| Title: | Student data mining solution - knowledge management system related to higher education institutions |
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| Authors: | ID Natek, Srečko (Author)
ID Zwilling, Moti (Author) |
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| Files: |  RAZ_Natek_Srecko_i2014.pdf (787,93 KB)
MD5: 9C3BF7CB59174B70B03A3711302D5411
 http://www.sciencedirect.com/science/article/pii/S0957417414002462
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | MFDPŠ - International School for Social and Business Studies
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| 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. |
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| Keywords: | data mining, knowledge management system, student's success rate, data mining for small data set, higher education institutions, educational data mining |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Year of publishing: | 2014 |
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| Number of pages: | str. 6400-6407 |
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| Numbering: | Vol. 41, iss. 14 |
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| PID: | 20.500.12556/ReVIS-767  |
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| COBISS.SI-ID: | 12870561 |
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| UDC: | 004.8:378 |
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| ISSN on article: | 0957-4174 |
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| DOI: | 10.1016/j.eswa.2014.04.024 |
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| Publication date in ReVIS: | 19.02.2016 |
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| Views: | 8410 |
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| Downloads: | 329 |
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