The Correlation between Pre-Service Science Teachers’ Astronomy Achievement, Attitudes towards Astronomy and Spatial Thinking Skills
Bibliographic record
Abstract
The purpose of this study was to examine the changes in pre-service Science teachers’ astronomy achievement, attitudes towards astronomy and skills for spatial thinking in terms of their years of study. Another purpose of the study was to find out whether there was correlation between pre-service teachers’ astronomy achievement, attitudes towards astronomy and skills for spatial thinking. The study was conducted with 280 pre-service teachers (freshmen, sophomores, juniors and seniors) studying at the education faculty of a university in Black Sea Region of Turkey. The data were collected through Astronomy Achievement Test, Astronomy Attitude Test and Purdue Spatial Visualization Test: Visualization of Rotations. One way factor analysis (One-Way Anova) technique for independent groups was used for data analysis in order to find out whether pre-service teachers’ astronomy achievement, attitudes towards astronomy and skills for spatial thinking differed in terms of their years of study. In order to find out whether there was correlation between pre-service teachers’ astronomy achievement, attitudes towards astronomy and skills for spatial thinking, simple linear correlation analysis was conducted and Pearson Moment Correlation Coefficient was checked. The results of the data analysis showed that pre-service teachers’ astronomy achievement, attitudes towards astronomy and skills for spatial thinking differed significantly in terms of their years of study. In addition, a low positive correlation was found between pre-service teachers’ astronomy achievement and attitudes towards astronomy while a high positive correlation was found between pre-service teachers’ astronomy achievement and their skills for spatial thinking. In line with these results, recommendations were made to increase the number of astronomy classes in education faculties and to teach astronomy not only in fourth year but also in the first three years.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".