Teaching Line Balancing through Active and Blended Learning*
Bibliographic record
Abstract
ABSTRACT The design of balanced assembly lines, especially when considering workforce, material, and cycle time factors, is an important managerial decision‐making activity in manufacturing firms. Numerous active learning activities are available to assist instructors in teaching assembly line balancing to students. While effective in improving student engagement, they require considerable planning and expense on the part of instructors, and they may be difficult to implement in inflexible teaching spaces and lecture‐oriented curricula. We present a new approach to teaching line balancing using online videos depicting an assembly process. Students design an assembly line by determining themselves how to separate and time tasks, rather than by modifying an existing configuration. To save valuable classroom time, students complete a portion of the activity outside of class. This blended learning approach allows for all students to be engaged in the activity, both in and out of class. Furthermore, a controlled study showed that compared to the traditional lecture format, it better equips students to address less tangible aspects of line balancing, such as ergonomic and workforce factors, material handling considerations, and changing cycle time. With the online content for this activity completely developed and available, other instructors can easily implement this approach within their courses.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".