The Relationship between Metacognitive Awareness Levels, Learning Styles, Genders and Mathematics Grades of Fifth Graders
Bibliographic record
Abstract
Previous studies have shown that students, who have high levels of metacognitive awareness, perform better achievement levels than other students. Besides, it can be said that learning styles may affect metacognitive awareness of students. In the literature, studies about metacognition focused on problem solving and learners’ mathematical achievement, improvement in metacognition, and supporting some learning environments with metacognition. Therefore, in this study, relationship between metacognitive differences, learning styles, genders and mathematics grades of the fifth grade students are examined. This study was designed as descriptive study and conducted by using relational screening model. The participants consist of 330 fifth grade students from public middle schools. Data collection tools of this study are “Metacognitive Awareness Scale for Children” and “Learning Styles Scale”. The data gathered through these scales were analyzed by using Statistical Package for Social Science (SPSS) 21.0. As a result, there is no statistically significant relationship between learning styles and gender. But, there is statistically significant relationship between learning styles-mathematics grades, metacognitive awareness levels (MAL)—grade levels in mathematics, MAL-gender and MAL-learning styles. Learning styles may affect individuals’ way of thinking in every moment of the life. Thus, this result has a significant part in education. In fact, parents, teachers and administrators should know metacognitive awareness and learning styles. Thus, knowing these terms can be helpful to understand how the problematic and unsuccessful students show undesirable behaviors since those students’ learning styles and metacognitive awareness levels are not considered.
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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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".