The Effect of Formative Assessment on the Academic Achievement Levels of Prospective Teachers
Bibliographic record
Abstract
This study aims to examine the effect of “Formative Assessment (FA)” practices in “Assessment and Evaluation in Education” class on the academic achievement levels of prospective teachers. It uses a mixed research design. Quantitative data were collected by using double pretest-posttest design, which is one of the complete experimental design structures; while a semi-structured Interview Form was used to collect the qualitative data. The study group consists of a total of 220 prospective teachers who participated in a “Teacher Training Course” in Faculty of Education in Yildiz Technical University, Turkey during the 2017-2018 academic year. The data collection instruments included a 40-item multiple-choice achievement test (AT) chosen from a question bank in accordance with the course objectives and a semi-structured interview form. For the achievement test, reliability was achieved by the test-retest method (r=.95), and validity was secured by the “analytical” method based on expert opinion. Following the nine-week FA practices using the complete experimental double pretest-posttest research design, it was found that these practices (what do I recall? and what have I learned?) resulted in a significant difference in favor of the experiment groups. The responses of prospective teachers to the semi-structured interview form developed to collect qualitative data for the study were categorized into common themes, which demonstrate that the quantitative data are confirmed by the qualitative data regarding the FA practices. This harmony between the quantitative and qualitative data showed that FA practices (independent variable) are strong enough to affect the achievements of prospective teachers (dependent variable).
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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 | 0.028 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".