Comparing strategies for modeling students learning styles through reinforcement learning in adaptive and intelligent educational systems: An experimental analysis

被引：59

作者：

Dorca, Fabiano A. ^{[1
,2
]}

Lima, Luciano V. ^{[2
]}

Fernandes, Marcia A. ^{[1
]}

Lopes, Carlos R. ^{[1
]}

机构：

[1] Univ Fed Uberlandia, Fac Comp Sci FACOM, BR-38400902 Uberlandia, MG, Brazil

[2] Univ Fed Uberlandia, Fac Elect Engn FEELT, BR-38400902 Uberlandia, MG, Brazil

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2013年 / 40卷 / 06期

关键词：

Student modeling; Learning styles; Adaptive and intelligent educational systems; Reinforcement learning; Student evaluation; e-Learning;

D O I：

10.1016/j.eswa.2012.10.014

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A huge number of studies attest that learning is facilitated if teaching strategies are in accordance with students learning styles, making the learning process more effective and improving students performances. In this context, this paper presents an automatic, dynamic and probabilistic approach for modeling students learning styles based on reinforcement learning. Three different strategies for updating the student model are proposed and tested through experiments. The results obtained are analyzed, indicating the most effective strategy. Experiments have shown that our approach is able to automatically detect and precisely adjust students' learning styles, based on the non-deterministic and non-stationary aspects of learning styles. Because of the probabilistic and dynamic aspects enclosed in automatic detection of learning styles, our approach gradually and constantly adjusts the student model, taking into account students' performances, obtaining a fine-tuned student model. (C) 2012 Elsevier Ltd. All rights reserved.

引用

页码：2092 / 2101

页数：10