|
Author(s): B. Minaei-Bidgoli, P. Tan, G. Kortemeyer &W.F. Punch
Abstract:
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
|
|
Download
the Full Article
Price: US$
50.00
You can purchase the full text version of this article in Adobe PDF format for the above price. Please click the 'Buy Paper' icon below to purchase this paper.
 Send
this page to a colleague.
|
Home
Follow WIT Press
WIT Press on Twitter
Wessex Institute of Technology on Blogspot
