An Investigation of Academic Self-Efficacy Perceptions of Primary Mathematics Teacher Candidates
Bibliographic record
Abstract
The aim of this study was to investigate the academic self-efficacy perceptions of primary school mathematics teacher candidates according to different variables. For this purpose, the “Academic Self-Efficacy Scale”, which was developed by Jerusalem and Schwarz (1981) to measure the academic self-efficacy beliefs of mathematics teacher candidates, which was adapted to Turkish by Yılmaz, Gürçay and Ekici (2007) and validated by the reliability and validity scale, were used as data collection tools. The study was conducted in the fall semester of 2017-2018 academic year. The sample of the study consists of 157 teacher candidates studying at the 1st, 2nd, 3rd and 4th years of the Elementary Mathematics Teaching Program of the Faculty of Education at a state university. Independent sample t-test and ANOVA were used for the analysis of collected information. As a results of data analysis, student’s academic self efficacy perceptions were found generally high. In addition, it was concluded that the mean scores of academic self-efficacy perceptions of mathematics teachers did not show a statistically significant difference according to their gender and grade level, but there was a significant difference between the age, whether they willingly chose what they are studying or not and mathematics achievement groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".