The Effect of Problem Based Learning (PBL) Charged Science Literacy Toward Students’ Learning Competences at XI Grade of SMAN 1 Lubuk Alung
Bibliographic record
Abstract
This research based on the problem on SMAN 1 Lubuk Alung, those are inappropriate learning model choices and low of students’ learning competencies. An efforts could done to solve it was implementation of problem based learning model charged with science literacy toward the students’ learning competencies on the topic of excretion system at XI grade of SMAN 1 Lubuk Alung. This research aimed to know the effects of Problem Based Learning (PBL) charged with science literacy toward the students’ learning competencies at XI grade of SMAN 1 Lubuk Alung. The type of this research was experimental research and designed on Control Group Posttest Only. The populations of this research were all of the students at grade XI MIPA of SMAN 1 Lubuk Alung academic years 2017/2018 that consisted of seven classes. This research used purposive sampling technique to take the samples; those are XI MIPA 3 as experiment group and XI MIPA 2 as control group. The research’s instruments used were test for cognitive competence and observation sheets for affective and psychomotor competence. Hypothesis was tested by using T-test method.Based on the thesis result by using t-test, it can be concluded that on students’ cognitive competence tscore 4,15> table 1,67, on students’ affective competence tscore 5,07 > table 1,67, and on students’ psychomotor competence tscore 1,69 > table 1,67. It showed that hypothesis was accepted. So it can be concluded that the implementation of Problem Based Learning (PBL) charged science literacy model improved toward students’ learning competences at XI grade of SMAN 1 Lubuk Alung significantly with 0,05 degrees of truthiness.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".