Associations among parental attitudes on technology, digital literacy, and children’s learning outcomes in the 2020 pandemic
Bibliographic record
Abstract
The purpose of this study was to explore how parents’ digital literacy affected children’s learning outcomes while engaging in online learning in the recent pandemic. Digital literacy is the ability to efficiently use technology, to navigate through different programs comfortably, and to manage information for proper online behaviour. This study focused on the parental perspective of their child’s learning from face-to-face to digital instruction. An online survey was completed by 22 parents with children in grades K-12 attending school in the Edmonton area. The survey explored parents’ digital technology use, their motivation and perspectives on technology in their child’s learning, as well as demographic factors such as ethnicity, digital age, and household income. Students’ school performance was recorded for a number of subject areas. The survey revealed that parents had good digital literacy, access to technology, and positive attitudes on technology for learning. However, some parents reported a lack of their child’s engagement, a desire for greater teacher interaction, and difficulties with balancing parenting and teaching at home. Despite these concerns, detriment to children’s school performance was not observed. The findings shed light on the challenges faced by parents and children’s learning at home in the recent pandemic and have the potential to inform teaching practices that will optimize learning outcomes in online learning settings.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".