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Record W4399997366 · doi:10.1007/978-3-031-58056-7_9

Going Beyond the Scoring Grid: How the Topic of Assessment Can Promote Reflection on Epistemic Beliefs and Agency in History Education

2024· book-chapter· en· W4399997366 on OpenAlexafffundabout
Catherine Duquette, Marie-Hélène Brunet, Arianne Dufour, Benjamin Lille

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsCegep de Trois-RivieresUniversity of OttawaUniversité du Québec à Chicoutimi
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Ottawa
KeywordsAgency (philosophy)Reflection (computer programming)EpistemologyRepertory gridGridPsychologyPolitical scienceSociologyComputer scienceData scienceSocial psychologyPhilosophyGeography

Abstract

fetched live from OpenAlex

Abstract An overarching aim of professional development for teachers in history education is to foster a more complex understanding of the discipline. Yet, substantial research underlines the stability of history teachers’ epistemological beliefs, demonstrating the difficulties they experience in shifting from their existing positivist approach to a more critical mindset. Could some contexts be more favorable to enhancing epistemic agency through in-depth reflection? This chapter discusses the potential of examining assessment as a method of encouraging teachers to question their epistemological understanding of history. Based on the results of a study conducted with six Quebec teachers, it illustrates how questioning assessment practices in a collaborative approach creates a space favorable to epistemological wobbling.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.008
Scholarly communication0.0070.007
Open science0.0020.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.126
GPT teacher head0.375
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2024
Admission routes3
Has abstractyes

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