Implementing Mini Design Projects to Maximize the Quality of Design-Build Term Project Student Work
Bibliographic record
Abstract
Project-based learning is a widely adopted strategy and a preferred pedagogical tool in the undergraduate engineering curriculum. However, design-and-build engineering projects are open-ended, ill-defined, and quite complex so that students often feel quite overwhelmed by the imposed need to solve relatively challenging and practical problems within limited time and resources. Although there are virtually no right or wrong feasible design engineering project solutions, over the years, students’ design project submissions identify a number of students with mediocre design competencies. This indicates that there is a need for developing a pedagogical strategy designed for assisting the students in better preparing for undertaking the challenges of term design engineering projects. Hence, a special series of deliberately designed small-scale “mini” design projects has been developed to serve as “just-in-time” means for building-up the students’ skills required to successfully undertake the tasks of the respective larger-scale term design projects. This paper focuses on exploring this strategy and the different ways of its implementation into the engineering curriculum through three representative core design courses at the beginner, intermediate and advanced levels, respectively.
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 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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".