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Record W2149953989

Formative assessment: A systematic and artistic process of instruction for supporting school and lifelong learning

2012· article· en· W2149953989 on OpenAlexvenueno aff
Ian Clark

Bibliographic record

VenueCanadian Journal of Education / Revue canadienne de l éducation · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsnot available
Fundersnot available
KeywordsFormative assessmentLifelong learningProcess (computing)Adaptation (eye)Mathematics educationPedagogyEngineering ethicsKnowledge managementPsychologyComputer scienceEngineering
DOInot available

Abstract

fetched live from OpenAlex

Formative assessment is a potentially powerful instructional process because the practice of sharing assessment information that supports learning is embedded into the instructional process by design. If the potential of formative assessment is to be realized, it must transform from a collection of abstract theories and research methodologies and become a creative and systematic classroom practice. Policy-makers and school administrators must support this transition from theory into practice, particularly in the early stages of professional adaptation, and design assessment systems that teachers may internalize and enact efficiently. The article explores the hypothesis that many public school teachers are ‘trapped’ within environments which deter them from enacting open and inventive social learning strategies in their own classrooms, which when implemented have great potential to support autonomous learning, realize achievement, and create economically productive lifelong learners. This article therefore reviews the literature on formative assessment in practical settings, and investigates the extent to which teachers perform the basic functions of gathering and using evidence to further learning and development in pursuit of the lifelong learning competencies that are essential in the ‘new economy.’

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.034
GPT teacher head0.358
Teacher spread0.324 · 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 designQualitative
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

Citations23
Published2012
Admission routes1
Has abstractyes

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