Online Learning During the COVID-19 Pandemic: Benefits and Challenges for EFL Students
Bibliographic record
Abstract
Online learning has been a vital tool to be used during the COVID-19 pandemic, and many research studies have been conducted on this topic from different perspectives. However, it can be argued that it is important to identify and evaluate the students’ experience especially those of them who are experiencing online learning for the first time. Therefore, the purpose of this study was to evaluate Saudi EFL learners’ experience towards the rapid shift to an entirely online learning environment. Specifically, this study aimed to identify the benefits and the challenges of online learning during COVID-19 and compare the traditional way of face-to-face learning to online learning from the students’ perspective. The research method employed for this paper was a quantitative method in terms of a questionnaire. The questionnaire contained 15 items and was utilized to identify the benefits and challenges that the students have faced during their online learning experience. Participants were 72 Saudi EFL learners in their preparatory year at a Saudi higher education institution. Major findings revealed a number of benefits of online learning, such as: “Easy access to online material”, “Ability to record meetings and sessions”, and “Retrieve information”. On the other hand, technical problems were the most reported challenge for students, in addition to lack of interaction with teachers. Based on the research findings, several suggestions and recommendations were presented to enhance the effectiveness of online learning.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".