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Record W2980125036 · doi:10.3390/socsci8100285

Exploring Gender Differences in Teacher–Student Interactions during an Adapted Robotics Program for Children with Disabilities

2019· article· en· W2980125036 on OpenAlexaffabout
Kendall Kolne, Sally Lindsay

Bibliographic record

VenueSocial Sciences · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoHolland Bloorview Kids Rehabilitation Hospital
Fundersnot available
KeywordsRoboticsPsychologyCompetence (human resources)General partnershipDevelopmental psychologyArtificial intelligenceComputer scienceRobotSocial psychology

Abstract

fetched live from OpenAlex

We compared the interactions between teachers and children with disabilities enrolled in an adapted robotics program to examine potential gender differences in these interactions. We coded video recordings from instructional sections of the HB FIRST® robotics program, an adapted robotics program developed through a partnership between a pediatric rehabilitation hospital and FIRST® Canada (For Inspiration and Recognition of Science and Technology). The program consists of 6-weekly, two-hour workshops. We coded videos from four separate single-gender workshops (two all-girls, and two all-boys) involving 22 children aged 6 to 8 years-old. Results revealed that boys and girls display different styles of engagement during the robotics, and teachers’ behaviour is functionally responsive to these differences. The differences in the interaction styles of boys and girls identified in this study may contribute to gender differences in students’ perceived STEM competence, and ultimately interest in pursuing STEM education and careers.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.229
GPT teacher head0.361
Teacher spread0.132 · 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 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

Citations3
Published2019
Admission routes2
Has abstractyes

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