Reinforcing Stealth Assessment in Serious Games

Konstantinos Georgiadis*, Giel van Lankveld, Kiavash Bahreini, Wim Westera

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

    Abstract

    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.
    Original languageEnglish
    Title of host publicationGames and Learning Alliance
    Subtitle of host publication8th International Conference, GALA 2019, Athens, Greece, November 27–29, 2019, Proceedings
    EditorsAntonios Liapis, Georgios N. Yannakakis, Manuel Gentile, Manuel Ninaus
    Place of PublicationCham
    PublisherSpringer
    Chapter49
    Pages512-521
    Number of pages10
    ISBN (Electronic)9783030343507
    ISBN (Print)9783030343491
    DOIs
    Publication statusPublished - Nov 2019
    Event8th International Conference on Games and Learning Alliance - Athens, Greece
    Duration: 27 Nov 201929 Nov 2019

    Publication series

    SeriesLecture Notes in Computer Science (LNCS)
    Volume11899
    ISSN0302-9743

    Conference

    Conference8th International Conference on Games and Learning Alliance
    Abbreviated titleGALA 2019
    Country/TerritoryGreece
    CityAthens
    Period27/11/1929/11/19

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