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Record W2129482923 · doi:10.5539/ies.v5n4p56

Teachers’ Nonverbal Behavior and Its Impact on Student Achievement

2012· article· en· W2129482923 on OpenAlexvenueno aff
Noureen Asghar Chaudhry, Manzoor Arif

Bibliographic record

VenueInternational Education Studies · 2012
Typearticle
Languageen
FieldPsychology
TopicCommunication in Education and Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsNonverbal communicationPsychologyRating scaleAcademic achievementObservational studyScale (ratio)Mathematics educationPopulationInterpersonal communicationSocial psychologyDevelopmental psychologyStatisticsMathematics

Abstract

fetched live from OpenAlex

The observational study was conducted to see the impact of teachers’ nonverbal behavior on academic achievement of learners. This also investigated the relationship of nonverbal communication of teachers working in different educational institutions. Main objectives of study were to measure nonverbal behavior of teachers’ both male and female working in English medium Federal Government Cantt Garrison schools, Army Public schools and Private schools and to find out the relationship between teachers’ nonverbal behavior and academic achievement of students. 90 science teachers were randomly chosen through cluster sampling technique. An observation form with seven-point rating scale (semantic differential) based on Galloways’ categories of nonverbal communication was developed. The rating scale complemented verbal dimension of Flanders’ interaction categories through nonverbal dimension. Design of research was descriptive cum observational. The statistical techniques of frequency distribution, mean, standard deviation, and ANNOVA and t-test were used for analysis. The results were generalized to the population by means of appropriate inferential statistics. It was found that the nonverbal behavior of the teachers was found to be consistent with their verbal behavior.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.001

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 head0.158
GPT teacher head0.575
Teacher spread0.417 · 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 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

Citations19
Published2012
Admission routes1
Has abstractyes

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