Simulation‐based Learning in Engineering Education: Performance and Transfer in Learning Project Management
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
Abstract This paper reports empirical findings on the impact of keeping and reviewing learning history in a dynamic and interactive simulation environment of engineering education. The simulator for engineering project management had two learning history keeping modes: automatic (simulator‐controlled) and manual (student‐controlled), and a version with no history keeping. A group of industrial engineering students performed four simulation‐runs divided into three identical simple scenarios (single project) and one complicated scenario (multi‐project). The performances of participants running the simulation with the manual history mode were significantly better than users running the simulation with the automatic history mode. Moreover, the effects of using the history mechanism with the ability to undo further enhanced the learning process. The findings imply that students' decision when to record the history during their engineering training process can have a particularly strong enhancing effect on learning. In addition, the simulator as educational innovation improves students learning and performance. The practical implications of using simulators in the field of engineering learning are discussed.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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