Analysis the Aspect of Higher Order Thinking Skill on Fungi Content Assesment Instrument for Senior High School Grade 10
Bibliographic record
Abstract
ABSTRACT High order thinking skills is very important based 2013 Curriculum in Indonesia. High order thinking skills need to be developed so that learners not only receive the information provided, but can use it and convert it into new information to solve the problems they face. The type of research used is descriptive research, by collecting data in the form of assessment instruments used by teachers in assessing the learning process. The assessment instruments on fungi material made by teachers for daily tests are generally still at the C1-C3 cognitive level (C1 is 40%, C2 is 46,7% and C3 is 13,3%), whereas high order thinking skills can be trained by providing an assessment instrument that is at the C4-C6 level of cognition in learning. Researchers analyze students by processing student's value data when answering C1-C3 problem commonly used by biology teacher in school and the result of the average score of learners did not experience problems, but the result of the analysis of learners with the provision of high ability thinking ability showed not yet capable of students answer about high-order thinking skills. This is evidenced by the test results obtained in the form of the average value of the class that is 28.15. Therefore, the assessment instrument used by the teacher has not been able to measure the high order thinking ability of the learners.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".