Learning Analytics Should Analyse the Learning: Proposing a Generic Stealth Assessment Tool

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

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


    Stealth assessment could radically extend the scope and impact of learning analytics. Stealth assessment refers to the unobtrusive assessment of learners by exploiting emerging data from their digital traces in electronic learning environments through machine learning technologies. So far, stealth assessment has been studied extensively in serious games, but has not been widely applied, as it is a laborious and complex methodology for which no support tools are available. This study proposes a generic tool for the arrangement of stealth assessment to remove its current limitations and pave the road for its wider adoption. It describes the conceptual design of such a tool including its requirements regarding users, functions, and workflow. A prototype was implemented as a basic console application covering the tool's core requirements, including a Gaussian Naïve Bayes Network utility. Generated input files were used fortesting and validating the approach. In a controlled test condition the stealth assessment classification accuracy was found to be inherently stable and high (typically above 92%). It is argued that the proposed approach could radically increase the applicability of stealth assessment in serious games and inform current learning analytics approaches with unobtrusive, more detailed and genuine assessments of learning.
    Original languageEnglish
    Title of host publicationIEEE Conference on Games 2019
    Subtitle of host publicationLondon, United Kingdom 20-23 August 2019
    Number of pages8
    ISBN (Electronic)9781728118840
    ISBN (Print)9781728118857
    Publication statusPublished - Aug 2019
    EventIEEE Conference on Games (CoG) 2019 - Queen Mary University of London, London, United Kingdom
    Duration: 20 Aug 201923 Aug 2019


    ConferenceIEEE Conference on Games (CoG) 2019
    Abbreviated titleCOG 2019
    Country/TerritoryUnited Kingdom
    Internet address


    • Generic tool
    • Learning analytics
    • Machine learning
    • Serious games
    • Stealth assessment


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