Investigation of Middle School Students’ Math Self-Efficacy Perceptions and Math Problem Posing Attitudes
Bibliographic record
Abstract
The main purpose of the current study is to investigate middle school students’ math self-efficacy perceptions and math problem posing attitudes. The sample of the study is comprised of 990 fifth, sixth, seventh and eighth grade students attending middle school in the Fethiye district of the city of Muğla. As the data collection tool, the “Math Self-efficacy Perception Scale” and the “Math Problem Posing Attitude Scale” were used. In the analysis of the data collected through the quantitative research method, frequencies, percentages, independent-samples t-test, one-way variance analysis, post-hoc tests (Scheffe and Dunnett’s C) and correlation analysis were used. As a result, it was found that the middle school students’ math self-efficacy perceptions and math problem posing attitudes are over the medium level. The middle school students’ self-efficacy perceptions were found to be varying significantly depending on gender. The middle school students’ problem posing attitudes were found to be varying significantly depending on gender. The math self-efficacy perceptions and math problem posing attitudes of the 5th and 6th grade students were found to be significantly higher than those of the 7th and 8th grade students. A medium, positive and significant correlation was found between the middle school students’ mean math self-efficacy perception score and their mean math problem posing attitude score.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".