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Record W3205747848 · doi:10.14324/herj.18.2.06

Cultivating historical consciousness in the history classroom: Uncovering the subtleties of student meaning making with the help of found poetry

2021· article· en· W3205747848 on OpenAlexafffund
Nathalie Popa

Bibliographic record

VenueHistory Education Research Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsMcGill University
FundersMcGill University
KeywordsConsciousnessMeaning (existential)PoetryContext (archaeology)The artsEpistemologyPsychologyPedagogyMathematics educationSociologyAestheticsLiteratureVisual artsHistoryArtPhilosophyArchaeology

Abstract

fetched live from OpenAlex

This article explores student meaning making in a Grade 11 US history unit on the Second World War. The 10-lesson unit was designed as an experiment that aimed to apply an instructional model of historical consciousness to a classroom context. Although the notion of historical consciousness has gained significant interest in the field of history education, translating it into educational practice remains a challenge. In this study, it refers to a disposition to make meaning of the past for oneself, which is manifested in three meaning-making abilities and processes (Boix Mansilla and Gardner, 2007; Nordgren and Johansson, 2015; Rüsen, 2004). To study the manifestation of historical consciousness in the learning process during this unit, I employed found poetry on collected classroom transcripts and observations, as well as student work. I turned to this qualitative, arts-informed method when I realised the analytic methods that I had employed so far failed to capture important subtleties of students’ historical consciousness emerging from the data. In this paper, I present and discuss the results of my analysis, offer a rationale for using found poetry in history education research and reflect on the need for relevant and meaningful school history.

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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.256
GPT teacher head0.456
Teacher spread0.199 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

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