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Record W2921230492 · doi:10.5539/gjhs.v11n4p19

Learning From Interactive Whiteboard Instruction Technology in Teaching Students With Autism Spectrum Disorders

2019· article· en· W2921230492 on OpenAlexvenueno aff
Fayez S. Maajeeny

Bibliographic record

VenueGlobal Journal of Health Science · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsnot available
Fundersnot available
KeywordsInteractive whiteboardWhiteboardNumeracyIntervention (counseling)PsychologyMathematics educationAutism spectrum disorderAutismComputer scienceMultimediaMultiple baseline designMedical educationPedagogyLiteracyDevelopmental psychologyMedicine

Abstract

fetched live from OpenAlex

Interactive whiteboard instruction technology provides interactive learning environment and serves as a motivational tool for the students. The study aims to investigate the effectiveness of interactive whiteboard (IAW) to teach early numeracy skills to the ASD students. The study has employed single-case design methodology and evaluated students for the effectiveness of using interactive whiteboard for teaching skills to the students through multiple probe design. A total of five baseline sessions were conducted on total four recruited students. During the intervention, data was obtained for at least three sessions from the date each student reached the acquisition criteria. The results showed that introduction of the intervention resulted in all participants meeting the established criteria. The early numeracy skills were generalized by all the four students to a different setting and with different materials. These results have supported the effectiveness of the interactive whiteboard, coupled with DTT to teach early numeracy skills to students with ASD. The study has concluded that interactive whiteboard with DTT was effective to teach early numeracy skills to the ASD students.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.008
GPT teacher head0.359
Teacher spread0.350 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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