WIT Press


Rule Discovery In Web-based Educational Systems Using Grammar-Based Genetic Programming

Price

Free (open access)

Paper DOI

10.2495/DATA050211

Volume

35

Pages

10

Published

2005

Size

1,763 kb

Author(s)

C. Romero, S. Ventura, C. Hervás & P. González

Abstract

This paper describes the use of data mining methods in an e-learning system for providing feedback to courseware authors. The discovered information is presented in the form of prediction rules since these are highly comprehensible and they show important relationships among the presented data. The rules will be used to improve courseware, particularly Adaptive Systems for Web-based Education (ASWE). We propose to use evolutionary algorithms as the rule discovery methods, concretely Grammar-Based Genetic Programming (GBGP) with multi-objective optimization techniques. We have developed a specific tool named EPRules (Education Prediction Rules) to facilitate and simplify the knowledge discovery process for usage data in web-based education systems. Keywords: web usage mining, rule discovery, web-based adaptive education, evolutionary algorithms, grammar-based genetic programming. 1 Introduction The application of knowledge discovery techniques and data mining in webbased education systems is a very novel and promising research area [20]. The same idea has been successfully used for a long time in e-commerce systems [15]. But whereas the e-commerce objective is to guide clients in making purchase decisions, the e-learning objective is to guide students in learning. Currently there are a lot of tools, both commercial and freeware, to carry out data mining tasks, and mainly rule discovery. Among all those, DBMiner [21] and Weka [19] stand out because they are very popular public domain systems and they have an integrated graphic environment that lets them carry out almost all data mining tasks. The main inconvenience is that those tools are very difficult to

Keywords

web usage mining, rule discovery, web-based adaptive education, evolutionary algorithms, grammar-based genetic programming.