Educatee's Thesaurus As An Object Of 
Measuring Learned Material Of The Distance Learning Course

Year 2013, Volume: 14 Issue: 4, 12 - 25, 01.12.2013

Alexander Aleksandrovich Rybanov

Abstract

Monitoring and control over the process of studying the distance learning course are based on solving the problem of making out an adequate integral mark to the educatee for mastering entire study course, by testing results. It is suggested to use the degree of correspondence between educatee's thesaurus and the study course thesaurus as an integral mark for the degree of mastering the distance learning course. Study course thesaurus is a set of the course objects with relations between them specified. The article considers metrics of the study course thesaurus complexity, made on the basis of the graph theory and the information theory. It is suggested to use the amount of information contained in the study course thesaurus graph as the metrics of the study course thesaurus complexity. Educatee's thesaurus is considered as an object of measuring educational material learned at the semantic level and is assessed on the basis of amount of information contained in its graph, taking into account the factors of learning the thesaurus objects.

Keywords

E-Learning, Thesaurus, Learning Management System, Thesaurus Metrics, Knowledge Measurement, Study Course Material, Knowledge Testing.

References

• Abdulmyanova, I.R. (2010). Formation of professional personal thesaurus as a goal of the professional education. Tomsk State Pedagogical University Bulletin, 2010, № 2, p. 36
• Bechhofer, S., Goble C. (2001). Thesaurus constructiom through knowledge representation. Data & Knowledge Engineering, 2001, Vol. 37, № 1, p. 25-45.
• Bonchev, D., Buck, G.A. (2005). Quantitative Measures of Network Complexity. In
• Complexity in Chemistry, Biology, and Ecology, D. Bonchev and D. H. Rouvray, Eds., Springer, New York, 2005, p. 191-235.
• Gangemi, A., Catenacci, C., Ciaramita, M., Lehmann, J. (2005). Ontology evaluation and validation. An integrated formal model for the quality diagnostic task. Technical Report, available at http://www.loa-cnr.it/Files/OntoEval4OntoDev_Final.pdf.
• Lozano-Tello, A., Gomez-Perez, A. (2004). Ontometric: A method to choose the appropriate ontology. Database Management, 2004, 15(2), p. 1–18.
• Maedche, A., Staab, S. (2002). Measuring similarity between ontologies. Knowledge
• Engineering and Knowledge Management. Ontologies and the Semantic Web, 13th International Conference, EKAW 2002. Siguenza, Spain, Vol. 2473, p. 251-263
• Myrick, J. (2010). Moodle 1.9 Testing and Assessment. Birmingham: Packt Publishing Ltd., 2010.
• Nash, S.S., Rice, W. (2010). Moodle 1.9 Teaching Techniques. Birmingham: Packt Publishing Ltd, 2010.
• Rybanov, A.A. (2013). Set of criteria for efficiency of the process forming the answers to multiple-choice test items. Turkish Online Journal of Distance Education, Vol. 14, No. 1, 75
• Scalise, K., Gifford, B. (2006). Computer-Based Assessment in E-Learning: A Framework for Constructing “Intermediate Constraint” Questions and Tasks for Technology
• Platforms. Journal of Technology, Learning, and Assessment, 2006, Vol. 4(6). Retrieved
• [date] from http://www.jtla.org. Shannon, C.E, Weaver, W. (1949). The Mathematical Theory of Communication. The University of Illinois Press, Urbana, Illinois.
• Soergel, D., Lauser, B., Liang, A., Fisseha, F., Keizer, J., Katz, S. (2004). Reengineering thesauri for new applications: the AGROVOC example. Journal of Digital Information, 2004, Vol. 4, available at ftp://ftp.fao.org/docrep/fao/008/af234e/af234e00.pdf.