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Record W4402685960 · doi:10.17344/acsi.2024.8783

Exploring Teachers’ Technological Pedagogical Content Knowledge as an Indicator for the Planning of In-service Teacher Training in Chemistry Education

2024· article· en· W4402685960 on OpenAlexaff
Mojca Orel, Cirila Peklaj, Vesna Ferk Savec

Bibliographic record

VenueActa chimica slovenica · 2024
Typearticle
Languageen
FieldComputer Science
TopicDigital literacy in education
Canadian institutionsIron Ore Company (Canada)
Fundersnot available
KeywordsChemistry educationInformation and Communications TechnologyMathematics educationPsychologyChemistryTeacher educationTeaching methodPedagogyComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

This study examined how teachers teaching chemistry at different levels of education perceive their levels of technological pedagogical content knowledge (TPACK) and examined the relationship of TPACK with age, gender, teaching at different levels of education, time spent teaching chemistry, and frequency of information and communication technology (ICT) use. The study involved 261 teachers, 246 women and 15 men, from all over Slovenia, who have been teaching chemistry for an average of 18 years at different levels of education, with an average age of 45 years. The results showed that teachers teaching chemistry content perceive a high level of TPACK. There is a statistically significant correlation between age, time spent teaching chemistry, and frequency of ICT use with the perceived level of technological pedagogical content knowledge. Younger teachers, those with less professional experience and teachers who use ICT more frequently rated their TPACK higher. Based on the results of the survey, guidelines for planning the in-service teacher training that would support the development of TPACK of teachers teaching chemistry content were developed.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.253
GPT teacher head0.376
Teacher spread0.122 · 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 designOther design
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

Citations1
Published2024
Admission routes1
Has abstractyes

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