The Educational Digital Divide for Vulnerable Students in the Pandemic: Towards the New Agenda 2030
Bibliographic record
Abstract
The COVID-19 pandemic has caused school closures worldwide and has disrupted nearly 1.6 billion students across the globe. This has widened existing digital gaps and has caused vulnerable students to be further digitally displaced. In efforts to mitigate this issue, various strategies have been used to cater for the educational digital divide of vulnerable students. However, there is a lack of studies investigating the relationship between access and connectivity of learning and use and exploitation of technology, particularly with regards to iPads during the pandemic. Thus, the present study investigates this scenario by examining the digital educational divide for vulnerable students in the pandemic, in terms of access and connectivity and use and exploitation. A survey was distributed to 518 vulnerable students in schools between the ages of 10 and 15 years old, and results were analyzed using partial least squares–structural equation modeling (PLS-SEM). The findings indicate that asynchronous learning is a stronger construct than synchronous learning, while creativity skills was stronger than productivity skills with regard to the use and exploitation of technology for pandemic learning of vulnerable students. This study’s findings could assist future developers and educators in the development of effective emergency teaching and learning strategies and design.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".