31 August 2014
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Association analysis for a web-based educational system

Author(s): B. Minaei-Bidgoli, P. Tan, G. Kortemeyer &W.F. Punch

An important goal of data mining is to discover the unobvious relationships among the objects in a data set.

Web-based educational technologies allow educators to study how students learn (descriptive studies) and which learning strategies are most effective (causal/predictive studies).

Since web-based educational systems collect vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of students, assessments, and the solution strategies adopted by students.

This research focuses on the discovery of interesting contrast rules, which are sets of conjunctive rules describing interesting characteristics of different segments of a population. In the context of web-based educational systems, contrast rules help to identify attributes characterizing patterns of performance disparity between various groups of students.We propose a general formulation of contrast rules as well as a framework for finding such patterns.

Our research provides a new algorithm for mining contrasting rules that can improve web-based educational systems for both teachers and students allowing for greater learner improvement and more effective evaluation of the learning process.

We apply this technique to an online educational system developed at Michigan State University called LON-CAPA.Alarger advantage of developing this approach is its wide application in any other data mining application. ...

Pages: 18
Size: 255 kb
Paper DOI: 10.2495/1-84564-152-3/08



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This paper can be found in the following book

Data Mining in E-Learning

Data Mining in E-Learning

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