Digital well-being begins with inclusion: A systematic review of videoconferencing guidelines for equitable learning
Bibliographic record
Abstract
As higher education institutions increasingly adopt videoconferencing technologies to broaden access to learning, the need for evidence-based, inclusive practices to support digital well-being becomes paramount. Integrating these technologies into the curriculum necessitates careful design considerations to prevent unintended consequences and uphold learners' privacy, safety, equity and humanity. Our systematic review, based on eight dimensions of digital wellness, has identified key inclusive design decisions for videoconference-enabled formal learning experiences. Drawing from data analysed from 36 empirical studies, we organised six inclusive design considerations for digital wellness in videoconferencing learning environments. These considerations – accessibility, active learning strategies, multimodal communication, readiness, social presence and sociocultural sensitivity – offer course designers a practical framework to create evidence-based practices that foster digital wellness and inclusion in videoconferencing learning spaces. Implications for practice or policy: Academic institutions should recognise digital wellness as a shared responsibility among institutional stakeholders, including faculty, learners, and administrative professionals. Institutional policies should prioritise learner choice and equitable access for co-creating knowledge and fostering safe communication. Stakeholders should be empowered to make informed choices about digital habits to mitigate unintended consequences and encourage mindful technology use. Accessibility barriers must be addressed through intentional learning design, ensuring meaningful participation and interaction for all.
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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 | 0.026 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".