A Personalized Learning System for Parallel Intelligent Education

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

Research output: Contribution to journalArticle

Abstract

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)
JournalIEEE Transactions on Computational Social Systems
DOIs
StateAccepted/In press - Jan 1 2020

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All Science Journal Classification (ASJC) codes

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

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