The Contribution of Student-Centered Pedagogy to the Effective Use of Educational Technology: A Meta-Analysis
Bibliographic record
Abstract
It is no longer a question whether technology should be integrated into the classroom. The focus has shifted to how to use it to enable and promote effective learning. For better or for worse, technology is pervasive in our lives, and educational settings are no exception. However, it is not sufficient to employ educational technology simply because it is available. How technology is deployed, when and for what purposes it is used, what kind of learning it is applied to, and which categories of students it affects, are now of prime importance. This paper presents findings of a meta-analysis (M-A) that investigated differences between teacher-centered and student-centered (T-C vs. S-C) pedagogical practices in their effect on educational technology use as measured by student achievement outcomes. To describe S-C strategies, eleven instructional dimensions were identified from our previous work. Findings, based on 168 independent effect sizes (ESs) comparing T-C with S-C revealed a weighted average of g+=0.402 indicating that educational technology moderately increases learning achievement outcomes. Significant findings are reported, with four dimensions -Course design, Problem type, Conceptual level, and Peer collaboration - strengthening the impact of educational technology on students’ achievement, and in one dimension - Pacing/Flexibility - weakening it.
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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 | 0.058 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.047 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".