Needs Assessment for Development of Primary School Administrators’ Attributes in 21st Century
Bibliographic record
Abstract
The objective of this research was to study the current condition, the desired condition, needs assessment, and guidelines of the development of primary school administrators’ attributes in the 21st century. This is descriptive research, and the research’s sample group consisted of 370 school administrators and teachers; they were selected using stratified random sampling. The research tools were semi-structured interviews and questionnaires, which have 0.98 of precision and 0.60-1.00 of IOC. The statistical measurements which were used in this research were frequency, percentage, average, standard deviation, and the value of needs assessment using the Priority Need Index (PNI_modified). The research found that the overall current conditions and conditions in each dimension were at a medium level, whereas the overall desired conditions were at the highest level, which later were analyzed for the needs assessment of the primary school administrators’ attributes in the 21st century in the following dimensions: 1) creativity and innovations, 2) visions, 3) being a desirable leader, 4) interpersonal relations, and 5) relations with work. Guidelines of the development of primary school administrators’ attributes in 21st century comprise of: 1) the development of creativity and innovations, 2) opportunity provision to reach the 21st century’s standards together in terms of the organization’s visions, 3) reinforcement of 21st century leadership’s attributes, 4) reinforcement of 21st century interpersonal relation’s attributes, and 5) increase of the effectiveness of services which relate to the work within the 21st century organizations.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".