Effect of the Van Hiele Model in Geometric Concepts Acquisition: The Attitudes towards Geometry and Learning Transfer Effect of the First Three Grades Students in Jordan
Bibliographic record
Abstract
This study aimed to investigate the Effect of the van Hiele model in Geometric Concepts Acquisition, and the attitudes towards Geometry and learning transfer of the first three grades students in Jordan. Participants of the study consisted of 60 students from the third grade primary school students from the First Directorate, Amman, in the academic year (2015-2016) and they were divided randomly into a control group and an experimental group. To achieve the objectives of the study, the teacher's guide was prepared for the unit of Engineering and Statistics "taken from the text book of Mathematics of the third grade in accordance with the model of the Hill, and the preparation of test engineering concepts which consisted of 17 questions of multiple choice, in addition to the scale of attitudes towards engineering and test learning transfer effect. Data were analyzed using ANCOVA and results were as follows: there are significant differences between the average performance of each of the two groups of the study on the scale of Geometric Concepts acquisition in favor of the experimental group taught by using the van Hiele model. And there are significant differences between the average performance of the two groups on a scale of attitudes towards geometry in favor of the experimental group taught by the van Hiele model. There are significant differences between the average performance of each of the two groups on the learning transfer test in favor of the group taught by the van Hiele model.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".