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Record W4293069411 · doi:10.18844/cjes.v17i5.7294

Using songs to teach students with intellectual disabilities to tell time

2022· article· en· W4293069411 on OpenAlexaboutno aff
Naime Güneş Özler

Bibliographic record

VenueCypriot Journal of Educational Sciences · 2022
Typearticle
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsnot available
Fundersnot available
KeywordsSingingSubject (documents)Mathematics educationPsychologyIntellectual disabilityQuarter (Canadian coin)PedagogyComputer scienceHistory

Abstract

fetched live from OpenAlex

The purpose of this study is to determine the effectiveness of using songs to teach students with intellectual disabilities to tell time. It followed a multiple-probe, across-subject design with a probe phase, which is a single-subject research design. The participants were three 17–19-year-old female students with mild intellectual disabilities who could not read clocks. They were taught to read analog clocks at full, half, and quarter past hours. Graphical analysis was used to analyze the data on the effectiveness of teaching through songs. At the end of the study, it was evaluated whether the students could read clocks without singing. Social validity data were collected from the students and their teachers. The findings show that all three students could learn to read clocks through songs and could tell the time correctly without songs. In the social validity data, the students stated that they were able to learn to tell time easily without getting bored and they wanted other lessons to also be taught through songs. The teachers stated that the students participated in the lesson more willingly than usual and that they enjoyed learning to tell time. Keywords: Intellectual disabilities, mathematical skills, telling time, teaching through song

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.076
GPT teacher head0.397
Teacher spread0.321 · 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.

Study designTheoretical or conceptual
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

Citations3
Published2022
Admission routes1
Has abstractyes

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