Artificial Intelligent Systems and Machine Learning

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Adaptive Education System Analysis Using Machine Learning Techniques


Affiliations
1 Department of Computer Science, Rathinam College of Arts and Science (Autonomous), Coimbatore, India
     

   Subscribe/Renew Journal


Educational Data Mining is the field of study concerned with mining educational data to find out interesting patterns and knowledge in educational organizations to analyse and study educational data for student’s improvement. This study explores multiple factors theoretically assumed to affect students’ performance in Machine Learning education, and finds a qualitative model which best classifies and predicts the students’ performance based on related personal and social factors. A student data from a community college database has been taken and various classification approaches have been performed and a comparative analysis has been done.


Keywords

Education, Students, Machine Learning, Data Mining.
User
Subscription Login to verify subscription
Notifications
Font Size

  • . Ally, M. (2004). Theory and Practice of Online Learning, chapter Foundations of Educational Theory for Online Learning, pages 3–32.

  • . Anderson, C. R. (2002). A Machine Learning Approach to Web Personalization. PhD thesis, University of Washington.

  • . Anderson, Terry (2004). Theory and Practice of Online Learning, chapter Toward a Theory of Online Learning, pages 33–60.

  • . ATutor (2010). ATutor Learning Content Management System. http://www.atutor.ca.

  • . Baker, R. S. (2010). International Encyclopedia of Education, chapter Data Mining for Education. Elsevier, 3 edition.

  • . Baker, R. S. and Yacef, K. (2009). The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining, 1(1).

  • . Blackboard Inc. (2010). Blackboard International. http://www.blackboard.com.

  • . Brusilovsky, P. (2007). Adaptive Navigation Support. In The Adaptive Web, Lecture Notes in Computer Science, pages 263–290. SpringerVerlag Berlin Heidelberg.

  • . Brusilovsky, P. and Maybury, M. T. (2002). From Adaptive Hypermedia to the Adaptive Web. Communications of the ACM, 45(5).

  • . Corral, G., Armengol, E., Fornells, A., and Golobardes, E. (2007). Data Security Analysis Using Unsupervised Learning and Explanations. In Innovations in Hybrid Intelligent Systems, Advances in Soft Computing, pages 112–119. Springer-Verlag Berlin Heidelberg.

  • . Das, A. S., Datar, M., Garg, A., and Rajaram, S. (2007). Google News Personalization: Scalable Online Collaborative Filtering. In Proceedings of the 16th International Conference on World Wide Web, pages 271– 280.

  • . Divotkey, R., Mu¨hlbacher, J., Reisinger, S., and Remplbauer, D. (2002). The WeLearn Distance Teaching Framework. In Open and Distance Learning in Europe and Beyond Rethinking International Co-operation; Proceedings of the 2002 EDEN Conference, pages 308–314.

  • . Fahy, P. J. (2004). Theory and Practice of Online Learning, chapter Media Characteristics and Online Learning Technology, pages 137–174.

  • . Greer, J., & Brooks, C. (2006). What to Adapt, and How? Personal communication, May 16, 2006.

  • . Heisele, B. (2003). Visual Object Recognition with Supervised Learning. IEEE Intelligent Systems, 18(3).

  • . Zancanaro, M., Kuflik, T., Boger, Z., Goren-Bar, D., and Goldwasser, D. (2007). Analyzing Museum Visitor’s Behavior Patterns. In User Modeling 2007, Lecture Notes in Computer Science, pages 238–246.

  • Springer-Verlag Berlin Heidelberg.

  • . Sonali Agarwal, G. N. Pandey, and M. D. Tiwari: Data Mining in Education: Data Classification and Decision Tree Approach, International Journal of e-Education, e-Business, e-Management and e-Learning, Vol. 2, No. 2, April 2012

  • . Amjad Abu Saa : Educational Data Mining & Students Performance Prediction, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 5, 2016

  • . Golriz Amooee, Behrouz Minaei-Bidgoli, Malihe Bagheri-Dehnavi : A Comparison Between Data Mining Prediction Algorithms for Fault Detection, IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 6, No 3, November 2011, ISSN (Online): 1694-0814

  • . R.Muralidharan, A.Uthiramoorthy : Object Recognition from an Image through Features Extracted from Segmented Image, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 12, December 2014

  • . J.Thirumaran, A.Uthiramoorthy :E-LEARNING CONTENT DEVELOPMENT A ROLE IN VIRTUAL CLASS ROOM, Scholarly Research journal for Humanity science & English Language, Oct-Nov 2015, Vol 2/12, Page - 2977-2184


Abstract Views: 236

PDF Views: 1




  • Adaptive Education System Analysis Using Machine Learning Techniques

Abstract Views: 236  |  PDF Views: 1

Authors

A. Uthiramoorthy
Department of Computer Science, Rathinam College of Arts and Science (Autonomous), Coimbatore, India
R. Muralidharan
Department of Computer Science, Rathinam College of Arts and Science (Autonomous), Coimbatore, India

Abstract


Educational Data Mining is the field of study concerned with mining educational data to find out interesting patterns and knowledge in educational organizations to analyse and study educational data for student’s improvement. This study explores multiple factors theoretically assumed to affect students’ performance in Machine Learning education, and finds a qualitative model which best classifies and predicts the students’ performance based on related personal and social factors. A student data from a community college database has been taken and various classification approaches have been performed and a comparative analysis has been done.


Keywords


Education, Students, Machine Learning, Data Mining.

References