Investigation of High School Students’ Geometry Course Achievement According to Their Learning Styles
Bibliographic record
Abstract
The aim of this study is to investigate high school students’ geometry course achievement according to their learning styles. 11th grade students in İzmir constitute the general universe of the research and the sampling of the research comprises of 11th grade students in Karabağlar district. Sampling of the research consists of total 50 high school students, determined by using appropriate sampling method. 60% (n=30) of these students are female and 40% (n=20) of them are males. Both quantitative and qualitative research methods were used depending on the main and sub-questions of the research. Kolb’s Learning Style Inventory was used in order to determine the learning styles of the students. As a result of the research, it was found out that most of the students who have diverging learning style were female students (77.8%), most of the students who have accommodating learning style were female students (75%), the number of male students (47.4%) and the number of female students (52.6%) who have assimilating learning styles are close and it was found out that, among the students who have converging learning style, female students (55.6%) were more than males. It was determined that there was no statistically significant difference between the geometry achievement scores according to learning styles and that the students' geometric achievement means were statistically significant according to gender. It was proposed that taking learning styles into account in the regulation of education environments can help to increase achievement.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".