Improvement of Learning Skills in Geometry Incorporating a Metacognitive Learning Model in Boys Compared to Girls
Bibliographic record
Abstract
“We learn by doing and by thinking about what we are doing.” (John Dewey) In this article, we shall present findings that describe the degree to which metacognitive orientation contributes to the study of the geometry the plan in boys compared to girls in 9th grade of middle school. The geometry study process does not only involve knowledge but also high thinking abilities. Beyond the knowledge of definitions and sentences, the students are required to write a full, precise, and logically constructed proof, as well as to show the validity and its correctness. In this article, we shall present a model of metacognitive orientation aiming to develop higher-order thinking skills in geometry. We built and applied the model to 9th-grade students. Since students experience difficulties in the study of geometry, the development of a structured study process is required. Numerous studies clearly show that the study process involving metacognitive orientation improves their study ability and deepens their understanding of the topic in question. The question that we addressed was to what extent the metacognitive orientation in geometry impacted boys in comparison to girls? In this study, we shall present data according to which metacognitive learning explicitly benefits girls more than boys. Nevertheless, as a modular model, it allowed every student of both sexes to strengthen the weak aspect and to overcome blockades inhibiting the learning process.
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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 | 0.002 |
| 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) | 0.004 | 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".