Experiences of Parents of Pre-Adolescents Coping with Online Learning, Socialization and Navigating Critical Media Literacy
Bibliographic record
Abstract
COVID-19 school closures necessitated shifts in how students engaged in learning and connected socially. For pre-adolescents and their families, these closures added urgency to an already identified challenge for parents trying to navigate their children’s engagement with digital platforms. Researchers utilized interviews to explore parent and child experiences related to online learning and behavior. Online workshops provided parents with critical media literacy (CML) knowledge and skills for navigating media texts and platforms. This paper employed emergent design-based research methodology and interpretive qualitative case-study methods. Data from interviews, recordings, and field notes were analyzed thematically. The analyses identified that parents noted increased time spent online by their children due to COVID-19 lockdowns. They also highlighted concerns for child safety, issues connected to CML, acknowledgment of the benefit of a support community amongst parents, and parent-child conversations about online actions. This study affirmed the need for parent support regarding CML and digital tools in educational and social online environments and provided suggestions for ways to promote this type of support.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".