A learner's journey towards a chemical engineering degree
Bibliographic record
Abstract
Abstract The overall goal of any engineering program is to maximize the capacity of its graduates to succeed academically and professionally. We describe how a path can be designed for learners to proceed towards this goal and describe the rationale used to create its foundational steps. It begins with a summer bridge program for incoming students before entering university as first‐year undergraduates. Since the prior knowledge of these learners is not uniform, the bridge program is designed to provide opportunities for them to become better prepared academically for first‐year engineering. These students thus transition to university‐level learning more smoothly. In their first year, students work in groups to tackle socially relevant projects through an integrated 13‐unit course that is designed based on integrated learning, collaboration, problem‐solving, community engagement, and communication. Since teaching and learning using this approach is unusual and challenging, the curriculum must be carefully designed and implemented with adequate resources in place, particularly for cohorts of more than 1000 students in our case who work in small four or five‐member teams. In upper years, learning in chemical engineering is enriched by conducting discovery‐based workshops where students work on engineering problems requiring the application of new mathematical concepts. Finally, we describe a hybrid method for testing and assessment, where learners take tests individually, following which they are also provided with the option to retake these tests in groups to promote collaborative learning. Retaking tests in teams enhances the ability of learners to reflect and learn from mistakes and promotes peer mentoring.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".