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Record W3038052960 · doi:10.1145/3392063.3394409

An english language learning study with rural chinese children using an augmented reality app

2020· article· en· W3038052960 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAugmented realityContext (archaeology)Exploratory researchEnglish as a foreign languagePsychologyEnglish languageLanguage acquisitionComputer scienceMathematics educationArtificial intelligenceSociologyGeography

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.625

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.293
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations28
Published2020
Admission routes1
Has abstractyes

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