CAPSTONE PROJECTS WITH LIMITED BUDGET AS AN EFFECTIVE METHOD FOR EXPERIENTIAL LEARNING
Bibliographic record
Abstract
This paper describes a method that is used inSchool of Engineering Technology Department atMcMaster University to promote experiential learning atundergraduate level through capstone projects.The goal of this paper is to demonstrate that capstoneprojects with limited budget can be effectively used toimplement experiential learning methodology toengineering students.According to this methodology, students work ingroups of three over two academic terms, to completecapstone projects that require designing, building,testing, and prototyping a final product. The methodologyis addressed from the perspective of experiential learningtheory, and several project cost-reducing strategies arepresented.Selected results from student capstone projects atMcMaster University are presented and discussed.Results indicate that capstone projects can besuccessfully implemented into experiential learningmethodology when adequate strategies for reducing theproject costs are considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".