Switching from In-person to Online Learning: Emotion and Temporal Focus in the Egyptian Tertiary Student Experience
Bibliographic record
Abstract
Responding to the Covid-19 pandemic, the Egyptian government closed universities and ordering teachers online. Adjusting to online teaching was difficult for learners. This study of 26 students at a private Egyptian university used Voyant Tools and LIWC to explore their psychometric and cognitive responses to the first few weeks of online learning. Results indicate that stress and anxiety were common. Despite more time with family, social and cognitive process words were less frequent than the norm. Time words focused more on the present and the past than the future. Work words were more frequent than, but mentions of leisure less frequent than the norm. Since the pandemic, tertiary teaching and learning has made increasing use of online platforms. This is likely to continue in future. Negative emotions and the past temporal focus of these students highlights time as a problem for online learning, because virtual time has no boundaries, and may be experienced subjectively as ongoing in an unbounded or unlimited manner. In combination with social isolation, this will tend to enhance negative emotions. This highlights the need for online teaching to manage virtual boundaries, as clock time has in classroom teaching. Qualitative data analysis will remain a frontline tool in assessing learners’ experiences online, as universities go forward with this teaching delivery mode.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".