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Record W3183278843 · doi:10.5539/ies.v14n8p51

Social Studies Teacher Candidates’ Views About Information Technologies and Material Used in Social Studies Lesson

2021· article· en· W3183278843 on OpenAlexvenueno aff
Mehmet Oran, Mehmet Akif KARALI

Bibliographic record

VenueInternational Education Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologySocial studiesQualitative researchPhenomenology (philosophy)Semi-structured interviewMathematics educationContent analysisReliability (semiconductor)PedagogyEducational researchSociologySocial science

Abstract

fetched live from OpenAlex

The research was prepared in order to reveal the opinions of social studies teacher candidates on the use of information technologies and materials in social studies lessons. The study group of the research consists of teacher candidates studying in the department of social studies teaching at Cukurova University in Turkey. The research was prepared in accordance with phenomenology, one of the qualitative research designs. A structured interview form, which was previously created by taking expert opinion, was used to collect data. There are 5 questions that complement each other in the interview form. The data obtained from the interview form were subjected to content analysis. The reliability of the study was calculated as 95.5% according to the reliability formula of Miles and Huberman (1994). It was concluded that the prospective teachers who participated in the research supported the use of information technologies in the social studies course. It was seen that the tools in the classroom environment as technology and material were expressed by the majority of the participants.

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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.279
GPT teacher head0.522
Teacher spread0.243 · 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 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".

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Citations1
Published2021
Admission routes1
Has abstractyes

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