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Record W2935126383

Investigating a School STEM Lab as a Site for Collaborative Inquiry: Exploring Makerspace Potential and Imbedded Creativity Through Reflective Practice

2018· article· en· W2935126383 on OpenAlexaffabout
James Gauthier, Marina Milner‐Bolotin, Douglas Adler, Samson Madera Nashon

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2018
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCreativityCurriculumConstruct (python library)Mathematics educationPedagogySpace (punctuation)SociologyPsychologyComputer science
DOInot available

Abstract

fetched live from OpenAlex

This paper summarizes research conducted a public secondary school in Vancouver, British Columbia, where teachers have developed and implemented a to teach science, technology, engineering, and mathematics.  Mostly through interviews with teachers, the paper chronicles the history and development of the STEM Lab and explores educators' objectives in creating and maintaining the laboratory.  Also, the paper investigates the potential of the STEM Laboratory to become what some researchers call a makerspace, a space where participants (students and their teachers) may inquire and construct knowledge together---working collaboratively to develop deeper, more informed connections with technology and technological devices, with their communities, and with each other.  Moreover, the paper explores the extent to which educators believe that the STEM Lab responds to recent changes in the B.C. Curriculum and the extent to which the lab, in helping participants to build confidence and proficiency in technical knowledge, also fosters the development of more general skills, including proficiency in inquiry and problem-solving, creative self-expression, and collaborative participation in a community of learners.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.993

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 communication0.0000.000
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 head0.058
GPT teacher head0.323
Teacher spread0.265 · 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 designQualitative
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
Published2018
Admission routes2
Has abstractyes

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