Digital Bonds: Exploring the Impact of Computer-Mediated Communication on Parent–Educator Relationships in Early Childhood Education and Care
Bibliographic record
Abstract
Despite advancements in the use of mobile technology in recent years, investigation of the technology designed for communication in parent–educator relationships in early education and its impact remains limited. This study investigated how computer-mediated communication could support parent–educator communication in the early childhood education and care (ECEC) sector. The participants selected were parent users (n = 140) at sites in Ontario, Canada, who had implemented a specified communication application; these participants were recruited by email, as identified within the organization’s database. Using a retrospective mixed-method design involving open- and close-ended and blended questions, an online survey consisting of 47 researcher-created questions was used to assess participants’ perspectives of changes in parent–educator communication. The quantitative and qualitative survey data were analyzed using paired sample sign tests and thematic analysis. Computer-mediated communication was found to have the potential to strengthen parent–educator communication practices, particularly when paired with face-to-face communication. The participants reported increased communication content regarding their children’s daily experiences, which positively influenced both parent–educator and parent–child relationships. To facilitate technology-mediated communication in childcare settings in the future, ongoing training and clear expectations for its use are recommended to support the effective application of technology within parent–educator communication practice.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".