Success Prediction System for Student Counseling using Data Mining

Jörg Frochte, Irina Bernst

Abstract

A framework how to use data mining of central exam data for the prediction of student success in bachelor degree courses is presented. For the prediction a supervised learning approach is used based on successful and unsuccessful student biographies. We develop a traffic light rating system and present results for two different kinds of bachelor degree courses; one in economics and one in engineering. We discuss applications for students and student counseling institutions as well as the limitations dealing with information privacy aspects, especially under the conditions regarding data mining in Germany.

References

  1. Baker, R. S. and Yacef, K. (2009). The state of educational data mining in 2009. JEDM-Journal of Educational Data Mining, 1(1):3-17.
  2. Erdel, B. (2010). Welche Determinanten beeinflussen den Studienerfolg? Technical report, FriedrichAlexander-Universität Erlangen-Nürnberg - School of Business and Economics.
  3. Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3):37.
  4. Jirjahn, U. (2007). Welche Faktoren beeinflussen den Erfolg im wirtschaftswissenschaftlichen Studium. Schmalenbachs Zeitschrift für betriebswirtschaftliche Forschung, 59(3):286-313.
  5. Jishan, S. T., Rashu, R. I., Haque, N., and Rahman, R. M. (2015). Improving accuracy of students' final grade prediction model using optimal equal width binning and synthetic minority over-sampling technique. Decision Analytics, 2(1):1-25.
  6. Kovac?ic, Z. J. (2010). Early prediction of student success: mining students enrolment data. In Proceedings of Informing Science & IT Education Conference (InSITE), pages 647-665. Citeseer.
  7. Law of the FRG (2016). Hochschulstatistikgesetz (hstatg). http://www.gesetze-im-internet.de/hstatg 1990/ BJNR024140990.html.
  8. Mosler, K. and Savine, A. (2004). Studienaufbau und Studienerfolg von Kölner Volks-und Betriebswirten im Grundstudium. Technical report, Discussion papers in statistics and econometrics.
  9. OECD (2010). PISA 2009 Results: Overcoming Social Background. OECD Publishing.
  10. Osmanbegovic, E. and Suljic, M. (2012). Data mining approach for predicting student performance. Economic Review Journal of Economics and Business, 10(1).
  11. Romero, C. and Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert systems with applications, 33(1):135-146.
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Paper Citation


in Harvard Style

Frochte J. and Bernst I. (2016). Success Prediction System for Student Counseling using Data Mining . In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016) ISBN 978-989-758-203-5, pages 181-188. DOI: 10.5220/0006036401810188


in Bibtex Style

@conference{kdir16,
author={Jörg Frochte and Irina Bernst},
title={Success Prediction System for Student Counseling using Data Mining},
booktitle={Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)},
year={2016},
pages={181-188},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006036401810188},
isbn={978-989-758-203-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)
TI - Success Prediction System for Student Counseling using Data Mining
SN - 978-989-758-203-5
AU - Frochte J.
AU - Bernst I.
PY - 2016
SP - 181
EP - 188
DO - 10.5220/0006036401810188