Teachers' Reflections on Utilizing Slido to Enhance Learner Engagement
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".