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Record W2911826425 · doi:10.5539/elt.v12n3p57

A Qualitative Study on the Best Motivational Teaching Strategies in the Context of Oman: Perspectives of EFL Teachers

2019· article· en· W2911826425 on OpenAlexvenueno aff
Mohamad Yahya Abdullah, Hawa Mubarak Harib Al Ghafri, Khadija Saleem Hamdan Al Yahyai

Bibliographic record

VenueEnglish Language Teaching · 2019
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyEnthusiasmThematic analysisMathematics educationQualitative researchContext (archaeology)FeelingSet (abstract data type)Data collectionPedagogyTeaching methodSocial psychology

Abstract

fetched live from OpenAlex

In the pedagogical process, lack of motivation becomes a controversial issue. In this qualitative study, the researcher intended to explore the best motivational teaching strategies from the perspectives of the EFL teachers at Buraimi University College (henceforth BUC) in Oman. Purposeful sampling was used in the selection of five EFL teachers in English language department at BUC. The protocol of interview (semi-structured interviews) was conducted for data collection. The obtained data provided by the participants was analysed using qualitative thematic analysis to answer the questions and accomplish the objective of the present study. The findings revealed that motivational strategies would not be applicable in any classroom without creating a helpful, interactive, engaging, and enjoyable environment. The participants of the current study believe in the vital role of playing games in stimulating the interest and enthusiasm of learners as well as giving fun and energetic environment. Besides, it is essential to inspire students with the feelings of a sense of accomplishment through helping them to set realistic goals according to their learning abilities and observe their progress once in a while. However, in any learning environment developing motivation is a difficult task for the teachers because students learn differently and every student is diverse in her way.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 integrity0.0000.001
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 head0.033
GPT teacher head0.391
Teacher spread0.358 · 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 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

Citations15
Published2019
Admission routes1
Has abstractyes

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