The Effect of Using the Constructivist Learning Model in Teaching Science on the Achievement and Scientific Thinking of 8th Grade Students
Bibliographic record
Abstract
The study aims to investigate the effect of using constructivist learning model in teaching science, especially in the subject of light: its nature, mirrors, lens, and properties, on the achievement of eighth-grade students and their scientific thinking. The study sample consisted of (136) male and female 8th graders were chosen from two basic schools in Tafila in the scholastic year 2015/2016. The four-class sample was divided into two groups (controlled & experimental). For achieving the study aims, the researcher prepared lesson plans using constructivist learning model, achievement test and scientific thinking test, which validity and reliability were checked. To answer the questions of the study, means, SD, ANOVA and ANCOVA were used to determine the differences in means of the groups of the study. The results show that there is statistically significant difference at (α= 0.05) for the effect of the constructivist Learning model on the achievement and scientific thinking in favor of experimental group, and there is no statistically significant difference at (α= 0.05) for the constructivist Learning model on the achievement and scientific thinking attributed to gender, and there is no statistically significant difference at (α= 0.05) for the dual interaction between teaching method and gender on the achievement and scientific thinking. In the light of the study results, the researcher presented a number of recommendations including: extra attention should be given to employ constructivist learning model within science courses, and conducting further studies about the effect of the constructivist Learning model on various 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".