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Record W2743494834 · doi:10.5539/hes.v7n3p103

Which Technologies Do Pre-Service Teachers Prefer to Use While Presenting Their Teaching Skills and for What Purposes Do They Use These Technologies?

2017· article· en· W2743494834 on OpenAlexvenueno aff
Fatma Şaşmaz Ören

Bibliographic record

VenueHigher Education Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsnot available
Fundersnot available
KeywordsMicroteachingPresentation (obstetrics)Computer scienceMathematics educationScience educationService (business)Medical educationTeacher educationSubject (documents)Teaching methodMultimediaPsychologyMedicineWorld Wide Web

Abstract

fetched live from OpenAlex

This research aims to determine the technologies that pre-service science teachers prefer to use in micro teaching presentations performed for improving their teaching skills and to determine the purposes of using these technologies. For this purpose, the case study model was used in the research. The research was made with some 48 pre-service science teachers. In the research, data was collected from the presentation files the pre-service science teachers had prepared with respect to the microteaching applications, from the instructor’s observation notes on their presentations, from the view form and from the semi-structured interviews. According to the findings obtained from the research, the pre-service science teachers used computers, projection apparatuses, overhead projectors, videos, animations, simulations and microscopes the most in the microteaching applications. The pre-service science teacher’s expressed that they used technology primarily for reasons such as enhancing the comprehensibility of the subject, concretizing abstract subjects, ensuring visuality and saving time. Considering these results, some recommendations were made regarding the use of technology in science courses.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0020.003
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 head0.114
GPT teacher head0.416
Teacher spread0.302 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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