Evaluation of Students’ Remote Learning Experience of Learning Arabic as a Second Language During the Covid-19 Pandemic
Bibliographic record
Abstract
The emergence of the Covid-19 global pandemic was followed by restrictions on social contact and interaction. Online remote learning was implemented all levels of school and universities in Saudi Arabia. In tertiary education, where Arabic is taught as a second language (ASL) with extensive interaction between the teacher and students, remote learning has specific advantages and disadvantages. This study aimed to investigate the success of remote learning, its influence upon learners’ attitudes, and its potential difficulties in implementation. Quantitative research was conducted on a sample of 236 students who had combined experiences of offline and online study during 2020 in their second semester at the Arabic Language Institute for Non-Native Speakers’. An online survey was administered consisting of three domains: students’ satisfaction with remote learning, obstacles faced, and students’ perceptions towards evaluation; students’ responses were measured using a Likert scale. The results demonstrated that there was hesitation about student satisfaction, as the result was moderate in general. Moreover, the study revealed difficulties included internet connection issues, the access and availability of remote learning, insufficiency in personal expression, and difficulties with technological devices. The results also showed that the students were dissatisfied regarding to the evaluation and assessment methods used in remote 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".