DataMining for Small Student Data Set: Knowledge Management System for Higher Education Teachers
Higher education teachers are often curious whether students will be successful or not. Before or during a course they try to estimate the percentage of successful students. But is it possible to predict the success rate of students enrolled in their course? Are there any specific student characteristics, known to a teacher, which can be associated with the student success rate? Is there any relevant student data available to teachers on the basis of which they could predict the student success rate? The answers to the above research questions can generally be obtained with data mining tools. Unfortunately, data mining algorithms work best with large data sets, while student data, available to higher education teachers, is extremely limited and clearly 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 using data and comparative analysis using data mining tools normally available to higher education teachers. The conclusions of this study are very promising and will encourage teachers to incorporate data mining tools as an important part of their higher education knowledge management systems.
|This chapter was published in: Srecko Natek & Moti Zwilling , , pages 1379-1389, 2013.|
|This item is provided by ToKnowPress in its series Active Citizenship by Knowledge Management & Innovation: Proceedings of the Management, Knowledge and Learning International Conference 2013 with number 1379-1389.|
|Contact details of provider:|| Web page: http://www.toknowpress.net/proceedings/978-961-6914-02-4/|
When requesting a correction, please mention this item's handle: RePEc:tkp:mklp13:1379-1389. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Alen Jezovnik)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.