Investigation of Pre-service Social Studies Teachers’ Self-Efficacy Perception of and Attitudes Towards the Profession of Teaching
Bibliographic record
Abstract
The current study aimed to investigate pre-service social studies teachers’ self-efficacy perception of their ability to teach geography and attitudes towards the profession of teaching in relation to the variables of gender and university attended. The study is a descriptive study employing the survey model. The research sample was determined from the universities, where there were two public universities represented from each of the seven geographical regions, and a fourth-year student of social studies teaching in the spring semester of the 2017-2018 academic year. The study was carried out with the participation of 654 pre-service social studies teachers from 14 different universities across Turkey. In order to collect data, “The Geography Teaching Self-Efficacy Scale” and “The Scale of Attitudes towards the Profession of Teaching” were used. In the analysis of the collected data, arithmetic mean, standard deviation, independent samples t-test and one-way variance analysis (ANOVA) were used. According to the findings of the current study, self-efficacy perceptions related to geography teaching and their attitudes towards the profession are high. The mean scores taken from the whole self-efficacy perception scale and its sub-dimensions were found to be not varying significantly depending on the gender variable while they were found to be varying significantly depending on the variable of the university attended. On the other hand, the attitudes towards the profession of teaching were found to be varying significantly depending on both of the variables.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".