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Record W4408998993 · doi:10.3390/educsci15040438

Formative Assessment in Upper Secondary Schools: Ideas, Concepts, and Strategies

2025· article· en· W4408998993 on OpenAlexaff
Davide Parmigiani, Elisabetta Nicchia, Myrna Pario, Emiliana Murgia, Chiara Silvaggio, Asia Ambrosini, Andrea Pedevilla, Ilaria Sardi, Marcea Ingersoll

Bibliographic record

VenueEducation Sciences · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsSt. Thomas University
Fundersnot available
KeywordsFormative assessmentMathematics educationComputer sciencePedagogyPsychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.654
Threshold uncertainty score0.898

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.457
Teacher spread0.432 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

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