Google Classroom: Understanding EFL Students’ Attitudes towards Its Use as an Online Learning Platform
Bibliographic record
Abstract
The closure of educational institutions across Thailand, as well as the maintenance of social distancing as a preventive and precautionary step against COVID-19, has thrown a wrench in the traditional method of teaching, which has given way to online learning. As a result, most online learning is managed through a learning management system, the most popular of which is Google Classroom. The purpose of this study was to find out how students felt about using levels as a virtual learning tool. One hundred and eleven second-year Thai EFL students from 7 majors who are taking English for Work participated in this study. They were mostly female (79.28%) and between 19 and 23 years old. They had attended online learning on Google Classroom. To obtain participants’ feedback, a Google Form questionnaire and a semi-structured interview were used. Means and Standard deviation were used as descriptive statistics. According to the results of the study, students indicated positive attitudes towards using Google Classroom in the aspect of ease of use (Mean = 4.41), usefulness (Mean = 4.12), and intention to use (Mean = 4.02). The results showed Google Classroom was well perceived by students. They perceived Google Classroom to be useful in submitting assignments and reminding class announcements. The results help teachers to consider arranging activities such as live online tutoring and discussion using Google Classroom to enhance students’ learning engagement or using blended learning (integrating online learning mode with face-to-face classroom).
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".