Bibliographic record
Abstract
The makerspace movement is gaining prominence within higher education. With the promise of improving the student learning experience, institutions invest space and resources to support making and the maker movement. The focus of my study was how postsecondary students engage in learning through makerspace in non-STEM (Science, Technology, Engineering, and Mathematics) courses in an eastern Canadian university. The qualitative case study investigated the implications maker activities have on learning in three non-STEM (Education and Geography) courses. The following questions guided the inquiry: How do postsecondary students engage in learning through makerspace activities in non-STEM courses? What is the nature of academic, social and intellectual student engagement when learning through making in non-STEM course environments? Furthermore, what factors influence or hinder the usage of makerspaces in non-STEM postsecondary course contexts? Data were collected using interviews, observations, and questionnaires with three different classes with subsequent thematic analysis. Three common themes emerged: how students perceived engagement, the impact of an experienced instructor, and the challenges associated with makerspace in a classroom environment. What differed between the three classes was the level of expertise between instructors, the maker activities' format, and the technology used. This study's significant contribution is that it reveals the importance of engagement for both instructor and student. Using makerspaces is one tool that could be considered in non-STEM courses in a university to enhance learning through engagement. For instructors and students to use makerspaces successfully, they must help solve an authentic problem, have experienced staff, have adequate infrastructure, and allow students to reflect on their problems. Implications for practicing makerspaces can be considered at various university leadership levels, from instructor to educational development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".