Does Higher Education Curriculum Contribute to Prospective Teachers' Attitudes, Self-Efficacy and Motivation?
Bibliographic record
Abstract
In Croatia, another comprehensive reform of the education system is being implemented. Although it proposes anumber of reforms to the school system, we think that providing better training to future teachers during their studieswould further contribute to the quality of education. The initial education of elementary and high school teachers isconsiderably different in terms of the number of courses where teaching competences are acquired, i.e. teachingcourses, and the number of classes of these courses students take every week. Therefore, the initial hypothesis of thestudy was that students who take more teaching courses will prefer a democratic atmosphere, and will be moreintrinsically motivated, more self-efficient and more satisfied with their course of study as compared to students whotake fewer of these courses. A series of questionnaires was given to 383 students at teacher education institutions atthe University of Split to examine their academic self-efficacy, motivation, satisfaction with the studies and to assessthe quality of teaching atmosphere. The obtained results confirm the hypothesis that future teachers who take moreteaching courses prefer a democratic atmosphere and are intrinsically more motivated. When explaining futureteachers’ attitudes towards a democratic teaching process, the predictive role of self-efficacy and satisfaction withthe course of study was not determined.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".