The Impact of Self-Assessment with Goal Setting on Academic Achievement: Results of a Study on Primary School Students in Greece
Bibliographic record
Abstract
The main purpose of the present study is to investigate the contribution of Self-Assessment with Goal Setting (SAGS), which was implemented with the “self-directed learning” (SDL) method, to the improvement of academic performance in the context of language teaching to sixth-grade students in primary school. The research methodology followed is an experimental design with two groups, one experimental and one control. The research was implemented in public primary schools in Rhodes and lasted for six weeks (from the beginning of March 2022 to the middle of April 2022). The research sample is a convenience sample consisting of 163 students (the control group made up of 78 students and the experimental group consisting of 85 students). According to the findings of the study, the students in the experimental group improved their performance, and this improvement was statistically significant. Specifically, it was observed that after the implementation of SAGS, the mean score for academic achievement of the students in the experimental group was higher than the mean score of academic achievement during the pretest period. Furthermore, the vast majority of students in the experimental group improved their academic achievements, while a very small number of students experienced a decrease in performance and an even smaller number of students did not experience any change in their performance after the intervention. Finally, the students in the experimental group also performed better posttest and improved their performance, in contrast to the students in the control group for whom both lower performance and a deterioration in their performance were observed compared to the experimental group.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".