Using Data Mining Technique for Scholarship Disbursement
This work is on decision tree-based classification for
the disbursement of scholarship. Tree-based data mining
classification technique is used in other to determine the generic rule
to be used to disburse the scholarship. The system based on the
defined rules from the tree is able to determine the class (status) to
which an applicant shall belong whether Granted or Not Granted. The
applicants that fall to the class of granted denote a successful
acquirement of scholarship while those in not granted class are
unsuccessful in the scheme. An algorithm that can be used to classify
the applicants based on the rules from tree-based classification was
also developed. The tree-based classification is adopted because of its
efficiency, effectiveness, and easy to comprehend features. The
system was tested with the data of National Information Technology
Development Agency (NITDA) Abuja, a Parastatal of Federal
Ministry of Communication Technology that is mandated to develop
and regulate information technology in Nigeria. The system was
found working according to the specification. It is therefore
recommended for all scholarship disbursement organizations.
[1] L. Chang, “Applying data mining to predict college admissions yield: A
case Study” New Directions for Institutional Research, 2006, pp.53–68.
doi: 10.1002/ir.187.
[2] S. S. Aksenova, D. Zhang, and M. Lu, “Enrollment prediction through
data Mining”, in Information Reuse and Integration, 2006 IEEE
International Conference. Retrieved on 01/13/2009. Available at
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4018543.
[3] J. Luan, and C. Zhao, “Practicing data mining for enrollment
management and beyond”, in J. Luan & C. Zhao (Eds.), New Direction
for Institutional Research, 2006, no.131. San Francisco: Jossey-Bass.
W.-K. Chen, Linear Networks and Systems (Book style). Belmont, CA:
Wadsworth, 1993, pp. 123–135.
[4] P. Eykamp, “Using data mining to explore which students use placement
to reduce time to degree”, in J. Luan and C. Zhao (Eds.), New Direction
for Institutional Research, 2006, no. 131. San Francisco: Jossey-Bass.
[5] M. Goyal and R. Vohra, “Applications of Data Mining in Higher
Education”, in International Journal of Computer Science Issues (IJCSI),
Vol. 9, Issue 2, No 1, March 2012 ISSN (Online): 1694-0814
www.IJCSI.org
[6] T. Silwattananusarn, and K. Tuamsuk, “Data Mining and Its
Applications for Knowledge Management : A Literature Review from
2007 to 2012,.International Journal of Data Mining and Knowledge
Management Process (IJDKP) Vol.2, No.5, September 2012, Pp 13-24.
[1] L. Chang, “Applying data mining to predict college admissions yield: A
case Study” New Directions for Institutional Research, 2006, pp.53–68.
doi: 10.1002/ir.187.
[2] S. S. Aksenova, D. Zhang, and M. Lu, “Enrollment prediction through
data Mining”, in Information Reuse and Integration, 2006 IEEE
International Conference. Retrieved on 01/13/2009. Available at
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4018543.
[3] J. Luan, and C. Zhao, “Practicing data mining for enrollment
management and beyond”, in J. Luan & C. Zhao (Eds.), New Direction
for Institutional Research, 2006, no.131. San Francisco: Jossey-Bass.
W.-K. Chen, Linear Networks and Systems (Book style). Belmont, CA:
Wadsworth, 1993, pp. 123–135.
[4] P. Eykamp, “Using data mining to explore which students use placement
to reduce time to degree”, in J. Luan and C. Zhao (Eds.), New Direction
for Institutional Research, 2006, no. 131. San Francisco: Jossey-Bass.
[5] M. Goyal and R. Vohra, “Applications of Data Mining in Higher
Education”, in International Journal of Computer Science Issues (IJCSI),
Vol. 9, Issue 2, No 1, March 2012 ISSN (Online): 1694-0814
www.IJCSI.org
[6] T. Silwattananusarn, and K. Tuamsuk, “Data Mining and Its
Applications for Knowledge Management : A Literature Review from
2007 to 2012,.International Journal of Data Mining and Knowledge
Management Process (IJDKP) Vol.2, No.5, September 2012, Pp 13-24.
@article{"International Journal of Information, Control and Computer Sciences:70773", author = "J. K. Alhassan and S. A. Lawal", title = "Using Data Mining Technique for Scholarship Disbursement", abstract = "This work is on decision tree-based classification for
the disbursement of scholarship. Tree-based data mining
classification technique is used in other to determine the generic rule
to be used to disburse the scholarship. The system based on the
defined rules from the tree is able to determine the class (status) to
which an applicant shall belong whether Granted or Not Granted. The
applicants that fall to the class of granted denote a successful
acquirement of scholarship while those in not granted class are
unsuccessful in the scheme. An algorithm that can be used to classify
the applicants based on the rules from tree-based classification was
also developed. The tree-based classification is adopted because of its
efficiency, effectiveness, and easy to comprehend features. The
system was tested with the data of National Information Technology
Development Agency (NITDA) Abuja, a Parastatal of Federal
Ministry of Communication Technology that is mandated to develop
and regulate information technology in Nigeria. The system was
found working according to the specification. It is therefore
recommended for all scholarship disbursement organizations.", keywords = "Decision tree, classification, data mining,
scholarship.", volume = "9", number = "7", pages = "1748-4", }