Examining the Problem Solving Skills of Primary Education Mathematics Teacher Candidates according to Their Learning Styles
Bibliographic record
Abstract
In this study, it is aimed to investigate the problem solving skills of primary education mathematics teacher candidates according to their learning styles. The students of Primary Education Mathematics Department students of the universities in TRNC constitute the general universe of the research and the students who study in Primary Education Mathematics Department of a private university in TRNC constitute the universe of the study. Sampling of the research consists of a total 26 students studying in Primary Education Mathematics Department in 2017-2018 academic year, determined by using the appropriate sampling method. In order to determine the learning styles of primary education mathematics teacher candidates, 12-item Kolb Learning Inventory, developed (1976) and rearranged (1985) by Kolb and the applicability of which was proven by Aşkar and Akkoyunlu (1993) in Turkey was used in this research. Problem solving inventory used to determine teacher candidates’ problem solving skills was developed in 1982 by Heppner and Petersen and adapted to Turkish by Şahin, Şahin and Heppner. As a result of the study, when students’ learning styles are examined it was observed that the students with converging learning style are 42.3%, students with assimilating learning style are 38.5%, students with diverging learning style are 11.5% and students with an accommodating learning style are 7.7%. It was observed that the students who participated in the study showed a tendency towards problem solving confidence. It has been suggested that taking learning styles and problem solving skills in organizing educational environments into account can help in increasing success.
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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) | 0.002 | 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".