioners, including teachers and school administrators, as well as for instructional designers and developers of educational software.
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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 | 0.056 | 0.107 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.060 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| 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".