Bibliographic record
Abstract
Purpose To advocate educational improvement science (EIS) as an emerging transdisciplinary field, I reflect on the three major pathways of educational advancement in human history, discern the misuses and pitfalls of reform, and theorize how education can be improved to better serve its mission. Design/Approach/Methods Employing a multiperspectival approach, I critically re-examine educational reforms and improvements worldwide and conceptualize the emerging transdisciplinary field through an extensive literature review, etymological analysis, international comparisons, and socio-historical, -cultural and -philosophical reflections. Findings In this paper, I advance the concept of neo-improvementalism for EIS by elucidating its philosophical assumptions, disciplinary fundamentals, and theoretical frameworks through historical and comparative lenses. I identify and construct disciplinary knowledge of EIS comprising two categories, namely, subject matter knowledge and profound knowledge, adopted from improvement science. I then highlight three methodological approaches of EIS and the building of professional improvement communities empowering individual and institutional improvement capabilities. I propose that EIS is the art of the improving organization for classes, schools, and/or more broadly defined educational agencies. Originality/Value This study recognizes the significance of EIS and research thereon, especially discipline-building and exploration based on local characteristics in a global vision, and the cultivation of new frontiers of educational research and practices.
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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 | 0.024 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.053 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".