“I Don’t Know if I Can Handle It All”: Students’ Affect During Remote Education in the COVID-19 Pandemic in Brazil
Bibliographic record
Abstract
The COVID-19 pandemic has impacted society in different areas. In education, several reports show the deleterious effects of the disease on the physical and mental health of students, family members, and teachers around the world. Also, in Brazil, affect studies indicate the prevalence of anxiety, stress, and depression among students. The present research, of a qualitative nature, explores what it means, under the lens of affect and from the student’s perspective, to experience remote education during the COVID-19 pandemic. An online questionnaire of 41 closed- and open-ended questions was given to 363 students from a public school in southeastern Brazil. This article analyzes the affective fields that emerged from the discursive textual analysis of the students’ responses (n = 100). Four affective fields were categorized: friends, classes, home, and teachers; intersecting emotions, attitudes, values, beliefs, and motivation. In general, students expressed more negative than positive affect but a positive disposition toward face-to-face classes. Boys focused their affect more on classes, while girls on teachers. The affective fields allow us to consider the friends–home–teachers tripod as fundamental to overcoming the phenomenon of affective fatigue that has been identified.
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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 | 0.003 | 0.007 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".