Bayesian Inference of Students' Knowledge Based on a Potential Answer Tree Using STACK, A Mathematics e-Learning System

ID: 52586 Type: Poster/Demo
  1. Yasuyuki Nakamura, Nagoya University, Japan

Wednesday, March 28 5:45 PM-7:00 PM Location: Edison Ballroom D View on map

Presider: Rashid Khan, DCC- King Fahd University of Petroleum and Minerals, Dhahran Saudi Arabia, Saudi Arabia

Abstract: We propose a procedure for inferring students' knowledge using the Bayesian network approach in the mathematics e-learning system STACK. If we appropriately construct a potential response tree (PRT) for STACK based on the knowledge structure and conditional probabilities among potential responses to questions - which are calculated based on a large amount of learning data - we can infer students' knowledge on the condition that a specific student's answer is obtained. This procedure is a well-known technique, and applying the Bayesian network to construct PRTs for STACK allows for the possibility of learning analytics through a mathematics e-learning system.


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