IJSTR

International Journal of Scientific & Technology Research

IJSTR@Facebook IJSTR@Twitter IJSTR@Linkedin
Home About Us Scope Editorial Board Blog/Latest News Contact Us
CALL FOR PAPERS
AUTHORS
DOWNLOADS
CONTACT
QR CODE
IJSTR-QR Code

IJSTR >> Volume 4 - Issue 4, April 2015 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Application Of Data Mining Techniques For Student Success And Failure Prediction (The Case Of Debre_Markos University)

[Full Text]

 

AUTHOR(S)

Muluken Alemu Yehuala

 

KEYWORDS

Index Terms: Data mining, failure, performance, predict, student, success, universities

 

ABSTRACT

Abstract: This research work has investigated the potential applicability of data mining technology to predict student success and failure cases on University studentsí datasets. CRISP-DM (Cross Industry Standard Process for Data mining) is a data mining methodology to be used by the research. Classification and prediction data mining functionalities are used to extract hidden patterns from studentsí data. These patterns can be seen in relation to different variables in the studentsí records. The classification rule generation process is based on the decision tree and Bayes as a classification technique and the generated rules were studied and evaluated. Data collected from MS_EXCEL files, and it has been preprocessed for model building. Models were built and tested by using a sample dataset of 11,873 regular undergraduate students. Analysis is done by using WEKA 3.7 application software. The research results offer a helpful and constructive recommendations to the academic planners in universities of learning to enhance their decision making process. This will also aid in the curriculum structure and modification in order to improve studentsí academic performance. Students able to decide about their field of study before they are enrolled in specific field of study based on the previous experience taken from the research-findings. The research findings indicated that EHEECE (Ethiopian Higher Education Entrance Certificate Examination) result, Sex, Number of students in a class, number of courses given in a semester, and field of study are the major factors affecting the student performances. So, on the bases of the research findings the level of student success will increase and it is possible to prevent educational institutions from serious financial strains.

 

REFERENCES

[1] Berry, M.,& Linoff, S. (2000). Mastering Data Mining: The Art and Science of Customer Relationship Management. New York: Wiley.

[2] Cross Industry Standard Process for Data Mining (CRISP-DM). August 29, 2004, Available at: www.crisp-dm.org

[3] Dekker, G., Pechenizkiy, M., and Vleeshouwers, J. (2009). Predicting students drop out: A case study. Proceedings of the 2nd International Conference on Educational Data Mining, EDM'09, 41-50.

[4] Dr.Vuda, S. & Capt. Genetu, Y. (2012). Improving Academic Performance of Students of Defence University Based on Data warehousing and Data mining. August 08/2004, Available at: http://www.dmu.edu.et

[5] Han,J. & Kamber, M. (2006). Data Mining: Concepts and Techniques( 2nd edition).

[6] Thai, N. (2007). A Comparative Analysis Of Techniques For Predicting Academic Performance. In Proceedings of 37th conf. on ASEE/IEE Frontiers in Education.

[7] Vandamme, J.P., Meskens, N., & Superby, J.F. (2007). Predicting Academic Performance by Data Mining Methods.