Abstract
Large amount of routine work necessary to perform comparative analysis of university courses possess the importance of the automation of this task. Possibility of effortless detection of similarity between different courses would give the opportunity to organize student exchange programmes effectively and facilitate curriculum management and development. The application of smartly adapted machine learning technologies in long term could reduce the manual course comparison effort. The goal of this paper is to present the application of earlier proposed inductive learning based classification system (accompanied with interactive capabilities) to directly and indirectly compare study courses semi-automatically. The evaluation of the proposed system has been carried out in 4 consecutive experiments which proved the ability to decrease the number of misclassified instances if uncertain classifications are detected and passed to the expert’s review.
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References
Kennedy, D., Hyland, Á., Ryan, N.: Writing and Using Learning Outcomes: a Practical Guide. In: EUA Bologna Handbook: Making Bologna Work (2009)
Alves, H., Figueira, Á.: A Educational Library based on Clusters of Semantic Proximity. In: IADIS European Conference on Data Mining, pp. 226–228 (2011)
Anohina-Naumeca, A., Graudiņa, V., Grundspeņķis, J.: Curricula Comparison Using Concept Maps and Ontologies (2012)
Birzniece, I., Kirikova, M.: Interactive Inductive Learning Service for Indirect Analysis of Study Subject Compatibility. In: Proc. BeneLearn 2010, Belgium, Leuven, pp. 1–6 (2010)
Biletskiy, Y., Brown, J.A., Ranganathan, G.: Information extraction from syllabi for academic e-Advising. Expert Syst. Appl. 36, 4508–4516 (2009)
Ranganthan, G.R., Biletskiy, Y., MacIsaac, D.: Machine Learning for Classifying Learning Objects. In: IEEE CCECE/CCGEI, pp. 280–283 (2006)
Biletska, O., Biletskiy, Y., Li, H., Vovk, R.: A semantic approach to expert system for e-Assessment of credentials and competencies. Expert Syst. Appl. 37, 7003–7014 (2010)
Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34, 1–47 (2002)
Yang, J., Chen, X.: A semi-structured document model for text mining. J. Comput. Sci. Technol. 17, 603–610 (2002)
Saleem, K., Bellahsene, Z., Hunt, E.: PORSCHE: Performance ORiented SCHEma mediation. Inf. Syst. 33, 637–657 (2008)
Birzniece, I.: Interactive Use of Inductive Approach for Analyzing and Developing Conceptual Structures (2012)
Birzniece, I., Rudzajs, P.: Machine Learning Based Study Course Comparison. In: IADIS Conference on Intelligent Systems and Agents (ISA 2011), Italy, Rome, pp. 107–111 (2011).
Birzniece, I.: Machine Learning Approach for Study Course Comparison. International Conference on Machine Learning and Data Mining (MLDM 2012), pp. 1–13. IBai, Berlin (2012)
Birzniece, I.: Interactive Inductive Learning System. In: Barzdins, J., Kirikova, M. (eds.) Selected Papers from the DB&IS 2010, pp. 380–393. IOS Press, Latvia (2010)
Birzniece, I.: Development of Interactive Inductive Learning Based Classification System’s Model (2013)
Birzniece, I., Kirikova, M.: Interactive Inductive Learning: Application in Domain of Education. Sci. J. Riga Tech. Univ. Comput. Sci. Appl. Comput. Syst., 1–8 (2011) (in press)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explor 11, 10–18 (2009)
Tsoumakas, G., Spyromitros-Xioufis, E., Vilcek, J., Vlahavas, I.: Mulan: A Java Library for Multi-Label Learning. J. Mach. Learn. Res. 12, 2411–2414 (2011)
European Committee For Standardization: European e-Competence Framework 2.0, http://ecompetences.eu/
Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining Multi-label Data. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 667–685. Springer, US (2010)
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Birzniece, I., Rudzajs, P., Kalibatiene, D. (2014). Evaluating the Application of Interactive Classification System in University Study Course Comparison. In: Johansson, B., Andersson, B., Holmberg, N. (eds) Perspectives in Business Informatics Research. BIR 2014. Lecture Notes in Business Information Processing, vol 194. Springer, Cham. https://doi.org/10.1007/978-3-319-11370-8_24
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DOI: https://doi.org/10.1007/978-3-319-11370-8_24
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