Formative Assessment in Upper Secondary Schools: Ideas, Concepts, and Strategies
Bibliographic record
Abstract
Recently, the European Commission issued a report on how to implement various assessment strategies in secondary schools. In particular, the Working Group on Schools emphasized the need to implement and balance assessment strategies that address both formative and summative aspects of learning. This study involves 716 Italian upper secondary teachers to highlight the ideas and concepts of formative assessment in Italian schools, to explore how teachers apply formative assessment strategies daily in their classrooms, and to understand whether formative assessment strategies support students’ learning processes. Through an analysis of quantitative and qualitative survey data collected during the school year of 2023–2024, the study explores the strategies used and strengths and weaknesses experienced by the teachers while applying formative assessment in classrooms. While the findings show that upper secondary teachers have heterogeneous and dissimilar ideas regarding formative assessments, they also indicate that using strategies based on feedback, self-assessment, Socratic methods, and metacognitive activities can foster students’ critical thinking and learning processes. Additionally, this study offers insights on how to create a meaningful link between summative and formative assessment procedures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".