Investigation of Prospective Teachers’ Beliefs Towards Authentic Assessment
Bibliographic record
Abstract
The aim of this study is to examine the prospective teachers’ beliefs toward authentic assessment in relation tovarious variables. The survey method has been used in this study and the sample of the study is comprised of 612prospective teachers 368 (60.1%) of whom are female and 244 (39.9%) of whom are male. The "AuthenticAssessment Belief Scale" (AABS) comprised of 17 items and 3 subscales and developed by the researcher has beenused as a data collection tool. As for the analysis of the data SPSS 20.0 package program has been preferred. Theprospective teachers’ beliefs toward the authentic assessment in terms of gender variable has been analyzed withindependent samples t-test and in terms of department and grade variables one way ANOVA has been used. 05degree of significance is used for the analysis. In case of observing significant differences LSD postdoc test has beenused to determine the source of difference and effect size has been calculated to determine the magnitude of thedifference. So as to determine the level of prospective teachers’ beliefs toward the authentic assessment, mean andstandard deviation values have been calculated. According to the findings of the study, the prospective teachers’beliefs toward the authentic assessment have been observed to be high. Moreover prospective teachers’ beliefs havebeen determined to differ significantly in terms of gender, grade and department 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".