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Record W3214070249 · doi:10.5539/jel.v11n1p40

Effectiveness of Video Modeling in Teaching Computer Skills to Students with Intellectual Disabilities

2021· article· en· W3214070249 on OpenAlexvenueno aff
Naime Güneş Özler, Gönül Akçamete

Bibliographic record

VenueJournal of Education and Learning · 2021
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsnot available
FundersTürkiye Bilimsel ve Teknolojik Araştırma Kurumu
KeywordsVideo modelingPsychologyIntellectual disabilityMathematics educationMultimediaTeaching methodIntellectual abilityComputer scienceModellingCognition

Abstract

fetched live from OpenAlex

The purpose of this study is to determine whether video modeling is effective in teaching computer skills to students with intellectual disabilities. The study was designed with the multiple probe design across subjects, one of the single-subject research designs. The study was conducted with three female students with intellectual disabilities, who were 17–19 years old. Graphical analysis was used to analyze the data. The results show that video modeling was effective for them to acquire and retain skills for preparing a résumé, printing it out, and emailing it. However, the students had difficulties generalizing some of the skills on different computers and printers. It can be said that the reason for this originates from different designs of technological tools. In line with this, it is thought that removing the accessibility barrier in technological equipment will increase availability. The video modeling motivated students to learn computer skills. The participants reported that they could use these skills to do homework, apply for a job, and communicate with friends.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.392
Threshold uncertainty score0.188

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 integrity0.0000.000
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 head0.011
GPT teacher head0.311
Teacher spread0.300 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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