文档介绍：Using ic Algorithms for Data Mining
Optimization in an Educational Web-based System
Behrouz Minaei-Bidgoli1, William F. Punch III 1
1 ic Algorithms Research and Applications Group (GARAGe)
Department puter Science & Engineering
Michigan State University
2340 Engineering Building
East Lansing, MI 48824
{minaeibi, punch}***@
Abstract. This paper presents an approach for classifying students in order to
predict their final grade based on features extracted from logged data in an edu-
cation web-based system. bination of multiple classifiers leads to a sig-
nificant improvement in classification performance. Through weighting the fea-
ture vectors using a ic Algorithm we can optimize the prediction accuracy
and get a marked improvement over raw classification. It further shows that
when the number of features is few; feature weighting is works better than just
feature selection.
1 Statement of problem
Many leading educational institutions are working to establish an online teaching
and learning presence. Several systems with different capabilities and approaches
have been developed to deliver online education in an academic setting. In particular,
Michigan State University (MSU) has pioneered some of these systems to provide an
infrastructure for online instruction. The research presented here was performed on a
part of the latest online educational system developed at MSU, the Learning Online
Network puter-Assisted Personalized Approach (LON-CAPA).
In LON-CAPA1, we are involved with two kinds of large data sets: 1) educational
resources such as web pages, demonstrations, simulations, and individualized prob-
lems designed for use on homework assignments, quizzes, and examinations; and 2)
information about users who create, modify, assess, or use these resources. In other
words, we have two ever-growing pools of data.
We have been studying data mining me