Scenarios of Secondary School Management During the Digital Era in the Next Decade (2022-2031)
Bibliographic record
Abstract
Nowadays, technological advances in providing transformations in the school scenario, and one of them are the drastic change in secondary school management during the digital era. The purposes of this research were 1) to study the current situations and problems of secondary school management during the digital era; 2) to study the scenarios of secondary school management during the digital era in the next decade (2022-2031). The research specifically looked into 4 core missions of secondary school: 1) management of teaching and learning 2) personnel management 3) budgeting and 4) educational management. The research was conducted in 2 phases. Phase I: studying the current situations and the problems of secondary school management during the digital era by using a questionnaire to collect data from a sample group of 230 secondary school administrators. Phase II: studying the scenarios of secondary school management during the digital era in the next decade (2022-2031) using Ethnographic Delphi Futures Research (EDFR) for 3 parts. Part 1: Interviewing 19 experts using a semi-structured interview form and then analyzing and synthesizing the future tenders (scenarios). Part 2: Assessing the feasibility and the appropriateness of the scenarios by using a 5-level scale questionnaire. The statistics used were median and interquartile range. Part 3: Confirm trend scenarios of secondary school management during the digital era in the next decade (2022-2031). The findings revealed that the current situations of practice are all at a high level and the problems of the management are all at a moderate level. The scenarios of secondary school management during the digital era in the next decade (2022-2031) consist of 4 key elements and 40 possible trends, namely: 1) management of teaching and learning in the digital era with 15 possible trends. 2) educational personnel in the digital era with 10 possible trends. 3) budget in the digital era with 6 possible trends and 4) educational management in the digital era with 9 possible trends.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".