Makerspaces in First-Year Engineering Education
Bibliographic record
Abstract
Langara College, as one of the leading undergraduate institutions in the province of British Columbia (BC), offers the “Applied Science for Engineering” two-year diploma program as well as the “Engineering Transfer” two-semester certificate program. Three project-based courses are offered as part of the two-year diploma program in Applied Science (APSC) and Computer Science (CPSC) departments: “APSC 1010—Engineering and Technology in Society”, “CPSC 1090—Engineering Graphics”, and “CPSC 1490—Applications of Microcontrollers”, with CPSC 1090 and CPSC 1490 also part of the Engineering Transfer curriculum. Although the goals, scopes, objectives, and evaluation criteria of these courses are different, the main component of all three courses is a group-based technical project. Engineering students have access to Langara College’s Makerspace for the hands-on component of their project. Makerspaces expand experiential learning opportunities and allows students to gain a skillset outside the traditional classroom. This paper begins with a detailed review of the maker movement and the impact of makerspace in higher education. Different forms of makerspace and the benefits of incorporating them on first-year students’ creativity, sense of community, self-confidence, and entrepreneurial skills are discussed. This paper introduces Langara’s engineering program and its project-based design courses. Langara’s interdisciplinary makerspace, its goals and objectives, equipment, and some sample projects are introduced in this paper in detail. We then explain how the group-project component of APSC 1010, CPSC 1090, and CPSC 1490 are managed and how using makerspace improves students’ performance in such projects. In conclusion, the paper describes the evaluation of learning outcomes via an anonymous student survey.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".