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Record W2749508581 · doi:10.5539/ies.v10n9p152

Are We Better without Technology?

2017· article· en· W2749508581 on OpenAlexvenueno aff
Ahmet Kara

Bibliographic record

VenueInternational Education Studies · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicEducation Practices and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyTechnology integrationTeaching methodPerceptionMathematics educationScale (ratio)Process (computing)CalmnessPedagogyComputer science

Abstract

fetched live from OpenAlex

The purpose of this study was to determine the effect of visual element and technology supported teaching upon perceived instructor behaviors by pre-service teachers. In accordance with this purpose, whereas the lessons were lectured without benefiting from visual elements and technology in a traditional way with the students included in the control group, in the experimental group, the lessons were lectured using visual elements and technology (PowerPoint, video, etc.) with the pre-service teachers included. In this research that was carried out using an experimental method, “Perceived Instructor Behaviors Scale” developed by Kara, İzci, Köksalan and Zelyurt (2015) was used as pre-application and post-application. According to the findings, visual elements and technology-assisted teaching caused pre-service teachers to perceive their instructors as calmer, more adequate and authoritative. When the probable negative effects of an authoritative instructor upon the students were considered, should sufficient and calm perception of the instructor be supported or should calmness and sufficiency of the instructor be preferred by avoiding technology-assisted teaching which makes the teaching process mechanic?

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.597
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.253
GPT teacher head0.449
Teacher spread0.196 · 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 designNot applicable
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
Published2017
Admission routes1
Has abstractyes

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