Bolstering Stealth Assessment in Serious Games

Konstantinos Georgiadis*, Tjitske Faber, Wim Westera

*Corresponding author for this work

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


    Stealth assessment is an unobtrusive assessment methodology in serious games that use digital player traces to make inferences of players’ expertise level over competencies. Although various proofs of stealth assessment’s validity have been reported, its application is a complex, laborious, and time-consuming process. To bolster the applicability of stealth assessment in serious games; a generic stealth assessment tool (GSAT) has been proposed, which uses machine learning techniques to reason over competence constructs, player log data and assess player performance. Current study provides empirical validation of GSAT by applying it to a real-world game, the abcdeSIM game, which was designed to train medical care workers to act effectively medical emergency situations. GSAT demonstrated, while relying on a Gaussian Naive Bayes Network, to be highly robust and reliable, achieving a three-level assessment accuracy of 96%, as compared with a reference score model defined by experts. By this result, this study contributes to the alleviation of stealth assessment’s applicability issues and hence promotes its wider uptake by the serious game community.
    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
    Number of pages10
    ISBN (Electronic)9783030343507
    ISBN (Print)9783030343491
    Publication statusPublished - 27 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)


    Conference8th International Conference on Games and Learning Alliance
    Abbreviated titleGALA 2019


    • ABCDE-method
    • Generic tool
    • Machine learning
    • Serious games
    • Statistical model
    • Stealth assessment
    • Stepwise regression


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