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Record W3000223255 · doi:10.1093/deafed/enz029

A Technology-based Intervention to Increase Reading Comprehension of Morphosyntax Structures

2019· article· en· W3000223255 on OpenAlexafffund
Joanna Cannon, Anita M. Hubley, Julia I. O’Loughlin, Lauren Phelan, Nancy Norman, Alayna Finley

Bibliographic record

VenueThe Journal of Deaf Studies and Deaf Education · 2019
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsVancouver Community CollegeKwantlen Polytechnic UniversityUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIntervention (counseling)PsychologySyntaxReading comprehensionComprehensionReading (process)NounDevelopmental psychologyLinguistics

Abstract

fetched live from OpenAlex

The purpose of this study was to examine the effectiveness of a technology-based intervention (LanguageLinks: Syntax Assessment and Intervention®; Laureate Learning Systems, Inc., 2013) to improve reading comprehension for d/Deaf and hard of hearing (DHH) elementary students. The intervention was a self-paced, interactive program designed to scaffold learning of morphosyntax structures. Participants included 37 DHH students with moderate to profound hearing levels, 7-12 years of age, in Grades 2-6. Assessment data were collected pre- and post- an 8-week intervention using a randomized control trial methodology. Findings indicate the intervention did not appear to be effective in improving performance, and 17 out of 36 morphosyntax structures were found difficult to comprehend for participants in the treatment group. These difficult structures included aspects of pronominalization, the verbal system, and number in nouns. Results are compared to previous research, with recommendations for future areas of research related to increasing knowledge of morphosyntax for learners who are DHH.

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 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)0.001
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 score0.573
Threshold uncertainty score0.204

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.027
GPT teacher head0.369
Teacher spread0.342 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations6
Published2019
Admission routes2
Has abstractyes

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