Lessons Learned from Implementing Web-Based Simulations to Teach Operations Management Concepts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The usage of Web-based computer simulations to facilitate experiential learning of operations management concepts continues to increase; however, successfully administering and incorporating them into a curriculum can be challenging. Littlefield Technologies is a popular simulation used in operations management education; however, there is limited literature providing guidance on how to successfully implement the simulation into a postsecondary course. Based on years of experience employing Littlefield and other computer-based simulations to over 2,500 students, we provide five keys specifically for implementing Littlefield and guidance on administering computer-based simulations in general. Student survey results reveal that over 86% recommend the continued usage of Littlefield, and that over two-thirds of students report increased interest in operations management because of the simulation. A comparison of results by course type indicates that MBA students outperformed undergraduate students, and that performance appears to improve when results-based marks are incorporated into their final grade rather than bonus marks. Finally, we discuss some forthcoming improvements to the Littlefield simulation and provide some additional improvement recommendations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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 it