Predicting student performance: an application of data mining methods with an educational web-based system

2004 ◽  
Author(s):  
B. Minaei-Bidgoli ◽  
D.A. Kashy ◽  
G. Kortemeyer ◽  
W.F. Punch
Keyword(s):  
Data Mining ◽  
Student Performance ◽  
Web Based ◽  
Mining Methods ◽  
Predicting Student Performance ◽  
Web Based System

scholarly journals Predicting Student Performance by Using Data Mining Methods for Classification

2013 ◽  
Vol 13 (1) ◽  
pp. 61-72 ◽  
Author(s):  
Dorina Kabakchieva
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Student Performance ◽  
High Potential ◽  
University Management ◽  
Mining Methods ◽  
Predicting Student Performance ◽  
Using Data ◽  
Initial Results ◽  
Support Decision Making

Abstract Data mining methods are often implemented at advanced universities today for analyzing available data and extracting information and knowledge to support decision-making. This paper presents the initial results from a data mining research project implemented at a Bulgarian university, aimed at revealing the high potential of data mining applications for university management.

Predicting College Students English Performance Using Education Data Mining

2017 ◽  
Vol 14 (1) ◽  
pp. 225-229 ◽  
Author(s):  
Ma Lu
Keyword(s):  
Data Mining ◽  
Prediction Accuracy ◽  
Approaches To Learning ◽  
Educational Technologies ◽  
Web Based ◽  
Final Grade ◽  
College English ◽  
Education Data ◽  
Mining Methods ◽  
Web Based System

Currently there is an increasing interest in Education Data Mining (EDM). Newly developed web-based educational technologies offer researchers unique opportunities to predict students’ academic performance. Web-based systems can routinely collect vast quantities of data on user patterns, and data mining methods can be applied to these databases. Data mining results can show how students learn and what approaches to learning lead to success. In this paper, an approach to classifying students in order to predict their final grade based on features extracted from logged data in an education web-based system is presented. We design, implement, and evaluate a series of pattern classifiers and compare their performance on an online college English course dataset. Furthermore, by learning an appropriate weighting of the features used via a genetic algorithm (GA), we further improve prediction accuracy.

CURRENT RESEARCH TREND: INFORMATION TECHNOLOGY AND DECISION MAKING IN 2008

2009 ◽  
Vol 08 (01) ◽  
pp. 1-5 ◽  
Author(s):  
YONG SHI
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Decision Analysis ◽  
Credit Scoring ◽  
Advisory Board ◽  
Research Trend ◽  
Web Based ◽  
Mining Methods ◽  
New Research

On behalf of the editorial advisory board of the International Journal of Information Technology and Decision Making (IT&DM), the Editor-in-Chief reviews the current research trend of this journal based on all the papers published in 2008. They are web-based decision analysis, credit scoring techniques and new data mining methods which combine both decision-making techniques and information technology tools. In addition, the Editor-in-Chief summarizes the key ideas of contributions in this new issue that may contain new research trend of IT&DM in 2009.

Sign in / Sign up

Export Citation Format

Share Document