Data Mining in Educational Databases

Contents:

Data mining is a process of identifying and extracting hidden patterns, knowledge and information from databases and data warehouses. Today, one of the biggest challenges that educational institutes face is to use educational data to improve the quality of managerial decisions. Due to the huge educational databases, data mining enables organizations to use their current reporting capabilities to uncover and understand hidden patterns in vast datasets. Furthermore, data mining techniques can be used to extract meaningful knowledge and useful information from the huge educational databases. Data mining is a powerful analytical tool that enables educational institutions to better allocate resources and staff, and improve the effectiveness of alumni development.

Seminarthemen:

Topics of this seminar are

• Data mining in higher education
• Importance of data mining in higher education
• Data mining and its application in higher education
• Educational data mining
• Data mining in course management
• Predicting student performance by data mining techniques

Literatur:

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• Baradwaj, B. K., & Pal, S. (2011). Mining Educational Data to Analyze Students' Performance. International Journal of Advanced Computer Science & Applications, 2(6).
• Bhardwaj, B. K., & Pal, S. (2012). Data Mining: A prediction for performance improvement using classification. arXiv preprint arXiv:1201.3418.
• Bhise, R. B., Thorat, S. S., & Supekar, A. K. (2013). Importance of data mining in higher education system. IOSR Journal Of Humanities And Social Science (IOSR-JHSS) ISSN, 2279-0837.
• Erdoğan, Ş. Z., & Tımor, M. (2005). A DATA MINING APPLICATION IN A STUDENT DATABASE. Journal of Aeronautics & Space Technologies/Havacilik ve Uzay Teknolojileri Dergisi, 2(2).
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• Kumar, V., & Chadha, A. (2011). An Empirical Study of the Applications of Data Mining Techniques in Higher Education. Internat. Journal of Advanced Computer Science and Applications, 2(3), 80-84.
• Merceron, A., & Yacef, K. (2005, May). Educational Data Mining: a Case Study. In AIED (pp. 467-474).