Toward Performance Prediction Using In-Game Measures

Sylvester Arnab, Odafe Imiruaye, Fotis Liarokapis, Gemma Tombs, Petros Lameras, Angel Serrano-Laguna, Pablo Moreno-Ger

    Research output: Contribution to conferencePaperpeer-review

    68 Downloads (Pure)


    The efficacy of a learning process is influenced by the quality of teaching, learning support and environment. This requires effort in tracking how students learn. This paper explores the use serious games in order to help understand the learning process, where interaction data during a play-learn session can be captured. The focus is on the use of ingame data, analyzed using Learning Analytics techniques, and discusses the potential of such an approach to predict learners’ performance. Gameplay data were collected from various play-learn sessions based on a First Aid Game. Results indicate that in-game measures can help to understand students’ progress and predict their performance, providing opportunities for individual support to be provided to learners.
    Original languageEnglish
    Publication statusPublished - 20 Apr 2015
    EventAmerican Educational Research Association annual meeting - Chicago, Illinois, Chicago, Illinois, United States
    Duration: 16 Apr 201520 Apr 2015


    ConferenceAmerican Educational Research Association annual meeting
    Country/TerritoryUnited States
    CityChicago, Illinois

    Bibliographical note

    This paper was presented on the 20th April 2015 at the AERA (American Educational Research Association) conference, Chicago, Illinois.
    Arnab, S. , Imiruaye, O. , Liarokapis, F. , Tombs, G. , Lameras, P. , Serrano-Laguna, Angel and Moreno-Ger, Pablo. 2015, 20 April. Toward Performance Prediction Using In-Game Measures. Paper presented at
    the 2015 annual meeting of the American Educational Research
    Association. Retrieved 18 August 2015, from the AERA Online Paper


    • serious games
    • performance prediction
    • user studies
    • learning analytics
    • game-based learning


    Dive into the research topics of 'Toward Performance Prediction Using In-Game Measures'. Together they form a unique fingerprint.

    Cite this