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Record W4413939220 · doi:10.24908/iqurcp19920

Envisioning Inclusive and Sustainable Makerspaces in Education

2025· article· en· W4413939220 on OpenAlexvenueaboutno aff
Nuri Park

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsnot available
Fundersnot available
KeywordsSociologyPolitical science

Abstract

fetched live from OpenAlex

STEAM+ makerspaces have become increasingly prominent in higher education as outlets of creativity, collaboration, and applied learning. An examination of makerspaces across Canadian universities suggests that those designed with a focus on sustainability, equity, inclusion, and accessibility tend to operate more effectively. Using a scoping review approach, this study first examined existing makerspaces in faculties of education through employing interviews and document review to identify effective practices and common challenges. Next, an inventory of available materials was then completed, with attention to aligning resource selection and use with sustainable and inclusive practices. The inventory of available materials was subsequently used to develop makerspace ‘kits’ that could be integrated into classrooms and preservice teacher education. Insights from the review of ten schools, including stakeholder interviews, informed the design of these kits by highlighting the need for low-cost, accessible, and curriculum-relevant resources. Two prototype kits were created: the first, ‘jitterbugs,’ employed off-centered mass and a battery-powered motor to explore concepts of energy transfer, balance, and motion; the second, ‘winged gliders,’ introduced principles of aerodynamics by engaging learners with concepts such as mass, lift, and drag. These kits served as tangible resources for applying STEAM+ approaches in practice, providing preservice teachers and students with accessible, hands-on opportunities to connect theory with experiential learning.By integrating literature review, material evaluation, workshop facilitation, and stakeholder engagement, the project generated practical insights to inform the planning of the Faculty’s makerspace and contributed to emerging scholarship on the role of STEAM+ makerspaces in higher education as sites of creativity, collaboration, and community-responsive educational innovation.

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)0.003
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.038
GPT teacher head0.352
Teacher spread0.313 · 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 designTheoretical or conceptual
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
Published2025
Admission routes2
Has abstractyes

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