Academic Decision Support System for Choosing Information Systems Sub Majors Programs using Decision Tree Algorithm

Cut Fiarni, Evasaria M. Sipayung, Prischilia B.T. Tumundo

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Background: Educational data mining is an emerging trend, especially in today Big Data Era. Numerous method and technique already been implemented in order  to improve its process to gain better understanding of the educational process and to extract knowledge from various related data, but the implementation of these methods into Decision support system (DSS) application still limited, especially regarding help to choose university sub majors .

Objective: To design an academic decision support system (DSS) by adopting Theory of Reasoned Action (TRA) concept and using Data Mining as a factor analytic apporach to extract rules for its knowledge model.

Methods: We implemented factor analysis method and decision tree method  of C.45 to produce rules of the impact course of the sub- majors and the job interest as the basic rules of the DSS.

Results: The proposed academic decision support system able to give sub majors recommendations in accordance with student interest and competence, with 79.03% of precision and 61.11% of recall. Moreover, the system also has a dashboard feature that shows the information about the statistic of students in each sub majors.

Conclusion: C.45 algorithm and factor analysis are suitable to build a knowledge model for Academic Decision Support System for Choosing Information System Sub Majors Bachelor Programs. This system could also help the academic adviser on monitoring and make decision accordance with that academic information



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Gorgone J.T et al, “Model Curriculum and Guidlines for Undergraduate Degree Programs in Information Systems”, ACM, AIS and AITP, 2002.

Deniz D.Z. and Ibrahim E., “Using An Academic DSS Student, Course, and Program Assesment”, International Conference on Engineering Education, 6B8-12, 2001.

Barnes, T., Desmarais, M., Romero, C., Ventura, S., “Educational Data Mining 2009: 2nd International Conference on Educational Data Mining, Proceedings. Cordoba, Spain, 2009.

P. Meena Kumari, SK.Abdul Nabi and Puppal Priyanka, "Educational Data Mining and its role in Educational Field", International Journal of Computer Science and Information Technologies (IJCSIT), Vol. 5 (2) , 2014, 2458-2461.

Romero C., Ventura S., Garcia E., “Data Mining in Course Management Systems: Moodle Case Study and Tutorial”, Computer and Education, Volume 51, Issue 1, pp. 368-384, 2008.

Minaei-Bidgoli B., Kashy D.A., Kortemeyer G. and Punch W. F., “Predicting Student Performance: An Application of Data Mining Methods with The Educational Web-based System LON-CAPA”, Proceeding of ASEE/IEEE frontiers in Education Conference, Boulder, CO:IEEE. 2003.

Osmanbegovic E. and Suljic M., “Data Mining Approach for Predicting Student Performance”, Journal of Economics and Business, Vol 10, Issue 1, 2012.

Kularbphettong K., Tongsiri C., “Mining Educational Data to Support Students Major Selection”, International Journal of Computer, Electrical, Automation, Control and Information Engineering, Volume 8, No 1, pp. 21-23, 2014.

I.E. Livieris, K. Drakopoulou, Th. Kotsilieris, V. Tampakas and P. Pintelas, "DSS-PSP - A Decision Support Software for Evaluating Students’ Performance", in Proceeding of 18th International Conference on Engineering Applications of Neural Networks (EANN 2017), 2017.

A.B.F Mansur and N.Yusof., “The Latent of Student Learning Analytic with K-Mean Clustering for Student Behaviour Classification”, Journal of Information Systems Engineering and Business Inteligence, Vol. 4 No. 2 October 2018.

A. Merceron., “ Educational Data Mining/Learning Analytics:Methods, Task And Current Trends”, Proceeding of DeLFI Workshop 2015

Ajzen I. and Fishbein M., “ Understanding Attitudes and Predicting Social Behavior, Englewood Cliffs, NJ: PrenticeHall, 1980.

Zhang W., “Why IS: Understanding Undergraduate Students Intentions to Choose an Information System Major”, Journal of Information Systems Education, 18(4), pp. 447-458, 2007.

Downey J., McGaughey R. and Roach D., “Attitudes and Influances Toward Choosing a Business Major: The Case of Information Systems”, Journal of Information Technology Education, Volume 10, pp. 232-250, 2011.

Pujari A.K., “Data Mining Techniques”, 2nd Edition, Universities Press (India) Private Limited, Himayatnager, Hyderabad 500029 (A.P.) 20012.

Thompson.B.., Exploratory and cCnfirmatory Factor Analysis: Understanding Concepts and Applications, American Psychological Association (APA), 2004.

A.A..A Rostamy, T.A Bioki, F.B. Takanlau, A.A Rostamy, “Utilizing Data Mining and Factor Analysis for Indentifying Activity Base Costing Cost Drivers in Iranian Bank”, Universal Journal of Accounting and Finance, 2013.

Gaurav L., Agrawal1 L. , G. Hitesh., “Optimization of C4.5 Decision Tree Algorithm for Data Mining Application”, International Journal of Emerging Technology and Advanced Engineering, Volume 3, Issue 3, March 2013.

Holmes, G., Donkin, A. & Witten, I.H., “WEKA: a machine learning workbench”, Hamilton, New Zealand: University of Waikato, Department of Computer Science, 1994

K.Y. H,, ong., P.A. Pavlou., “ Online Labor Markets : An Informal Freelancer Economy “ , A publication of the institute for business and information technology, The IBIT Report, A Publication Of The Institute For Business And Information Technology, Februray 2013.


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