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Record W2989697829 · doi:10.14705/rpnet.2019.38.988

Shouting in space: promoting oral reading fluency with Spaceteam ESL

2019· book-chapter· en· W2989697829 on OpenAlexafffund
Walcir Cardoso, David I. Waddington, Enos Kiforo, Anne-Marie Sénécal

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsAga Khan FoundationConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFluencyReading (process)AffordanceAnecdoteSpace (punctuation)PsychologyReading aloudMathematics educationComputer scienceLinguisticsCognitive psychology

Abstract

fetched live from OpenAlex

This study examined whether the pedagogical use of Spaceteam ESL (English as a Second Language), a digital shouting game, could contribute to the development of Oral Reading Fluency (ORF) among 71 English students in secondary schools in Mombasa, Kenya. Following a mixed-methods approach for data collection and analysis, we pre- and post-tested the participants on their ability to read aloud efficiently (speed) and accurately (accuracy) in three tasks: (1) phrases extracted from the game; (2) phrases not related to the game; and (3) an anecdote. Our findings indicate that participants who played Spaceteam ESL improved their ORF on all measures of speed, but no significant differences were observed in terms of accuracy. Overall, these findings corroborate our hypothesis that some of the affordances of Spaceteam ESL (e.g. speed reading) would contribute to the development of some aspects of ORF.

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: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.017
GPT teacher head0.287
Teacher spread0.270 · 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

Citations1
Published2019
Admission routes2
Has abstractyes

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