Incorporating a Metacognitive Learning Model to Improve Geometric Thinking in High-School Students
Why this work is in the frame
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Bibliographic record
Abstract
Thinking development processes among high-school students is an important and significant issue that has been widely investigated (Leviathan, 2012; Ball, 1996; De Risi, 2015). A few studies discuss the development of mathematical thinking as this field contains additional difficulties to the traditional factors, teachers, students, and parents, and is one of the most important areas taught in school, according to De Risi (2015). Due to the importance of this subject, the challenge facing researchers, mathematicians, and educators is how to improve students’ abilities and achievements in mathematics. In recent years, researchers have found that in order to improve students’ achievements and abilities in mathematics, one can use self-direction. Self-direction is a strategy by which the learner acquires the ability to cope with learning from several aspects and contributes to inking development. In this study, we showed that self-directed learning with an emphasis on metacognition would improve students’ understanding of the subject in question. Using the metacognitive guidance model, the students acquire and develop learning skills that contribute to developing their geometric thinking. In this study, there is the added value of using a learning model based on metacognitive guidance and its significant contribution to combining multiple subjects into one problem.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it