RE-ENGINEERING SUCCESS: YEAR TWO OF A CROSS-COURSE ASSIGNMENT TO DEVELOP CRITICAL THINKING AND COMMUNICATION SKILLS IN A LAB SETTING
Bibliographic record
Abstract
Last year we reported on a new initiative designed to help students develop their ability to communicate to a client while applying technical lab skills in a joint, end-of-term problem-based learning exercise that we incorporated into our second-year Chemical Engineering curriculum at University of Toronto. The project asked students to research and develop a lab-basedapproach to a real-world problem and to communicate their solution to a client through various deliverables. A student survey, as well as our own observations and student performance have led to revisions in year two, specifically in our feedback schedule, the composition of our grading team, and the nature of a related term two project. This paper discusses the implementation of these changes, and their success using new student survey data and performance. We conclude that these changes have improved student experience and technical performance; communication performance can be further improved by more consistent training of the instructional and grading team, and additional project support in term two.
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 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".