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Record W2085673615 · doi:10.1080/19404151003796516

Investigating the effects of a game-based approach in teaching word recognition and spelling to students with reading disabilities and attention deficits

2010· article· en· W2085673615 on OpenAlexaff
Anna Garito Cassar, Eunice Eunhee Jang

Bibliographic record

VenueAustralian Journal of Learning Difficulties · 2010
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSpellingPsychologyReading (process)Word recognitionPhonological awarenessLiteracyPhonicsLearning disabilityWord (group theory)Exploratory researchCognitive psychologyMathematics educationPrimary educationLinguisticsDevelopmental psychologyPedagogy

Abstract

fetched live from OpenAlex

The purpose of this small-scale exploratory study was to examine the effects of using a game-based instructional approach to teach word recognition and spelling to Grade 6 students with reading disabilities (RD) and attention deficit disorders. Treatment and comparison groups were formed. The students were placed in either a traditional spelling group or an alternate game group. Different measures of phonological awareness, phonological memory and rapid naming as well as the word recognition and spelling subtests were administered to six students with RD and attention deficits. The study results indicate that students in the game-based approach outperformed students in the traditional, text-based spelling programs. In addition, the remediation of specific deficit subskills was possible through a game-based approach. Rapid naming, word recognition and spelling subtest results all improved through this method, with rapid naming improving the most. The study provides evidence for marked improvement in engaging behavior as well as literacy skills through alternate instructional methodologies.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.026
GPT teacher head0.297
Teacher spread0.271 · 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

Citations38
Published2010
Admission routes1
Has abstractyes

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