EFL Students Perspective towards Online Learning Barriers and Alternatives Using Moodle/Google Classroom during COVID-19 Pandemic
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
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".