Self‐ and peer‐assessment in upper secondary schools. A quasi‐experimental study to investigate the educational effectiveness of formative assessment
Bibliographic record
Abstract
Abstract The assessment of student learning represents a key component of daily instructional practice. Formative assessment strategies are associated with the development and reinforcement of a series of skills linked to cognitive, metacognitive, behavioural and affective areas. This study investigated the effectiveness of formative assessment strategies in supporting the development of students' emotional and motivational capacities, resilience, self‐efficacy, self‐reflection, metacognitive awareness and self‐regulation. Specifically, it examined whether formative assessment strategies could enhance these skills and improve students' academic preparation for a summative mid‐term test. This quasi‐experimental study involved 581 students from 37 Italian upper secondary school classes in an investigation that tested the use of self‐ and peer‐assessment to support students' improvements in study organisation and effective summative test preparation. Findings indicate that formative assessment activities supported student preparation for a mid‐term summative test. Specifically, improvements in emotional attitudes, self‐efficacy, metacognitive awareness and aspects of self‐regulation related to rehearsal, elaboration and organisation of learning materials were reported. The data indicate that this skill development was attributed mainly to the implementation of a peer‐assessment strategy.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".