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Stealth assessment derives the progression of learning in an unobtrusive way from observed gameplay captured in log files. To this end, it uses machine learning technologies to provide probabilistic reasoning over established latent competency variable models. Now that video games are increasingly being used for training and learning purposes, stealth assessment could provide an excellent means of monitoring learning progress without the need for explicit testing. However, applying stealth assessment is a complex and laborious process. This paper analyses the limitations of stealth assessment and conceptualizes the requirements for developing a generic tool that could overcome its barriers and accommodate its practical application. Hence, a framework is presented describing its user and functional requirements. The proposed generic solution could open up the wider uptake of stealth assessment in serious games.
|Title of host publication||2018 10th International Conference on Virtual Worlds and Games for Serious Applications (VS-Games)|
|Place of Publication||Würzburg, Germany|
|Number of pages||4|
|Publication status||Published - 23 Oct 2018|
- stealth assessment
- machine learning
- serious games
- generic tool
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Rage: Realising an Applied Gaming Eco-system
Westera, W., Georgiadis, K., Saveski, G., van Lankveld, G., Bahreini, K., van der Vegt, W., Berkhout, J., Nyamsuren, E., Kluijfhout, E. & Nadolski, R.
1/02/15 → 31/07/19