Improved modeling of intelligent tutoring systems using ant colony optimization

被引：24

作者：

Rastegarmoghadam M. ^{[1
]}

Ziarati K. ^{[1
]}

机构：

[1] Department of Computer Science & Engineering and Information Technology, Shiraz University, Molla Sadra Ave, NO. 2, Shiraz

来源：

Education and Information Technologies | 2017年 / 22卷 / 3期

关键词：

Adaptive learning; Ant colony optimization; Learning style; Problem Solving style; Swarm intelligence;

D O I：

10.1007/s10639-016-9472-2

中图分类号：

学科分类号：

摘要：

Swarm intelligence approaches, such as ant colony optimization (ACO), are used in adaptive e-learning systems and provide an effective method for finding optimal learning paths based on self-organization. The aim of this paper is to develop an improved modeling of adaptive tutoring systems using ACO. In this model, the learning object is personalized based on learning and solving problem styles. The purposed algorithm, based on ACO, generates the adaptive optimal learning path. The algorithm describes an architecture which supports the recording, processing and presentation of collective learner behavior designed to create a feedback loop informing learners of successful paths towards the attainment of learning goals. The algorithm parameters are tuned dynamically to conform to the actual pedagogical process. The article includes the results of implementation and experiment represent this algorithm is able to provide its main purpose which is finding optimal learning paths based on learning styles and improved performance of previous adaptive tutoring systems. © 2016, The Author(s).

引用

页码：1067 / 1087

页数：20

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