A web-based intelligent report e-learning system using data mining techniques

This paper presents a PDCA (Plan, Do, Check, Act) method of improving web-based intelligent reports of an e-learning system as intelligent system, which was created and implemented at the Technical Faculty in Cacak, University of Kragujevac. The focus is on improving LMSs (Learning Management Systems) or e-learning systems by predicting behavior patterns of students and adjusting the structure of these electronic courses. An existing learning management system is improved by using data mining techniques and increasing the efficiency of the courses using custom modules. This study presents the design, implementation, and evaluation of the system. Future work should relate to the continued improvement of the PDCA-created system, as well as the introduction of additional modules and a comparative analysis of the presented and future results.

[1]  Zygmunt Kucharczyk,et al.  Assuring quality of an e-learning project through the PDCA approach , 2011 .

[2]  Khaled M. Hammouda,et al.  Data Mining in E-Learning , 2007 .

[3]  K. McCusker,et al.  Engineering Education Island: Teaching Engineering in Virtual Worlds , 2009 .

[4]  Javier Torrente,et al.  Enhancing moodle to support problem based learning. The Nucleo experience , 2011, 2011 IEEE Global Engineering Education Conference (EDUCON).

[5]  Azwa Abdul Aziz,et al.  INTELLIGENT SYSTEM FOR PERSONALIZING STUDENTS' ACADEMICBEHAVIORS- A CONCEPTUAL FRAMEWORK , 2012 .

[6]  William F. Punch,et al.  Using Genetic Algorithms for Data Mining Optimization in an Educational Web-Based System , 2003, GECCO.

[7]  Amr Badr,et al.  An Auto-Recommender Based Intelligent E-Learning System , 2011 .

[8]  Elena Álvarez,et al.  Towards Virtual Course Evaluation Using Web Intelligence , 2007, EUROCAST.

[9]  Nikolaos Valkanos,et al.  A Collaborative Approach for the Development of Networked Learning Environments Using the ADDURI Framework , 2007 .

[10]  Arvin Agah,et al.  Human interactions with intelligent systems: research taxonomy , 2000, Comput. Electr. Eng..

[11]  Ms. Ishtake " Intelligent Heart Disease Prediction System Using Data Mining Techniques " , .

[12]  Victor,et al.  E- Learning: An effective pedagogical tool for learning , 2011, ArXiv.

[13]  Takeshi Tanigawa Construction of Driving Model with Faculty Perspectives of ePortfolio for Improving University Education in Japan , 2010 .

[14]  W. Edwards Deming,et al.  Out of the Crisis , 1982 .

[15]  Saurabh Pal,et al.  Mining Educational Data to Analyze Students' Performance , 2012, ArXiv.

[16]  Toshio Okamoto,et al.  N 3 : NN Navigation Support System—Knowledge-Navigation in Hyperspace: The Sub-Symbolic Approach , 2001 .

[17]  S. Prakasam,et al.  An agent -based Intelligent System to enhance E-Learning through Mining Techniques , 2010 .

[18]  Àngela Nebot,et al.  Applying Data Mining Techniques to e-Learning Problems , 2007 .

[19]  Ramiro Jordan,et al.  A new computer system for education , 1991 .

[20]  C.J.H. Mann,et al.  Handbook of Data Mining and Knowledge Discovery , 2004 .

[21]  Youngjun Lee,et al.  A review of online course dropout research: implications for practice and future research , 2011 .

[22]  Silvia Sanz-Santamaría,et al.  Quality techniques applied in the improvement of item-based LMS , 2005 .

[23]  Herman Neuckermans,et al.  Content Management Systems versus Learning Environments , 2009 .

[24]  Marta E. Zorrilla,et al.  A decision support system to improve e-learning environments , 2010, EDBT '10.

[25]  Osmar R. Zaïane,et al.  Web Usage Mining for a Better Web-Based Learning Environment , 2001 .