A Personalized Learning System for Parallel Intelligent Education

Ying Tang, Joleen Liang, Ryan Hare, Fei Yue Wang

    Research output: Contribution to journalArticlepeer-review

    3 Scopus citations


    Technological advancement has given education a new definition - parallel intelligent education - resulting in fundamentally new ways of teaching and learning. This article exemplifies an important component of parallel intelligent education - artificial education system in a narrative game environment to offer personalized learning. The system collects data on the player's actions while they play, assessing their concept knowledge via k-nearest-neighbor (kNN) classification, and provides tailored feedback to that student as they play the game. Based on an empirical evaluation, the kNN-based game system is shown to accurately provide players with differentiated instructions to guide them through the learning process based on the estimation of their knowledge levels.

    Original languageEnglish (US)
    Article number9044626
    Pages (from-to)352-361
    Number of pages10
    JournalIEEE Transactions on Computational Social Systems
    Issue number2
    StatePublished - Apr 2020

    All Science Journal Classification (ASJC) codes

    • Modeling and Simulation
    • Social Sciences (miscellaneous)
    • Human-Computer Interaction


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