EFL Students Perspective towards Online Learning Barriers and Alternatives Using Moodle/Google Classroom during COVID-19 Pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Covid-19 pandemic has made many countries adapt on new situations in different sectors including education. The Indonesia government has decided to adjust the education mode from face-to-face to online meeting using various learning management systems (LMS) such as moodle and google classroom. Moreover, the present research depicted the online learning barriers faced by students as well as their alternatives to cope them. The research implemented descriptive mixed-method survey design. The participants were 25 students of English Education Department. The instruments used to gather the data were the questionnaires and interview regarding the topics. The results showed that students experienced three barriers during the online learning including infamiliriaty of e-learning, slow internet connection, and physical condition e.g. eye strain. The alternatives they proposed were providing training to implement the LMS before the real class, converting high-definition or big-size files into smaller one, and giving break during the online class. The conclusion stated that students had to be creatives to find any solutions and innovations regarding learning barriers including maintaining good communication with teacher and understanding best learning styles individually
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it