Strategies for the 21st Learning Skills Development of Students in Schools Under the Office of the Basic Education Commission
Bibliographic record
Abstract
The purposes of this research were 1) to study the components and indicators of learning skills in the 21st century of the students, 2) to study the current conditions, desirable conditions and the need for learning skills development in the 21st century of the students, and 3) to develop strategies for the 21st learning skills development of students in schools under the Office of the Basic Education Commission. The samples consisted of 426 administrators and teachers. The instrument was a 5 level rating scale questionnaire with IOC values between 0.80-1.00 with confidence equal to 0.98. The statistics used were percentage, frequency, mean, standard deviation, modified priority need index (PNImodified) and matrix analysis. The research results were as follows: 1) Components and indicators of learning skills in the 21st century of the students. There were 3 main components: (1) Learning and innovation skills consisted of 3 sub-components 19 indicators (2) Life and work skills consisted of 5 sub-components 38 indicators (3) Information skills in media, technology consisted of 3 sub-components 28 indicators 2) Current conditions of the 21st century of the students, in the overall and each aspect were at medium level. In desirable condition, in overall and each aspect were at high level. The priority needs for skills development were life-skills and work, learning skills and innovation, information skills media and technology. 3) Strategies for 21st learning skills development of students in schools under the Office of the Basic Education Commission consisted of the 1st strategy: Speed up the development of life and career skills to keep up with the changes. There were 4 goals, 11 measures, and 14 successful indicators. The 2nd strategy: Continuous learning skills and innovation development included 3 goals, 10 measures, 11 successful indicators. The 3rd strategy: Promote, develop of information skills, media and technology to be up to date included 3 goals, 10 measures, 10 successful indicators.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".