Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 539-552 |
| Number of pages | 14 |
| Journal | Interactive LearnIng Environments |
| Volume | 26 |
| Issue number | 4 |
| Early online date | 29 Aug 2017 |
| DOIs | |
| Publication status | Published - 2018 |
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Keywords
- Serious game
- learning
- simulation
- flow theory
- computational model
- methodology
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Simulating serious games: a discrete-time computational model based on cognitive flow theory. / Westera, Wim.
In: Interactive LearnIng Environments, Vol. 26, No. 4, 2018, p. 539-552.Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Simulating serious games: a discrete-time computational model based on cognitive flow theory
AU - Westera, Wim
PY - 2018
Y1 - 2018
N2 - This paper presents a computational model for simulating how people learn from serious games. While avoiding the combinatorial explosion of a games micro-states, the model offers a meso-level pathfinding approach, which is guided by cognitive flow theory and various concepts from learning sciences. It extends a basic, existing model by exposing discrete-time evolution, allowing for failure, drop-out, and revisiting of activities, and accounting for efforts made and time spent on tasks, all of which are indispensable elements of gaming. Three extensive simulation studies are presented involving over 10,000 iterations across a wide range of game instances and player profiles for demonstrating model stability and empirical admissibility. The model can be used for investigating quantitative dependences between relevant game variables, gain deeper understanding of how people learn from games, and develop approaches to improving serious game design.
AB - This paper presents a computational model for simulating how people learn from serious games. While avoiding the combinatorial explosion of a games micro-states, the model offers a meso-level pathfinding approach, which is guided by cognitive flow theory and various concepts from learning sciences. It extends a basic, existing model by exposing discrete-time evolution, allowing for failure, drop-out, and revisiting of activities, and accounting for efforts made and time spent on tasks, all of which are indispensable elements of gaming. Three extensive simulation studies are presented involving over 10,000 iterations across a wide range of game instances and player profiles for demonstrating model stability and empirical admissibility. The model can be used for investigating quantitative dependences between relevant game variables, gain deeper understanding of how people learn from games, and develop approaches to improving serious game design.
KW - Serious game
KW - learning
KW - simulation
KW - flow theory
KW - computational model
KW - methodology
U2 - 10.1080/10494820.2017.1371196
DO - 10.1080/10494820.2017.1371196
M3 - Article
VL - 26
SP - 539
EP - 552
JO - Interactive LearnIng Environments
JF - Interactive LearnIng Environments
SN - 1049-4820
IS - 4
ER -