A Personalized Learning System for Parallel Intelligent Education
Ying Tang, Joleen Liang, Ryan Hare, Fei–Yue Wang
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.