Effectiveness of Non-Formal Education Program to Enhance Career Planning Abilities of Lower Secondary School Students
Bibliographic record
Abstract
The 3Ds Career Planning Program is a sequential learning activity consisting of three stages: Diagnosing (8 activities), Designing (9 activities), and Doing (2 activities). These stages help students determine their future careers by exploring themselves and the world of careers, creating paths to desired jobs, and putting their plans into action. The purpose of this research is to study the effectiveness of this non-formal education program in improving lower secondary school students’ career planning abilities at Angthong Patthamarot Witthayakhom School. Twenty lower secondary school students volunteered to join the program. The 3Ds career planning instruments, which included questionnaires, tests, and reflection notes, were used to collect data. The findings indicate that the 3Ds Career Planning Program effectively improves students’ career planning abilities overall. Learning outcomes were higher than 80%, and the overall career planning ability level was significantly higher (Pre-M = 2.38, S.D. = 0.96; Post-M = 4.37, S.D = 0.64). Students’ career planning journals showed three levels of planning and implementation abilities: high, medium, and low. Moreover, the journals provided insights into several areas of learning reflection towards career planning: awareness of the importance of career planning, benefits of career planning, career decision-making and plans, feelings of plan accomplishment, and application to daily life.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".