‘We Were Scared of Catching the Virus’: Practices of Saudi College Students During the COVID-19 Crisis
Bibliographic record
Abstract
College students in Saudi Arabia engaged in various English activities in digital spaces during the COVID-19 crisis, despite it being their second language. Drawing on the concept of digital literacies proposed by Jones and Hafner (2012), this paper identifies the digital literacy practices that occurred in the English language during COVID-19 crisis. Focus group interviews and individual interviews were conducted via WhatsApp with ten college students who had recently attended an intensive English course at a university in Saudi Arabia. Thematic analysis, assisted by ATLAS.ti, revealed that the college students engaged in complex digital literacy practices in English during the COVID-19 crisis to improve their competency in English, educating the community and oneself about COVID-19 as well as to cope with the boredom of remaining indoors. College students mainly used smartphone apps in their literacies, and their usage was guided by their feelings, commitment and the contextual events around them. The literacies these students drew on reflected their metacognitive awareness of the value of English to their everyday life experience. This paper concludes by encouraging college students to exploit the potential of smartphone apps to improve their capacity in English and incorporate apps into their everyday lives.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".