20.500.12556/ReVIS-767 Student data mining solution - knowledge management system related to higher education institutions 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. data mining knowledge management system student's success rate data mining for small data set higher education institutions educational data mining podatkovno rudarjenje upravljanje znanja uspešnost študentov visokošolsko izobraževanje true true true Angleški jezik Ni določen Znanstveno delo 2015-04-08 12:47:37 2016-02-19 09:30:31 2024-06-13 03:06:46 0000-00-00 00:00:00 2014 0 0 str. 6400-6407 iss. 14 Vol. 41 15 Oct. 2014 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.8:378 0957-4174 171291 12870561 10.1016/j.eswa.2014.04.024 http://www.sciencedirect.com/science/article/pii/S0957417414002462 1 https://revis.openscience.si/Dokument.php?lang=slv&id=809 RAZ_Natek_Srecko_i2014.pdf RAZ_Natek_Srecko_i2014.pdf 1 9C3BF7CB59174B70B03A3711302D5411 7c6b108c274bdefdcc8de5c95eeb56779adf2299f40c9ece6a7a5de1e76abae8 8cd6c24b-2896-11ef-9c9e-001a4af901a5 https://revis.openscience.si/Dokument.php?lang=slv&id=810 Mednarodna fakulteta za družbene in poslovne študije 0 0 0