A Research on Learning Approaches and Achievement Focused Motivations of Prospective Visual Arts Teachers
Bibliographic record
Abstract
Investigating the relationship between the learning approaches and achievement-focused motivations of the prospective visual arts teachers is the purpose of this study. The data of the research, in which the survey model was employed, were collected from 115 prospective visual arts teachers. As the data collection tool, the Achievement Focused Motivation Scale (AFMS), and the Learning Strategies Scale (LSS) were applied. The descriptive analysis was used to test the learning approaches and achievement-focused motivation levels of the prospective visual arts teachers, the independent samplings t-test to test the achievement focused motivation difference according to the gender, the One-way Variance Analysis to investigate the difference according to the class level. According to the research, it was found that the achievement focused motivation levels of the prospective visual arts teachers was pretty high; nevertheless, no significant difference was found in the achievement focused motivation levels according to the gender and class level. However, a significant difference was encountered in the Learning Approach dimension according to the class level. In addition, it was found in the study that a positive, high level of relationship was observed between the Achievement Focused Motivation and the Learning Approaches, In-depth Teaching Approach and Strategic Approach.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".