IMPROVEMENTS IN A CROSS-COHORT MECHANCICAL ENGINEERING COURSE PROJECT
Bibliographic record
Abstract
A cross-cohort project was created and implemented as part of the core curriculum for mechanical engineering students. A team of second-year students in “Dynamics” course was grouped with a team of third-year students in “Kinematics and Dynamics of Machines” course where they designed, prototyped and conducted dynamic motion analysis of a pick and place mechanism. Each cohort was tasked to create a sub-mechanism, combining these two mechanisms created the final machine. The teaching and learning activities are defined towards accomplishing four main interrelated objectives: (1) To provide a design challenge to guide students to implement creative potential solutions. (2) To allow second-year and third-year teams to analyze the dynamic motion of their mechanism while considering the design of the other group’s mechanism. (3) To introduce industrial dynamic simulation tools and hands on prototyping skills. (4) To facilitate cross-cohort collaboration within teams with more emphasis on students’ peer exchange of knowledge and experience. With the experience gained from conducting the project, evaluating the students’ reports, and student feedback, several modifications can be implemented in future iterations to allow the students to benefit more from this kind of project structure. This research discusses improvements based on the lessons learned.
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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 | 0.025 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".