Edifice an Educational Framework using Educational Data Mining and Visual Analytics
Автор: S Anupama Kumar
Журнал: International Journal of Education and Management Engineering(IJEME) @ijeme
Статья в выпуске: 2 vol.6, 2016 года.
Бесплатный доступ
Educational Data Mining and Visual analytics are two emerging trends in the industry that plays a major role in bringing out changes in the educational institutions. This paper discusses about building an educational framework that suits the higher education in India using the above mentioned technologies. Educational data mining comprises of various technologies and tasks which can applied on educational data to bring out useful information. In this research work, a data ware house is built to store the student data, two data mining tasks classification and association rule mining are applied over the student data set to analyse their performance in the examination. Decision tree algorithm is used to predict the course and program outcome. Association mining is used to analyze the outcome and understand technical capability of the students. The algorithms were found very accurate in predicting and analyzing the performance. Visual analytics is used in the framework to depict the analysis of the student's performance.
Education data mining, Classification, Association mining, Visual analytics
Короткий адрес: https://sciup.org/15013859
IDR: 15013859
Список литературы Edifice an Educational Framework using Educational Data Mining and Visual Analytics
- Titus DE Lafayette Winters, A dissertation work submitted on Educational Data Mining: Collection and Analysis of Score Matrices for Outcomes-Based Assessment, 2006.
- Dodge, Y. (2006), "The Oxford Dictionary of Statistical Terms" http://www.amazon.com/The-Oxford-Dictionary-Statistical-Terms.
- Rajnijindal, A Survey of Educational Data mining and Research Trends, International journal of database management System (IJDMS), vol.5, No.3 June 2013.
- Igor, LjiljanaBrkić, Mirta, Improving the ETL process and maintenance of Higher Education Information System Data Warehouse Issue 10, Volume 8, October 2009.
- Zlatko J. Kovačić, Early Prediction of Student Success: Mining Students Enrolment Data, Proceedings of Informing Science & IT Education Conference (In SITE) 2010 pp 647-665.
- J. Ross Quinlan. "C4.5: programs for machine learning", Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1993.
- Minaei-Bidgoli, B., D.A. Kashy, G. Kortemeyer, & W.F. Punch. "Predicting student performance: an application of data mining methods with the educational web-based system LON-CAPA" in Proceedings of ASEE/IEEE Frontiers in Education Conference, Boulder, CO: IEEE (2003).
- Cristóbal Romero and et al, Data Mining Algorithms to Classify Students, Computer Science Department, Córdoba University, Spain.
- E.Chandra and K.Nandhini, "Predicting Student Performance using Classification Techniques", Proceedings of SPIT-IEEE Colloquium and International Conference, Mumbai, India,p.no83-87.
- Merceron, A. & K. Yacef. "A Web-based Tutoring Tool with Mining Facilities to Improve Learning and Teaching" in Proceedings of 11th International Conference on Artificial Intelligence in Education., F. Verdejo and U. Hoppe (Eds), pp 201-208, Sydney: IOS Press (2003).
- Mr. M. N. Quadri and Dr. N.V. Kalyankar, "Drop Out Feature of Student Data for Academic Performance Using Decision Tree Techniques", GJCST Computing Classification H.2.8 & K.3.m, Page|2 Vol. 10 Issue 2 (Ver 1.0), April 2010 Global Journal of Computer Science and Technology.
- M. Ramaswami and R. Bhaskaran, "A CHAID Based Performance Prediction Model in Educational Data Mining", IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 1, No. 1, January 2010.
- Myzatul Akmam Sapaat, Aida Mustapha and et al, "A Data Mining Approach to Construct Graduates Employability Model in Malaysia", International Journal on New Computer Architectures and Their Applications (IJNCAA) 1(4): 1086-1098, The Society of Digital Information and Wireless Communications, 2011 (ISSN: 2220-9085).
- Zlatko J. Kovačić, John Steven Green, "Predictive working tool for early identification of 'at risk' students", Published under Creative Commons 3.0 New Zealand Attribution Non-commercial Share Alike Licence (BY-NC-SA) Licensed copy.
- "Assessing and Evaluating Student Learning", Atlantic Canada English Language Arts Curriculum: K–3 263, ttp://www.ed.gov.nl.ca/edu/k12/curriculum/guides.
