Student data mining solution - knowledge management system related to higher education institutions

Srečko Natek (Author), Moti Zwilling (Author)

Abstract

Higher education institutions (HEIs) are often curious whether students will be successful or not during their study. Before or during their courses the academic institutions try to estimate the percentage of successful students. But is it possible to predict the success rate of students enrolled in their courses? Are there any specific student characteristics, which can be associated with the student success rate? Is there any relevant student data available to HEIs on the basis of which they could predict the student success rate? The answers to the above research questions can generally be obtained using data mining tools. Unfortunately, data mining algorithms work best with large data sets, while student data, available to HEIs, related to courses are limited and falls into the category of small data sets. Thus, the study focuses on data mining for small student data sets and aims to answer the above research questions by comparing two different data mining tools. The conclusions of this study are very promising and will encourage HEIs to incorporate data mining tools as an important part of their higher education knowledge management systems.

Keywords

podatkovno rudarjenje;upravljanje znanja;uspešnost študentov;visokošolsko izobraževanje;data mining;knowledge management system;student's success rate;data mining for small data set;higher education institutions;educational data mining;

Data

Language:	English
Year of publishing:	2014
Typology:	1.01 - Original Scientific Article
Organization:	MFDPŠ - International School for social and business studies
UDC:	004.8:378
COBISS:	12870561
ISSN:	0957-4174
Views:	7942
Downloads:	329
Average score:	0 (0 votes)
Metadata:

Other data

Secondary keywords:	podatkovno rudarjenje;upravljanje znanja;uspešnost študentov;visokošolsko izobraževanje;
Type (COBISS):	Scientific work
Pages:	str. 6400-6407
Volume:	ǂVol. ǂ41
Issue:	ǂiss. ǂ14
Chronology:	15 Oct. 2014
DOI:	10.1016/j.eswa.2014.04.024
ID:	9124307