Incorporating Agile Methodologies Into the Chemical Engineering Curriculum
Bibliographic record
Abstract
ABSTRACT Agile methodologies, when applied within an engineering education context, can provide a strategic and insightful framework that can incorporate key pedagogical techniques to maximize the student learning experience. In this work, we present a revamp of an undergraduate chemical engineering data‐based modelling course by implementing two agile methodologies: XP‐pair programming and Sprint. The selected agile methodologies are implemented in tutorials and the final exam while developing and/or completing system identification codes in R as a computational tool. Student feedback is obtained via surveys to track the effectiveness of our implemented methodologies; students provided both general and subject‐specific feedback. Our unique approach promises to pave the way for novel course design and curriculum revamp and to enhance active and experiential learning experiences among students by merging education pedagogy with engineering practices in the industry. Student responses reveal that agile methodologies substantially improved their coding, modelling, teamwork and time management skills. We also observed that our agile‐based approach works to inspire and motivate students to (i) further their own knowledge of the subject matter, (ii) appreciate the importance of data‐based modelling in both industrial and academic environments and (iii) critically identify the fallacies and real‐life consequences of poor/inefficient modelling and prediction practices. Our initiative holds the potential to successfully implement well‐known industry best practices within a university chemical engineering curriculum. Our selected agile methodologies also facilitate active and experiential and enquiry‐based learning environments, leading to students recognizing the importance of ‘how’ to learn rather than ‘what’ to learn.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".