TY - GEN
T1 - Reinforcing Stealth Assessment in Serious Games
AU - Georgiadis, Konstantinos
AU - van Lankveld, Giel
AU - Bahreini, Kiavash
AU - Westera, Wim
PY - 2019/11
Y1 - 2019/11
N2 - Stealth assessment is a principled assessment methodology proposed for serious games that uses statistical models and machine learning technology to infer players’ mastery levels from logged gameplay data. Although stealth assessment has been proven to be valid and reliable, its application is complex, laborious, and time-consuming. A generic stealth assessment tool (GSAT), proven for its robustness with simulation data, has been proposed to resolve these issues. In this study, GSAT’s robustness is further investigated by using real-world data collected from a serious game on personality traits and validated with an associated personality questionnaire (NEO PI-R). To achieve this, (a) a stepwise regression approach was followed for generating statistical models from logged data for the big five personality traits (OCEAN model), (b) the statistical models are then used with GSAT to produce inferences regarding learners’ mastery level on these personality traits, and (c) the validity of GSAT’s outcomes are examined through a correlation analysis using the results of the NEO PI-R questionnaire. Despite the small dataset GSAT was capable of making inferences on players’ personality traits. This study has demonstrated the practicable feasibility of the SA methodology with GSAT and provides a showcase for its wider application in serious games.
AB - Stealth assessment is a principled assessment methodology proposed for serious games that uses statistical models and machine learning technology to infer players’ mastery levels from logged gameplay data. Although stealth assessment has been proven to be valid and reliable, its application is complex, laborious, and time-consuming. A generic stealth assessment tool (GSAT), proven for its robustness with simulation data, has been proposed to resolve these issues. In this study, GSAT’s robustness is further investigated by using real-world data collected from a serious game on personality traits and validated with an associated personality questionnaire (NEO PI-R). To achieve this, (a) a stepwise regression approach was followed for generating statistical models from logged data for the big five personality traits (OCEAN model), (b) the statistical models are then used with GSAT to produce inferences regarding learners’ mastery level on these personality traits, and (c) the validity of GSAT’s outcomes are examined through a correlation analysis using the results of the NEO PI-R questionnaire. Despite the small dataset GSAT was capable of making inferences on players’ personality traits. This study has demonstrated the practicable feasibility of the SA methodology with GSAT and provides a showcase for its wider application in serious games.
U2 - 10.1007/978-3-030-34350-7_49
DO - 10.1007/978-3-030-34350-7_49
M3 - Conference Article in proceeding
SN - 9783030343491
T3 - Lecture Notes in Computer Science (LNCS)
SP - 512
EP - 521
BT - Games and Learning Alliance
A2 - Liapis, Antonios
A2 - Yannakakis, Georgios N.
A2 - Gentile, Manuel
A2 - Ninaus, Manuel
PB - Springer
CY - Cham
T2 - 8th International Conference on Games and Learning Alliance
Y2 - 27 November 2019 through 29 November 2019
ER -