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Record W2771855104 · doi:10.5539/elt.v13n2p32

The Qualities of Effective Teachers as Perceived by Saudi EFL Students and Teachers

2020· article· en· W2771855104 on OpenAlexvenueno aff
Iman Alzobiani

Bibliographic record

VenueEnglish Language Teaching · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyLikert scaleDescriptive statisticsPerceptionMathematics educationSample (material)Scale (ratio)Test (biology)Developmental psychologyStatistics

Abstract

fetched live from OpenAlex

The aim of this study was to investigate the dominant types of qualities of effective teachers. The study further aimed at exploring the extent to which the students' and teachers' perceptions are different. The sample of the current study consisted of 150 students and 40 teachers in (16) female public intermediate schools in Al-Madinah Al-Munawarah to participate in the study. To achieve the purpose of the study, data were collected via a four point Likert-scale questionnaire designed by the researcher. The results of the descriptive statistics indicated that the mean and rank of the two types of the qualities of effective EFL teachers revealed that the dominant type among the participants was the instructional skills. Meanwhile, the personal traits of effective EFL teachers were ranked first by teachers and second by students. Additionally, the results of the Independent Sample T-Test showed no statically significant differences in the perceptions of Saudi EFL students and teachers with respect to the qualities of effective teachers.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.037
GPT teacher head0.384
Teacher spread0.347 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations16
Published2020
Admission routes1
Has abstractyes

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