Approach with Active-cooperative, Flipped and Hands-on Learning: A Case Study in Transport Phenomena
Bibliographic record
Abstract
This study reports the implementation and evaluation of an active learning strategy in an undergraduate Chemical Engineering course. The course was redesigned using a flipped classroom model, incorporating active-cooperative learning method. The weekly structure included three main components: (1) asynchronous video lectures to introduce core concepts, (2) in-class group work utilizing structured worksheets and small cooperative teams, and (3) hands-on simulations. The course Transport Phenomena served as the case study. Traditionally perceived as a mathematically challenging and abstract subject, Transport Phenomena often struggles to engage students effectively. To address this issue, the approach combined inverted classrooms, experiential learning, and the use of ANSYS Fluent in a computer lab setting. Student feedback was overwhelmingly positive: 90% of students rated the new methodology as adequate or highly effective, 93.3% preferred the guided study format over traditional lectures, and 73.3% found both the feedback process and the use of the computational fluid dynamics (CFD) tool beneficial for their learning. Additionally, academic outcomes improved, with the average final grade rising from 5.4 to 6.2, and there was a significant reduction in failure and remediation rates, even amid rigorous summative assessments. These results suggest that integrating flipped learning with active-cooperative and hands-on activities can enhance student engagement and support deeper learning in challenging engineering subjects.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".