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 language | English (US) |
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Article number | 9044626 |
Pages (from-to) | 352-361 |
Number of pages | 10 |
Journal | IEEE Transactions on Computational Social Systems |
Volume | 7 |
Issue number | 2 |
DOIs | |
State | Published - Apr 2020 |
All Science Journal Classification (ASJC) codes
- Modeling and Simulation
- Social Sciences (miscellaneous)
- Human-Computer Interaction