THE USE OF DATA MINING TO MODEL PERSONALIZED LEARNING MANAGEMENT SYSTEM.
- Laguna State Polytechnic University.
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Licensure Examination performance is a growing concern of most of the educational institution because it is one of the determinants of quality education and validates high quality instruction. Educational institution is focused on monitoring and improving the Licensure Examination performance particularly in Teacher Education Institution (TEI). The study intends to offer a possible solution to most TEIs apprehensions regarding LET performance by providing the students of Teacher Education a student support service in the form of a personalized Learning Management System with performance prediction and recommendation capability. This can be developed through drawing data model using several data mining techniques and tools. Previous literature suggested using data mining to classify students, predict student performance, improve student retention, enhanced student achievement and assess complex students? behavior to name a few. This research project will provide the groundwork for the generation of a prediction model an innovative Student Support System with an Integration of Information Technology that would help the students by providing a Learning Management System (LMS) with personalized learning environment, performance prediction and recommendation engine that can be beneficial to board programs. This particular study covers the identification of the appropriate data mining algorithms to be used in modeling the PLMS. After an intensive investigation and literature review conducted, the research the result shows that ID3 and J48 is the best data mining algorithms is student performance prediction. The researcher will be using these algorithms in the development of the PLMS to integrate the prediction function of the prototype.
- K. Pal and S. Pal, ?Analysis and Mining of Educational Data for Predicting the Performance of Students?, (IJECCE) International Journal of Electronics Communication and Computer Engineering, Vol. 4, Issue 5, pp. 1560-1565, ISSN: 2278-4209, 2013.
- AI-Radaideh,Q. A., AI-Shawakfa, E.M., and AI-Najjar, M. I., ?Mining Student Data using Decision Trees?, International Arab Conference on Information Technology(ACIT'2006), Yarmouk University, Jordan, 2006.
- Ayesha, S. Mustafa, T. Sattar, A.R., Khan, M.I., ?Data mining model for higher education system?, Europen Journal of Scientific Research, Vol.43, No.1, pp.24-29, 2010.
- Brijesh Kumar Baradwaj , Saurabh Pal , ?Mining Educational Data to Analyze Students‟ Performance? ,(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No. 6, 2011
- M. Fayyad, G. Piatetsky-Shapiro, P. Smyth and R. Uthurusamy,Advances in Knowledge Discovery and Data Mining.?AAAI/MIT Press, 1996.
- Kaur and A. Chhabra, ?Improved J48 Classification Algorithm for the Prediction of Diabetes,? Int. J. Comput. Appl., vol. 98, no. 22, pp. 13?17, 2014.
- Piatetsky, ?The CRISP-DM process model?, 2014. [Online]. Available: (http://www.kdnuggets.com/2014/10/crisp-dm-top-methodology-analytics-data-mining-data-science projects.html) [Accessed 10-Aug-2015].
- Tarun, B. Gerardo, and B. Tanguilig, Generating Licensure Examination Performance Models Using PART and JRIP Classifiers: A Data Mining Application in Education. International Journal of Computer and Communication Engineering, 3(3), 203-207, 2014.
- Ramaswami and R. Bhaskaran , ?A CHAID Based performance Prediction model in Educational Data Mining?, IJCSI Vol. 7 issue no. 1 January 2010. ISSN 1694-0784.
- Philippine Business for Education ?Let Schools Performance Briefer (2009-2013 LET)?, 2014.
- Philippine Business for Education ?Manifesto for Teacher Quality?, 2015.
- Chapman, J. Clinton, R. Kerber, T. Khabaza, T. Reinartz, C. Shearer and R. Wirth :CRISP-DM 1.0: Step-by-step data mining guide. SPSS Inc., 2000.
- A. AI-Radaideh, E. W. AI-Shawakfa, and M. I. AI-Najjar, ?Mining student data using decision trees?, International Arab Conference on Information Technology(ACIT'2006), Yarmouk University, Jordan, 2006.
- Baker and K. Yacef,.? The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3-17, 2009.
- Quinlan, ?Induction of Decision Trees?, Kluwer Academic Publishers-Plenum Publishers. Print ISSN 0885-6125, Online ISSN 1573-0565, Kluwer Academic Publishers-Plenum Publishers, Volume 1,Issue?1,?pp 81?106 March 1986.
- Syed TahirHijazi and S.M.M. RazaNaqvi, ?Factors Affecting Students? Performance: A Case of Private Colleges?. Bangladesh e-Journal of Sociology: Volume 3, Number 1, January 2006).
- K. Yadav, B.K. Bharadwaj and S. Pal, ?Mining Educational Data to Predict Student?s Retention: A Comparative Study?, International Journal of Computer Science and Information Security (IJCSIS), Vol. 10, No. 2, 2012.
- Vasani and R. Gawali, ?Classification and performance evaluation using data mining algorithms?, International Journal of Innovative Research in Science, Engineering and Technology ,(An ISO 3297: 2007 Certified Organization), Vol. 3, Issue 3, March 2014.
- J. Kovacic, ?Early prediction of student success: Mining student enrollment data?, Proceedings of Informing Science & IT Education Conference, 2010.
[Mia Villar Villarica. (2019); THE USE OF DATA MINING TO MODEL PERSONALIZED LEARNING MANAGEMENT SYSTEM. Int. J. of Adv. Res. 7 (Mar). 1191-1200] (ISSN 2320-5407). www.journalijar.com
Laguna State Polytechnic University, Sta. Cruz Campus