Analysis and Identification of Data Heterogeneity on Learning Environment Using Ontology Knowledge

Arda Yunianta, Norazah Yusof, Abdul Aziz, Nataniel Dengen, Mohd Shahizan Othman

Abstract


Heterogeneity on learning environment is about different data and applications to support a learning process in education institutions. Distributed and various systems on learning environment is the current issues to produce big and heterogeneity data problem. A lot of relationships are formed between elements on learning environment. The element on learning environment consists of learning data, learning applications, data sources, learning concept, and data heterogeneity aspect on learning environment. These elements are interrelated and produce complex relationship between each other. A complex relationship problem between elements on learning environment makes a process of analysis and identification difficult to be done. Existing method to drawing this heterogeneity problem make confuse and misunderstanding readers. To solved this problem, researcher using ontology knowledge to describe and draw a semantic relationship that represent the complexity of data relationship on learning environment. The result of this analysis is to develop ontology knowledge to solve complexity relationship on learning environment, and also to help reader’s better understanding the complex relationship between elements on learning environment.

Keywords


learning environment; data heterogeneity; ontology knowledge; semantic approach

References


Shyamala, R., R. Sunitha, and G. Aghila, Towards Learner Model Sharing Among Heterogeneous E-Learning Environments. International Journal of Engineering Science and Technology (IJEST), 2011. 3(4): p. 2034-2040.

Xiaofei, L., A.E. Saddik, and N.D. Georganas. An implementable architecture of an e-learning system. in Electrical and Computer Engineering, 2003. IEEE CCECE 2003. Canadian Conference on. 2003.

Dietinger, T., ASPECTS OF E-LEARNING ENVIRONMENTS, in Institute for Information Processing and Computer Supported New Media (IICM)2003, Graz University of Technology: Austria.

Yunianta, A., et al., Ontology Development to Handle Semantic Relationship between Moodle E-Learning and Question Bank System, in International Conference on Soft Computing and Data Mining2014: Johor, Malaysia.

Huang, O.-r., et al., Application of Ontology-based Automatic ETL in Marine Data Integration, in Symposium on Electrical & Electronics Engineering (EEESYM)2012, IEEE

Wang, C.-C., W.-C. Pai, and N.Y. Yen, A Sharable e-Learning Platform Based on Cloud Computing, in Computer Research and Development (ICCRD), 2011 3rd International Conference on2011. p. 1-5.

Gudanescu, N., Using modern technology for improving learning process at different educational levels. Procedia - Social and Behavioral Sciences, 2010. 2(2): p. 5641-5645.

Biggs, J., Enhancing teaching through constructive alignment. Higher Education, 1996. 32(3): p. 347-364.

Cain, A. and C.J. Woodward. Toward constructive alignment with portfolio assessment for introductory programming. in Teaching, Assessment and Learning for Engineering (TALE), 2012 IEEE International Conference on. 2012.

Nkambou, R., J. Bourdeau, and R. Mizoguchi, Introduction: What Are Intelligent Tutoring Systems, and Why This Book?, in Advances in Intelligent Tutoring Systems, R. Nkambou, J. Bourdeau, and R. Mizoguchi, Editors. 2010, Springer Berlin Heidelberg. p. 1-12.

Vanfretti, L. and M. Farrokhabadi. Implementing constructive alignment theory in a power system analysis course using a consensus model. in e-

Learning in Industrial Electronics (ICELIE), 2012 6th IEEE International Conference on. 2012.

J. Biggs, and C. Tang. Teaching for Quality Learning at University. Berkshire, England: Open University Press, 2007.

J. Biggs. Aligning Teaching for Constructing Learning. The Higher education Academy. 2004.

You, D., et al., Flexible Collaborative Learning Model in E-Learning with Personalized Teaching Materials, in Advances in Computer Science, Intelligent System and Environment, D. Jin and S. Lin, Editors. 2011, Springer Berlin Heidelberg. p. 127-131.

v kambou, R., Modeling the Domain: An Introduction to the Expert Module, in Advances in Intelligent Tutoring Systems, R. Nkambou, J. Bourdeau, and R. Mizoguchi, Editors. 2010, Springer Berlin Heidelberg. p. 15-32.Nkambou, R., Modeling the Domain: An Introduction to the Expert Module, in Advances in Intelligent Tutoring Systems, R. Nkambou, J. Bourdeau, and R. Mizoguchi, Editors. 2010, Springer Berlin Heidelberg. p. 15-32.


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