MétaCan
Menu
Back to cohort
Record W4307867055 · doi:10.53967/cje-rce.5455

Using Robotics to Support the Acquisition of STEM and 21st-Century Competencies: Promising (and Practical) Directions

2022· article· en· W4307867055 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Journal of Education / Revue canadienne de l éducation · 2022
Typearticle
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsUniversity of WaterlooMemorial University of Newfoundland
Fundersnot available
KeywordsRoboticsCurriculumArtificial intelligenceInterpersonal communicationIntrapersonal communicationPsychologyVariety (cybernetics)Mathematics educationComputer sciencePedagogyRobotCommunication

Abstract

fetched live from OpenAlex

To enhance how educators use robotics to support the development of STEM and 21st century competencies, we report findings from focus groups and interviews with 133 elementary teachers and 46 elementary students, 19 video-recorded classroom observations, and a teacher survey from Ontario, Canada. We find that teachers use robotics in a variety of ways to support the development of cognitive, interpersonal, and intrapersonal skills. Despite the potential benefits, our participants identified several factors that limit the adoption of robotics teaching and learning on a wider scale, including insufficient curriculum and assessment integration, resources, and professional development and support. We provide practical policy guidelines to support the broader integration of robotics and reflect on how these recommendations may inform teaching and learning in a (post-) COVID-19 classroom.

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.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.604
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.065
GPT teacher head0.300
Teacher spread0.235 · 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