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Record W2764221628 · doi:10.14434/ijdl.v8i1.22665

Challenges and Tradeoffs When Engaging Young Makers With Constructing for Others

2017· article· en· W2764221628 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.

Bibliographic record

VenueInternational Journal of Designs for Learning · 2017
Typearticle
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsLearning Partnership
Fundersnot available
KeywordsMentorshipLeverage (statistics)Function (biology)Process (computing)Engineering design processComputer scienceDiversity (politics)Key (lock)Rapid prototypingKnowledge managementProcess managementEngineeringBusinessSociologyComputer securityMarketing

Abstract

fetched live from OpenAlex

As makerspaces and fabrication labs enter schools as a means of motivating children to explore STEM fields, the lack of diversity in engineering and computing must be addressed. The Bots for Tots project explores the potential of leveraging deeper values and perspectives in making practices by engaging young children in designing and creating objects for others rather than for themselves. In this design case, we present outcomes from the first Bots for Tots implementation highlighting key design challenges and tradeoffs for (a) encouraging a personal relationship between builders and clients while retaining design complexity, and (b) ensuring productive prototyping while providing materials and tools with which designers are familiar. We also discuss revisions for a second iteration where we leverage an existing mentorship program to ensure close designer-client relationships, and constrain material choices throughout the construction process to encourage participants to focus on function and process during prototyping.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.972
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.084
GPT teacher head0.322
Teacher spread0.237 · 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