Inquiry-Based Learning Model to Improve Higher Order Thinking Skills
Bibliographic record
Abstract
This study aims to present ways of implementing inquiry- learning model with the use of scientific reports to improve teachers’ understanding and ability on teaching biology at secondary level. The quantitative research method is quasi-experiment design with pre-test and post-test control group. The research instrument for collecting data of students’ higher order thinking skills is scoring rubrics for assessing abilities on developing and presenting a scientific report. The instruments for assessing teachers’ skills are teacher observation sheets over inquiry-based learning scientific report using an induction method. The research subjects consist of 4 biology teachers and 80 of grade 10 students from Public Secondary School 3 Samarinda.The teachers are all female; while from 80 students, 53 of them are female and the rest 27 are male. The students’ age ranges from 16 to 18 years old. The research lasted for 1 month.Analysis of data uses t test, that if toutcome is higher than ttable, the inquiry-based learning model using scientific reports does affect students’ higher order thinking skills. Data analysis is composed in tabulation format with several graded categories: inadequate, sufficient, good and excellent. The result of the study is that higher order thinking skills of students are increasing in numbers and more equal compared with classes taught by teachers who did not follow the inquiry-based learning model workshop and presentation. The inquiry-based learning model was applied via preparation and presentations of scientific reports after the students carry out practical activities through the guidance of student activity worksheets.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".