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Record W3008894009 · doi:10.5539/ies.v13n3p100

The Effect of Teachers’ Level of Visual Use on Students’ Visual Reading Skills: Comparison Between the Students of Expert and Novice Teachers

2020· article· en· W3008894009 on OpenAlexvenueno aff
Hasan Aslan, İbrahim Turan

Bibliographic record

VenueInternational Education Studies · 2020
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)PsychologyMathematics educationQualitative researchVisual impairmentPedagogy

Abstract

fetched live from OpenAlex

The aim of this study is to examine the effect of history teachers’ visual usage level on high school students’ visual reading skills. This qualitative research, consisting of two stages, was carried out using a holistic multi-case study design. In the first stage, 19 history teachers were observed in the classroom and their visual usage status in history lessons was determined (Aslan, 2015). Among these 19 teachers, a total of 4 teachers, 2 experts, and 2 novices, were determined according to their visual usage levels, and the second stage of the research was conducted with their students. The study group consisted of 92 high school students who agreed to participate in the study on a voluntary basis among the students of the teachers identified in 4 high schools. The data required for the research was gathered via “Visual Reading Level Form” which developed by the researchers. Visual Reading Level Form aims to determine the visual reading skill levels of the participants in related sub-skills such as; estimating time and space, identifying persons, events and symbols, recognizing propaganda, understanding the message, and the historical importance of visual. According to research findings, in almost all sub-skills determined in this study, the students of the expert teachers were more successful than the students of the novice teachers. This shows that teachers’ higher levels of visual use precipitate an increase in their students’ visual reading skills.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.228
GPT teacher head0.561
Teacher spread0.334 · 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 designObservational
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

Citations2
Published2020
Admission routes1
Has abstractyes

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