The Application of Problem-Based Learning Strategy to Increase High Order Thinking Skills of Senior Vocational School Students
Bibliographic record
Abstract
This research is to apply and develop a strategy of problem-based learning to increase the ability of higher order thinking skills of senior vocational schools students. The research was done due to a fact that the quality of outputs of the senior vocational schools has not met the competency needed by the stakeholders in the field, that has made the outputs difficult to get jobs, or fail to run a private business of their own. This research is a quasi experiment applying Nonequivalent Control Group Design, done at X TKR 1 class of 38 students and X TKR 2 class of 38 students of Senior Vocational School II, Kupang, NTT Province, Indonesia. The normality and homogeneity of tests were done to obtain the test of analysis requirement. T-test was done to analyze the data obtained. The results show that: (1) the use of problem-based learning strategy is superior to the conventional study; (2) the application of problem-based learning strategies capable of improving high order thinking skills of students, which is implemented in problem solving skills, teamwork, and self-confidence better. (3) in the future, the high order thinking skills will be very important in winning the job competition, find solutions to problems in the workplace and establish good cooperation with others, so it will support the success of their careers in the future.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".