The Evaluation of Data Based School Management Practices: A Comparative Study
Bibliographic record
Abstract
This study focuses on describing data based school management processes in Türkiye, Spain and Romania. The study group consists of 49 participants. Maximal variation and stratified sampling were applied. For internal trustworthiness, respondent validation, data triangulation and cross check were utilized. For transferability, ‘expert opinions’ and ‘focus group interview’ were applied. Findings show that the school administrators have competencies about data collection, interpretation, storage, backup and visual data. They are perceived as incompetent in terms of data cleaning, debugging, data analysis, big data and value creation. Various types of data such as performance evaluation, perception, opinion, demographic and academic success data are utilized and they have functions such as needs analysis, transparency, accountability, guidance and functional archiving. It was found out that for decision processes, data have functions such as participation, adoption, clarification, fighting against decision bias, setting attainable standards and making adaptations to changing conditions. Data can add to decision quality by helping set criteria, contributing to accuracy of predictions, verifying alternatives and selecting the best and determining various scenarios for actions. Data bases have functions as benchmarking, real time data flow and macro and meso level learning analytics. Hard copy materials contribute to individualization of learning, evidence-based learning, micro level learning analytics and concurrent feedback. Issues of data based school management are data management, data literacy, data quality and value creation. Suggestions to improve processes are synchronized data sharing, internet of things, survey archives, real time data flow, artificial intelligence, functional archiving, data based decision support systems and a shared insight and attitude for data use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".