MétaCan
Menu
Back to cohort
Record W2912519095 · doi:10.1080/17482798.2019.1575887

Children’s attention to screen-based pedagogical supports: an eye-tracking study with low-income preschool children in the United States

2019· article· en· W2912519095 on OpenAlexaff
Rachel M. Flynn, Kevin M. Wong, Susan B. Neuman, Tanya Kaefer

Bibliographic record

VenueJournal of Children and Media · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsLakehead University
FundersInstitute of Education Sciences
KeywordsScreen timePsychologyTracking (education)Developmental psychologyEye trackingSalientPovertyVocabularyVisual mediaVisual attentionCLIPSMultimediaPedagogyMedicineComputer sciencePhysical activityCognition

Abstract

fetched live from OpenAlex

Educational screen media is increasingly salient in the lives of young children. Research affirms preschool-aged children can learn content from media when they attend to it, however less is known about how specific screen-based pedagogical supports (SBPS) might draw children’s attention. Using eye-tracking methodology, the current study examines specific SBPSs that engage children’s attention. The sample consisted of 106 3- to 5-year-olds from a poverty-impacted neighborhood. Participants viewed 12 video clips of Sesame Street that used four different SBPSs to support vocabulary: visual effects, visual + sound effects, explicit definitions, and explicit definitions + repetitions. Results indicated that children attended significantly more to the SBPSs with definitions. Findings also revealed differences in screen composition. Children attended more to people than objects, and attended more to on-screen conversations than conversations cut between screens. This study demonstrates the importance for educational media to use appropriate SBPSs and on-screen compositions to engage children.

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)0.000
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 score0.003
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.313
Teacher spread0.297 · 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

Citations17
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Children and MediaSame topicChild Development and Digital TechnologyFrench-language works237,207