EVALUATING THE IMPACT OF TABLET COMPUTERS ON DESIGN ENGINEERING
Bibliographic record
Abstract
Since 2005, with the endowment of the NSERC-GMCL Chair in Innovative Design Engineering at UOIT, and the Laptop-based, web-centric teaching approach, an ideal setting for the creation, prompt adoption, and implementation of advanced and innovative practices in teaching design engineering have been implemented, in addition to the use of traditional methods. A pilot program was recently completed to evaluate the use of Tablets in an engineering course. Tablets are currently used by faculty for teaching purposes at UOIT, but the program aims to integrate the use of Tablets within courses in the engineering design curriculum, namely for using CAD/CAM/CAE software. As CAD software capabilities improve, greater memory and computer speed is required, making the currently used conventional Laptops less useful for engineering design. In addition, Laptops do not lend themselves to graphical, free-form idea generation. It is intended that Tablets, with improved memory and processing speed, will facilitate CAD software usage, and hence, improve and enhance the overall design learning and application experience. Also, students can take advantage of software such as Microsoft OneNote to create preliminary sketches of designs and improve record-keeping of decisions during team meetings. In this pilot program, Tablet computers were issued to students and instruction personnel in a fourth-year Advanced Mechatronics course at UOIT. In this context, students were able to more efficiently carry out design assignments for term design projects, and students and instructors were able to evaluate the benefits of using Tablets. Overall, it was determined that Tablets were better as an engineering design tool compared to traditional Laptops.
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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".