Global student perspectives on digital inclusion in education during COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic globally disrupted education, forcing a shift to remote learning that excludes many learners. This paper examines student perspectives of the changes to their education. In October 2020, students worldwide participated in the Digital Inclusion Challenge, a hackathon-style event hosted by Convergence.Tech, a digital transformation company. Participants described barriers to learning and proposed solutions to increase inclusivity and effectiveness. Using thematic analysis, student-identified barriers and their proposed solutions were coded and explored. Overall, themes of four barriers to digital inclusion in education and themes of six solutions were identified. The findings demonstrate what students value in their education, and what they felt they had lost in the transition to online and remote learning. This research contributes to knowledge on the severe impacts of the loss of in-person learning and explores technological and conceptual innovations ideated by youth. Further, it provides insight into global student experiences in accessing education during the pandemic and offers considerations for future research.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".