Home  | Impressum | Sitemap | KIT

Educational Data Mining

Educational Data Mining
Typ: Seminar
Lehrstuhl: Lehrstuhl für Ökonometrie und Statistik
Semester: für Bachelor-Studenten
Ort:

Geb. 20.54, Raum 101 (Statistik)

Zeit:

Di, 04.11.2014, 13:00 Uhr

Dozent:

Mirashrafi

LVNr.: 2521030
Prüfung:

Vortrag, schriftliche Ausarbeitung

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

Literatur:

Aher, S.B. & Lobo, L.M.R.J. (2011). Data Mining in Educational System using WEKA. International Conference on Emerging Technology Trends.
Ahmed, A. B. E. D., & Elaraby, I. S. (2014). Data Mining: A prediction for Student's Performance Using Classification Method. World Journal of Computer Application and Technology, 2(2), 43-47.
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).
Han, J., Kamber, M., & Pei, J. (2011). Data mining: concepts and techniques, 3rd ed. Morgan Kaufmann.
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).
Minaei-Bidgoli, B., Kashy, D. A., Kortmeyer, G., & Punch, W. F. (2003, November). Predicting student performance: an application of data mining methods with an educational web-based system. In Frontiers in Education, 2003. FIE 2003 33rd Annual (Vol. 1, pp. T2A-13). IEEE.
Parack, S., Zahid, Z., & Merchant, F. (2012, January). Application of data mining in educational databases for predicting academic trends and patterns. In Technology Enhanced Education (ICTEE), 2012 IEEE International Conference on (pp. 1-4). IEEE.
Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), 135-146.
Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. Systems, Man, and Cybernetics, Part C Applications and Reviews, IEEE Transactions on, 40(6), 601-618.
Romero, C., Ventura, S., & García, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 51(1), 368-384.

Anmeldung:

To register for this seminar by e-mail is required. Interested persons please contact the seminar leader (seyed.mirashrafi@kit.edu).

Anmeldungsschluss: 31.10.2014