Google Classroom in TEFL for Basic School Students amid the COVID 19 Pandemic: Teachers’ Reflections
Bibliographic record
Abstract
This paper exposed TEFL basic schoolteachers’ reflections on the use of Google Classroom amid the COVID 19 pandemic. It is a qualitative descriptive field research that tackled 82 TEFL teachers who responded to a reflection instrument which includes four questions tackled: (1) demographic general information, (2) uses of google classroom, (3) challenges teachers faced and ways they use to address these challenges, and (4) suggestions for best uses. The participants’ responses were qualitatively collected and analyzed. The findings showed that most TEFL teachers used Google Classroom for three purposes: evaluating students’ work using various assignments and tests, assigning useful homework and determining the participants in the course. The most common challenges that faced TEFL teachers were the weak e-learning skills they possess and the lack of suitable infrastructure, the huge number of students, thick textbooks and the negative psychological impact of the COVID 19. Several suggestions for the best uses related to lesson presentation, practice stage, follow-up and giving feedback on students’ work, and synchronous meetings were presented by TEFL teachers. The researcher recommended the officials in the Ministry of Education conduct specialized training courses for TEFL teachers to help them use Google Classroom for developing EFL students’ skills. Besides, it is essential to present general standards that guide EFL teachers to construct effective EFL courses.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".