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Record W2999526479 · doi:10.5539/jel.v9n1p205

Thematic vs Chronological History Teaching Debate: A Social Media Research

2020· article· en· W2999526479 on OpenAlexvenueno aff
İbrahim Turan

Bibliographic record

VenueJournal of Education and Learning · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsThematic analysisSociologyThe InternetSubject (documents)Teaching methodThematic mapSocial mediaQualitative researchSocial studiesPedagogyPsychologySocial scienceMathematics educationLibrary sciencePolitical scienceLawComputer science

Abstract

fetched live from OpenAlex

At the beginning of the 19th century, history education has found its place as an independent course in formal education institutions, since then it has undergone many reforms and revisions but none of these reforms has solved its chronic problems. In discussions on how to solve the problems of teaching history, one of the most important issues is whether history should be taught chronologically or thematically. The purpose of this research is to review the discussions on this topic in Internet blogs to examine teachers’ views on the advantages and disadvantages of these two approaches. The social media research method was applied in this research. Required data obtained through teachers’ blogs on thematic and chronological history teaching and teachers’ comments on the subject in these blogs. The thematic content analysis carried out with the NVivo program, which helped in-vivo codes emerged from teachers’ blogs and comments. Most of the 71 teachers whose remarks are referenced in the research have expressed a positive view on thematic history teaching, although it is more difficult to understand and apply. Some teachers proposed a mixed approach in which the thematic and chronological approach is combined in different order and proportions based on students’ grades.

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)0.003
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.359
GPT teacher head0.471
Teacher spread0.112 · 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.

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

Citations9
Published2020
Admission routes1
Has abstractyes

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