MétaCan
Menu
Back to cohort
Record W2467372883 · doi:10.5539/mas.v10n10p48

Flexibility of Iranian Teachers Teaching Methods and High School Students’ Gains

2016· article· en· W2467372883 on OpenAlexvenueno aff
Naeimeh Ahmadipour, Reza Norouzi Kouhdasht, Najmeh Bordbar

Bibliographic record

VenueModern Applied Science · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFlexibility (engineering)PaceStratified samplingMathematics educationPsychologyStatistical inferenceSimple random sampleSample (material)Cluster samplingReliability (semiconductor)Style (visual arts)PopulationStatisticsMathematicsMedicineGeography

Abstract

fetched live from OpenAlex

The teaching skills are the key element for all teachers. The main objective of this descriptive study was to investigate the relationship between Flexibility of Iranian Teachers Teaching Methods and High school Students’ emotional, social and intellectual gains as a model. The study population included all Shiraz high schools teachers and their students. By stratified random sampling, 100 teachers and for each of them, 4 students were selected. Data instruments were flexible personal style questionnaires (Taggart & Hausladen, 1993) and students gains subscale of college student experiences questionnaire (Kuh & pace, 2002). After measurement of validity and reliability of instruments, they were distributed among sample and data was collected. Data was analyzed by inferential statistical methods included Pearson correlation coefficient and multiple regression. The results showed that: 1. There is a relationship between teachers’ intuitive and logical teaching style and student gains. 2. The intuitive teaching style of teachers is stronger predictor of student gains.

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)0.011
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.763
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0000.000
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 head0.070
GPT teacher head0.489
Teacher spread0.419 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueModern Applied ScienceSame topicHigher Education Research StudiesFrench-language works237,207