Using Big Data for Educational Decisions: Lessons from the Literature for Developing Nations
Bibliographic record
Abstract
Educational leaders from developing countries may be tasked with using big data to help inform educational decisions. Although many researchers have explored how to use big data or datasets to help solve educational problems, few studies have articulated how educational researchers and leaders from developing nations can use big data to make educational decisions. This study provides a literature review and takes a position to help educational leaders from developing nations use big data to make educational decisions and understand the strengths and weaknesses of using data to drive decision making. Moreover, this study addresses how datasets may be limited and how educational leaders can understand these limitations when using big data.
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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 | 0.063 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.012 | 0.029 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.013 |
| 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".