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Record W2058322748 · doi:10.24908/eoe-ese-rse.v8i0.577

Fostering Educative Experiences in Virtual High School History

2008· article· en· W2058322748 on OpenAlexafffundvenue
Rosa Bruno‐Jofré, Karin Šteiner

Bibliographic record

VenueEncounters in Theory and History of Education · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsQueen's University
FundersQueen's UniversityAmerican Educational Research Association
KeywordsInformation and Communications TechnologyPedagogySociologyKey (lock)Cultural historyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

This position paper on virtual learning in high school history argues for situating integration of information and communication technologies (ICT) in an ethically defensible vision of education. Our main purpose is to establish a broad theoretical platform to enable critique of new technologies in history classrooms. However, we also argue in favor of embracing ICT integration within a theoretical framework that places teaching and learning as the driving force behind adopting new technologies. First, we remind history teachers in computer-supported classrooms that their teaching is grounded in educational aims and in well-formulated ideas about what constitutes educative experiences. We place the development of the historical mindedness of the student at the core of educational aims in history teaching. Further, it is our contention that high school history modules should become steeped in a vision of education that recognizes its cultural-psychological dimensions. This means dovetailing the construction of content knowledge with teaching the cultural practices of historians and the functions of history.
 
 Key words: technologies of information and communication, history teaching and learning, historical mindedness, cultural psychology, educative experiences

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)0.001
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 score0.208
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.074
GPT teacher head0.349
Teacher spread0.275 · 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

Citations7
Published2008
Admission routes3
Has abstractyes

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