An Examination of High School Social Science Students’ Levels Motivation towards Learning Geography
Bibliographic record
Abstract
This aim of this research was to examine the levels of motivation among high school social science students towards learning geography. The study group consisted of 397 students from different classes at Aksaray Ahmet Cevdet Pasa High School in the College of Social Science. The research was carried out with a scanning model, with data obtained using the Scale for Motivation Towards Learning Geography. In the analysis of the data, the t-test and the one-way analysis of variance (ANOVA) were used. As a result of the research, the levels of motivation among social science students towards learning geography were found to be moderate. From the analysis of the aforementioned scale’s subfactors, those related to the interest of students and information acquisition were found to be ‘undecided’, while the subfactors related to self-confidence and performance were found to be ‘in agreement’. It was determined that the level of motivation towards learning geography reported in the findings, with regard to the gender variable, showed a significant difference among male students. In addition, it was indicated that the motivation levels of male students were higher for the subfactors of interest and self-confidence than those of female students. In terms of the subfactors of information acquisition and performance, no significant changes were found in the motivation levels among both male and female students. Analyses based on class level demonstrated that the average scores of the students differ in this context, but that this difference was found to be statistically significant for 11th grade students for the subfactor of self-confidence.
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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 | 0.001 | 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".