Combining Simulation-based Training and Flipped Classroom in Project Management Learning
Bibliographic record
Abstract
Every year, countless projects are finished late, go over budget or end up being cancelled, often because their project managers and project teams lack the necessary tools and techniques to support their decision-making. Students of project management courses around the world have difficulty integrating the different knowledge areas of project management, after studying each knowledge area separately. Students then struggle and even fail when it comes to applying these concepts in a real-life project. Simulation-based training contributes to the solution of these problems by linking the concepts learned during a project management course and providing the experience of managing a simulated project that serves as preparation for real life. The objective of this research is to study the impact of simulation-based training and flipped classroom methodology on students learning project management. The contribution of this research is twofold. First, from a theoretical perspective, simulation-based training and flipped classroom methodology literature is enriched and broadened by applying both teaching tools. Second, from a practical perspective, an improvement in results, satisfaction and lessons learned was found when using simulation-based training under flipped classroom methodology compared to simulation-based training in a traditional classroom.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".