Cognitive Development, Analytical Thinking, and Learning Satisfaction of Second Grade Students learned through Inquiry-based Learning
Bibliographic record
Abstract
Science teaching needs to be able students having knowledge and understanding. Also, students have to develop their thinking skills it should help students meet real science through inquiry-based pedagogical process. This study aims to (i) investigate effective teaching criterion through inquiry-based teaching at 80/80, (ii) find out effectiveness index of inquiry-based teaching, (iii) compare analytical thinking between before and after students had learned by inquiry-based learning activities, and (iv) study learning satisfaction of second grade students after they had learned through inquiry method. Participants of the study were 10 second grade students, sampled by purposive sampling technique. Research instruments comprised of 8-lesson plan, 20-item achievement test, 20-item analytical thinking test, and 15-item questionnaire on learning satisfaction. Data were gathered and analyzed by Wilcoxon Matched Pairs Singed–Ranks Test. Results revealed that inquiry-based learning activities had effective criterion at 84.46/82.50; effectiveness index of inquiry-based learning activities was 0.5200; post test score of achievement test higher than those pre test score at .05 statistical significance level; and students had learning satisfaction on inquiry-based learning activities at highest level. It can be concluded that inquiry-based learning activities promoted students in terms of both cognitive, analytical thinking, and learning satisfaction. It should be suggested in for pedagogical preparation and incorporate it into science curriculum.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".