Abstract
| Original language | English |
|---|---|
| Title of host publication | Games and Learning Alliance |
| Subtitle of host publication | 8th International Conference, GALA 2019, Athens, Greece, November 27–29, 2019, Proceedings |
| Editors | Antonios Liapis, Georgios N. Yannakakis, Manuel Gentile, Manuel Ninaus |
| Place of Publication | Cham |
| Publisher | Springer |
| Chapter | 49 |
| Pages | 512-521 |
| Number of pages | 10 |
| ISBN (Electronic) | 9783030343507 |
| ISBN (Print) | 9783030343491 |
| DOIs | |
| Publication status | Published - Nov 2019 |
| Event | 8th International Conference on Games and Learning Alliance - Athens, Greece Duration: 27 Nov 2019 → 29 Nov 2019 |
Publication series
| Series | Lecture Notes in Computer Science (LNCS) |
|---|---|
| Volume | 11899 |
| ISSN | 0302-9743 |
Conference
| Conference | 8th International Conference on Games and Learning Alliance |
|---|---|
| Abbreviated title | GALA 2019 |
| Country | Greece |
| City | Athens |
| Period | 27/11/19 → 29/11/19 |
Fingerprint
Cite this
}
Reinforcing Stealth Assessment in Serious Games. / Georgiadis, Konstantinos; van Lankveld, Giel; Bahreini, Kiavash; Westera, Wim.
Games and Learning Alliance: 8th International Conference, GALA 2019, Athens, Greece, November 27–29, 2019, Proceedings. ed. / Antonios Liapis; Georgios N. Yannakakis; Manuel Gentile; Manuel Ninaus. Cham : Springer, 2019. p. 512-521 (Lecture Notes in Computer Science (LNCS), Vol. 11899).Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review
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
ER -