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Record W3170558018 · doi:10.1007/s10758-021-09520-7

Malleability of Spatial Reasoning With Short-Term and Long-Term Robotics Interventions

2021· article· en· W3170558018 on OpenAlexafffund
Krista Francis, Stefan Rothschuh, Dana Poscente, Brent Davis

Bibliographic record

VenueTechnology Knowledge and Learning · 2021
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsUniversity of Calgary
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSpatial intelligenceTerm (time)RoboticsSpatial abilityIntervention (counseling)Artificial intelligenceCurriculumMalleabilityComputer sciencePsychologyRobotMathematics educationPedagogyCognition

Abstract

fetched live from OpenAlex

Spatial reasoning correlates with academic success in mathematics and science, is highly malleable, and can be learned. Yet, spatial reasoning is often underused, underdeveloped, and ignored in current grade-school curriculum and teaching. This study explores changes in elementary students’ spatial reasoning skills after participation in either a short-term or a long-term robotics intervention. The robotics intervention and the spatial elements within the intervention tasks are described. Measures of spatial reasoning elements were administered before and after each intervention. Two different groups of students ages 9–10 years were tested: a short-term group (N = 11) and a long-term group (N = 48). Statistical analysis revealed significant improvements to several different elements of spatial reasoning in both groups. Findings suggest that programming robots in either the short- or long-term intervention was associated with improvements in spatial reasoning.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.042
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0020.004
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.015
GPT teacher head0.267
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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

Citations13
Published2021
Admission routes2
Has abstractno

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