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iPad and iPod in the Language Classrooms

2018· book-chapter· en· W2799510696 on OpenAlexaffabout
Martine Pellerin

Bibliographic record

VenueAdvances in educational technologies and instructional design book series · 2018
Typebook-chapter
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAffordanceAgency (philosophy)Language acquisitionLearner autonomyComputer scienceAction researchPedagogyMathematics educationMobile technologyMobile devicePsychologyMultimediaLanguage educationComprehension approachWorld Wide WebSociologyHuman–computer interaction

Abstract

fetched live from OpenAlex

The chapter examines how the use of emergent mobile technologies such as iPad and iPod in the classroom with young language learners can promote innovative learning environments and authentic oral language learning experiences. The chapter is based on a collaborative action research (CAR) project involving young French language learners in primary schools in a western province of Canada. Findings show that the affordances of mobile technologies support the creation of innovative learning environments and authentic oral language learning experiences through collaborative dialogue and peer-peer scaffolding among young language learners. The outcomes of the inquiry also demonstrate that the use of mobile devices such as iPad and iPod promotes the emergence of metacognitive reflection among learners, as well as a greater sense of agency and autonomy.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.003

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 head0.011
GPT teacher head0.253
Teacher spread0.242 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations2
Published2018
Admission routes2
Has abstractyes

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Same venueAdvances in educational technologies and instructional design book seriesSame topicMobile Learning in EducationFrench-language works237,207