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Record W4409526716 · doi:10.5430/wjel.v15n5p242

Teachers' Reflections on Utilizing Slido to Enhance Learner Engagement

2025· article· en· W4409526716 on OpenAlexvenueno aff
Ruel Ancheta, Deny Daniel, Cherubim Gilbang, Afrah Al Shammakhi, Samya Al- Shidi, Meenakshi Vytiyanathan

Bibliographic record

VenueWorld Journal of English Language · 2025
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceMathematics educationPsychology

Abstract

fetched live from OpenAlex

This paper was conducted to determine the reflections of teachers of English as a Foreign Language (EFL) in the application of Slido to enhance learners’ engagement at EFL classrooms. It utilized a qualitative phenomenological research design in exploring and describing the practical experiences of the participants in using Slido through critical evaluative reflection and personal views. Participants in this study were EFL teachers teaching in one of the higher education institutions in Oman with more than 15 years of teaching experience. Guided by the CARL model of reflection, the participants’ views and observations focused on the four areas of reflection such as context, action, result, and learning. Participants’ reflections were gathered through focus group discussion (FGD) using open-ended interview guide questions. The results showed that Slido technology helped learners to actively participate in the teaching and learning process. The interactive features of Slido, such as quizzes, and open-text exercises, fostered speedy response and improved student engagement. The result of the evaluative reflection showed that Slido, as an IT solution tool, is a valuable instrument for increasing student engagement and active participation in the classroom especially in English language classes. It contains user-friendly features essential for the learning process, with a unique platform that can make learning enjoyable and enhances the quality of teaching and teacher-learner interaction.

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)0.004
metaresearch head score (Gemma)0.004
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.779
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.048
GPT teacher head0.451
Teacher spread0.403 · 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 designNot applicable
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

Citations0
Published2025
Admission routes1
Has abstractyes

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