Camera Use in the Online Classroom: Students’ and Educators’ Perspectives
Bibliographic record
Abstract
The global pandemic created by COVID-19 altered the landscape of education, creating the need for flexible methods of teaching and learning and a reliance on technology that many educators and students were not prepared for. Educators adapted their instructional methods to include shifts in pedagogy and the use of remote, hybrid, and flipped classrooms. Despite the additional preparation time, educators found themselves grappling with questions about creating inclusive communities for learners, decisions about how to meaningfully incorporate technology, and how to support student engagement. Without the presence of clear research and guidance, decisions such as whether students should be mandated to enable their cameras during class manifested. Educators were challenged to balance their obligations to assess learning with concerns about increasing equity gaps, access issues, and systemic challenges that are disproportionately experienced by marginalized learners. In an educational environment where video conferencing has become the norm, understanding how requiring camera use is experienced by students and educators and its role in supporting the classroom community is paramount. This study focused on students’ and educators’ perspectives of camera use in the classroom. Findings revealed that educators and students made sense of the utility of cameras, mandating camera use and their role in developing classroom communities differently. Students generally expressed their capacity to decide for themselves when camera use supported versus hindered their participation and appreciated practicing their agency. Educators generally understood camera use as central and necessary to building classroom community and assessing student involvement, participation, and understanding of class content.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".