Journal
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
Volume 7, Issue 2, Pages 352-361Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSS.2020.2965198
Keywords
Adaptive game system; k-nearest-neighbor (kNN); parallel intelligent education; personalized learning
Funding
- National Science Foundation [1913809, 1610164]
- Division Of Undergraduate Education
- Direct For Education and Human Resources [1610164, 1913809] Funding Source: National Science Foundation
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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.
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