Reinforcing Stealth Assessment in Serious Games
| Autor: | Georgiadis, Konstantinos, van Lankveld, Giel, Bahreini, Kiavash, Westera, Wim, Liapis, Antonios, Yannakakis, Georgios N., Gentile, Manuel, Ninaus, Manuel |
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| Přispěvatelé: | Liapis, Antonios, Yannakakis, Georgios N., Gentile, Manuel, Ninaus, Manuel, RS-Theme Applied Gaming and Simulation, Department FEEEL, Rage project |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Serious games
Computer science business.industry Statistical model Serious game Stepwise regression Machine learning computer.software_genre Robustness (computer science) Correlation analysis Generic tool Personality questionnaire Artificial intelligence Personality traits Big Five personality traits Stealth assessment business computer |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030343491 GALA Georgiadis, K, van Lankveld, G, Bahreini, K & Westera, W 2019, Reinforcing Stealth Assessment in Serious Games . in A Liapis, G N Yannakakis, M Gentile & M Ninaus (eds), Games and Learning Alliance : 8th International Conference, GALA 2019, Athens, Greece, November 27–29, 2019, Proceedings . Springer, Cham, Lecture Notes in Computer Science (LNCS), vol. 11899, pp. 512-521, 8th International Conference on Games and Learning Alliance, Athens, Greece, 27/11/19 . https://doi.org/10.1007/978-3-030-34350-7_49 Games and Learning Alliance: 8th International Conference, GALA 2019, Athens, Greece, November 27–29, 2019, Proceedings, 512-521 STARTPAGE=512;ENDPAGE=521;TITLE=Games and Learning Alliance |
| ISSN: | 0302-9743 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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