An ensemble approach in converging contents of LMS and KMS

• Published in: Education and information technologies Vol. 22; no. 4; pp. 1673 - 1694
• Main Authors: Sabitha, A. Sai, Mehrotra, Deepti, Bansal, Abhay
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2017; Springer; Springer Nature B.V
• Subjects: Algorithms; Analysis; Classification; Computer Appl. in Social and Behavioral Sciences; Computer Science; Computer System Design; Computers and Education; Courseware; Data mining; Data Processing; Database Design; Database Management Systems; Design Preferences; Education; Educational Technology; Electronic Learning; Information Networks; Information Systems Applications (incl.Internet); Knowledge Management; Learning Management Systems; Management Systems; Metadata; Online instruction; Performance Technology; Usability; User Interfaces and Human Computer Interaction; Germany; Naïve Bayes; Learning objects; Knowledge objects; Classification; Boosting; Decision tree; Bagging
• ISSN: 1360-2357; 1573-7608
• DOI: 10.1007/s10639-016-9516-7

Currently the challenges in e-Learning are converging the learning content from various sources and managing them within e-learning practices. Data mining learning algorithms can be used and the contents can be converged based on the Metadata of the objects. Ensemble methods use multiple learning algorithms and it can be used to converge the Learning Objects from Learning Management Systems (LMS) and Knowledge Objects from Knowledge Management System (KMS). This can increase the performance of the learning system, especially when there is different content available from a variety of models. In this research, Data mining ensemble techniques are used so that an appropriate learning content is delivered to the learner. By converging, the learning content from various sources the Learning system pedagogies can also be revolutionized and a right learning path can be provided to the learners. This research work uses various classification techniques for converging and are evaluated using statistical measures.