In-Person Versus Online Learning in Relation to Students’ Perceptions of Mattering During COVID-19: A Brief Report
Bibliographic record
Abstract
We examined students’ perceptions of mattering during the pandemic in relation to in-person versus online learning in a sample of 6578 Canadian students in Grades 4–12. We found that elementary school students who attended school in-person reported mattering the most, followed by secondary school students who learned part-time in-person and the rest of the time online (blended learning group). The students who felt that they mattered the least were those who learned online full-time during the pandemic (elementary and secondary students). These results were not driven by a selection effect for school choice during the pandemic—our experimental design showed that students’ perceptions of mattering did not differ by current learning modality when they were asked to reflect on their experiences before the pandemic even though some were also learning online full-time at the time they responded to our questions. No gender differences were found. As a validity check, we examined if mattering was correlated with school climate, as it has in past research. Results were similar in that a modest association between mattering and positive school climate was found in both experimental conditions. The results of this brief study show that in-person learning seems to help convey to students that they matter. This is important to know because students who feel like they matter are more protected, resilient, and engaged. Accordingly, mattering is a key educational indicator that ought to be considered when contemplating the merits of remote learning.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".