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Record W2991908853

A Time for Change: Transforming a New Generation of Students into Historical Thinkers

2011· article· en· W2991908853 on OpenAlexfundno aff
Lauren Seghi

Bibliographic record

VenueThe Keep (Eastern Illinois University) · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsnot available
FundersStanford Bio-XUniversity of TorontoTemple University
KeywordsRote learningMemorizationSocial studiesStereotype (UML)Perspective (graphical)World historyOrder (exchange)SociologyPedagogyPsychologyMathematics educationTeaching methodHistorySocial scienceVisual artsArtSocial psychology
DOInot available

Abstract

fetched live from OpenAlex

The history teaching profession has long been criticized for promoting an unwavering procession of educators who emphasize lectures, note-taking, worksheets, and recitation. This way of teaching, while practiced in many classrooms, is being challenged by teachers who view their classrooms as a history laboratory where students and teachers co-investigate the past by analyzing primary and secondary sources. Due to the excellent teacher preparation at many universities across the country, a new generation of history teachers encourages their students to discuss sources, ask questions of sources, and write about the past using practices of historians. Moreover, this new generation of history teachers promotes historical thinking rather than general models of thinking. Teaching students how to think historically will make them not only more knowledgeable about the past but will help them come to a greater understanding of the world around them. Recent efforts on the part of history educators have assisted teachers towards the aim of helping students learn to think historically. From the outset of my essay, I maintain there is a way to improve inquiring about the past and practicing the discipline of history through a method called the 1st-, 2nd-, 3rd-Order documents approach.1 This approach helps

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)0.000
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.944
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.227
GPT teacher head0.330
Teacher spread0.103 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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