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Record W4392292348 · doi:10.5539/ijps.v16n1p61

5-Year-Old Children Performing Piaget’s Liquid Conservation Tasks Demonstrated in Physical and Digital Environment

2024· article· en· W4392292348 on OpenAlexvenueno aff
Christos Sakkas, Stavroula Samartzi

Bibliographic record

VenueInternational Journal of Psychological Studies · 2024
Typearticle
Languageen
FieldComputer Science
TopicEducational Research and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyDevelopmental psychologyCognitive psychologyCognitive science

Abstract

fetched live from OpenAlex

This study investigates the understanding of Piaget's concept of liquid conservation in 5-year-old children, comparing physical and digital environments of executing conservation tasks. Involving 86 participants (equal gender representation), it employs an Android tablet to demonstrate the pouring of water between glasses with animated images in a digital environment condition and real glasses (one short-wide and one long-narrow) filled with water for the physical environment condition. Each child completed four distinct conservation tasks, each one 3 times, designed to parallel each other in both environments. Two of the tasks concerned the general concept of conservation, and the other two were either about identity or compensation or reversibility concepts. The study aims to determine whether digital environments can be as effective as physical ones in teaching fundamental conservation concepts, exploring the impact of emerging digital learning tools versus traditional methods. Another objective of this study is to find associations between the general concept of conservation and the three other concepts: identity, compensation, and reversibility. This research contributes to the understanding of cognitive development in children and the efficacy of digital versus physical learning aids by verifying that children perceive physical and virtual learning with the same effectiveness.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.473
Threshold uncertainty score0.245

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 studies0.0000.000
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 head0.071
GPT teacher head0.412
Teacher spread0.341 · 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
Published2024
Admission routes1
Has abstractyes

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