A CASE STUDY ON BLENDED LEARNING IN ENGINEERING EDUCATION
Bibliographic record
Abstract
This paper explores a case study of implementing blended learning in a third-year engineering course. In “Mechanical and Thermal Energy Conversion Processes”, blended learning was implemented by flipping the classroom (i.e. reversing the roles of lectures and homework) for selected units of the course. While flipping an entire course can be a significant undertaking, it can be much easier to take a blended approach and only flip lectures on selected topics. Many studies on flipped classroom learning have focused on the production of online lectures and active learning methods; often these case studies have overlooked the mechanisms to bring homework into the classroom. In this case study, homework was adapted into a variety of in-class activities, composed of hands-on learning, problem solving, and classroom discussions. In addition, a variety of classroom space types were used to conduct these activities. In this paper, the successes, challenges, and lessons learned for each type of activity and classroom space are discussed. Strategies for student engagement and acceptance of blended learning are also discussed.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| 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".