Effectiveness of Using Discovery Learning Model Assisted Tracker on Improvement of Physics Learning Outcomes Observed From Students’ Initial Knowledge
Bibliographic record
Abstract
The purpose of this study is to determine the effectiveness of the use of discovery-assisted discovery learning models to improve physics learning outcomes in terms of students' initial knowledge. This research was conduct at Senior High School 1 Talibura in the academic year 2019/2010. This research is an experimental research that uses a quasi-experimental design consisting of a nonequivalent (pretest-posttest) control group design. Sampling uses simple random sampling so that two sample classes obtained, namely level XI MIA 1 as an experimental class and class XI MIA 2 as a control class. The first knowledge instrument and learning outcomes are subjective tests (essays) that have been tested for validity and reliability. Hypothesis testing using ANCOVA \ntest. Based on data analysis, the results showed that there was an influence of the tracker assisted discovery hearing model on student physics learning outcomes, where Fcount is higher than Ftable (4,484 > 3,20) with the significant value obtained is smaller than the significance level (0,017 < 0,05). From this study, we can conclude that the discovery-assisted discovery learning model tracker is handy to be used in physics learning to improve student physics learning outcomes.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".