Friend or Foe? A Mixed-Methods Study on the Impact of Digital Device Use on Chinese–Canadian Children’s Heritage Language Learning
Bibliographic record
Abstract
Abstract Digital devices have been increasingly integrated into language learning environments, particularly since the COVID-19 pandemic. Existing literature, focusing predominantly on dominant languages like English, presents mixed findings on the effectiveness of digital resources for language learning. Few studies address heritage languages, which often have limited resources beyond the home and may depend more on digital tools for support. This longitudinal, mixed-methods study investigated the impact of digital device use on heritage language learning among Chinese–Canadian families. We examined the relationship between digital device use and Chinese receptive vocabulary among 128 first graders, 137 second graders, and 66 third graders over three years. Additionally, we conducted parental interviews with 42 focal families for three years to explore the evolving patterns of digital resource use at home. Our findings revealed a statistically significant positive impact of digital device use on Chinese receptive vocabulary development among first and second graders, while no significant effects were observed in third graders. The analyses of parental interviews uncovered increased digital use, diversity of resources, positive parental attitudes, and digital literacy among families from grades 1 to 2 but decreased digital use and parental enthusiasm in the third grade due to health and addiction concerns, reinforcing the quantitative results. Conducted during the COVID-19 pandemic, this study offers a unique perspective on how families’ digital device use for heritage languages changed before, during, and after the pandemic. The findings offer valuable insights for families and educators to better support heritage language learners with digital resources.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".