An english language learning study with rural chinese children using an augmented reality app
Bibliographic record
Abstract
Augmented reality (AR) apps have the potential to support early English learning for children. However, few studies have investigated how children from rural low socio-economic status (SES) schools, who learn English as a foreign language (EFL) used and perceived an AR app in language learning. In this paper, we present an exploratory case study of 11 EFL children and four school teachers from a Chinese rural county who used an AR app (called AR PhonoBlocks), for one week. The goal of the app is to support children to learn the alphabetic principle of English. The key features are overlaid dynamic colour cues on 3D physical letters. We present the results including themes related to children's interactional behaviours and motivations, and rural teachers' feedback on the opportunities and concerns around using an AR app in a rural school context. We suggest design implications and future research directions for designing AR apps to support EFL children from low SES schools in early English learning.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".