Acquiring complex knowledge and skills through digital simulation‐based training: Evidence from an agile project management teaching experience
Bibliographic record
Abstract
Abstract This study examines the effectiveness of digital simulation‐based training (DSBT) for acquiring complex knowledge and skills. After identifying key aspects of DSBT, it explains how they were used to design a new DSBT activity with Minecraft Education® for teaching agile project management to dispersed students. It then presents a theoretically grounded model that links these key aspects of DSBT to learning perceptions. The model was tested over a 3‐year period through an online survey administered to groups of students who had participated in the new DSBT activity in a project management course. The findings show that two learning‐related beliefs, involvement and enjoyment, are crucial to the success of DSBT, and they offer valuable insights into the acquisition of these beliefs. The findings should be useful to educators and human resource professionals interested in integrating DSBT into their teaching and training practices. Practitioner notes What is already known about this topic? Digital simulation‐based training (DSBT) enhances learning and knowledge retention. Experiential activities have been used effectively at business schools to teach complex managerial skills. Learning involvement and enjoyment have been shown to be crucial to successful learning outcomes. What this paper adds? A theoretically grounded model linking DSBT to learning perceptions, particularly for teaching agile project management (APM) using Minecraft Education®. Empirical evidence for the important role of factors such as psychological safety, task interdependence and content relevance in shaping student involvement and enjoyment when DSBT is used. Insights into the unique challenges and opportunities related to implementing DSBT for remote learners. Implications for practice and/or policy Educators and practitioners can leverage the study findings to design effective DSBT courses and programmes that enhance both individual and collaborative learning experiences. Especially in remote learning contexts, they should strive to maintain focus and clarity while at the same time creating a psychologically safe learning environment. Incorporating real‐world scenarios into DSBT enhances learner engagement and ensures the applicability of knowledge and skills to real‐world settings.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".