Automatic student modeling in adaptive educational systems through probabilistic learning style combinations: a qualitative comparison between two innovative stochastic approaches

Brazilian Computer Society. Journal

Fabiano A. Dorç¹  Má²  a³  rcia A. Fernandes³  Luciano V. Lima⁴  Carlos R. Lopes⁴ 

[1] Faculty of Computer Science, FACOM, Federal University of UberlâFaculty of Electrical Engineering, FEELT, Federal University of Uberlândia (UFU), Uberlândia, Brazil

关键词: Automatic learning styles assessment;    Student modeling;    Stochastic detection;    E-learning;    Adaptive educational systems;   

DOI  :  10.1007/s13173-012-0078-2

学科分类：农业科学（综合）

来源: Springer U K

【 摘 要 】

Considering learning and how to improve students’ performances, adaptive educational systems must know the way in which an individual student learns best. In this context, this work presents a comparison between two innovative approaches to automatically detect and precisely adjust students’ learning styles during an adaptive course. These approaches take into account the nondeterministic and nonstationary aspects of learning styles. They are based upon two stochastic techniques: Markov chains and genetic algorithms. We found that the genetic algorithm (GA) based approach detects learning styles earlier and consequently provides personalized content earlier, making the learning process easier. The Markov based approach produces more fine-tuned results, taking into account strengths of learning styles.

【 授权许可】

CC BY