Data Scaling by Differential Evolution for FCA over Data from LMS eLogika
Abstract
The e-learning system eLogika serves for teaching logic. The system collects data about users who are
logged in, e.g. time spent on a particular activity, the number of activities performed by particular students, what
data is a student interested in, etc. The goal of this paper is to describe the application of many-valued formal
concept analysis (FCA) in order to discover typical patterns of students behavior. Since the data stored in the
eLogika system are numerical we need to categorize them in order to be used in the many-valued FCA method.
In the paper we describe a way of data categorization by di erential evolution that proved to be applicable in the
FCA method with promising results.
References
Skopljanac-Macina, F., Blaskovic, B.: Formal Concept Analysis–Overview and Applications, Procedia Engineering, pp. 1258–1267 (2014).
Beydoun, G.: Formal concept analysis for an e-learning semantic web. Expert Systems with Applications,pp. 10952–10961 (2009).
Lau, R. Y., Song, D., Li, Y., Cheung, T. C., Hao, J. X.: Toward a fuzzy domain ontology extraction methodfor adaptive e-learning. IEEE transactions on knowledge and data engineering, pp. 800–813 (2009).
CH, A. K., Dias S. M., Viera, N. J.: Knowledge reduction in formal contexts using non-negative matrixfactorization. Mathematics and Computers in Simulation, pp. 46–63 (2015).
Belohlavek, R., Konecny, J.: Scaling, granulation, and fuzzy attributes in formal concept analysis. FuzzySystems Conference, IEEE International (2007).
Wille R.: Restructuring lattice theory: an approach based on hierarchies of concepts. In: I. Rival, editor,Ordered sets, Reidel, Dordrecht-Boston, pp. 445–470 (1982).
Ganter B., Wille R.: Formal Concept Analysis. Springer Verlag, (1999).
Birkhoff G.: Lattice theory. American Mathematical Society Colloquium Publications, Providence, RI.(1967).
Mensik, M., Drazdilova, P.: Analysis of students behavior in LMS eLogika, SGEM2015 Conference Pro-ceedings, Book 2, Vol. 1, pp. 27–34 (2015).
Storn, R., Price, K.: Differential evolutiona simple and efficient heuristic for global optimization overcontinuous spaces. Journal of global optimization 11.4 pp. 341–359 (1997).
Andrews, S.: In-close, a fast algorithm for computing formal concepts. In: International Conference onConceptual Structures (ICCS), Moscow, (2009).
Ma, L., Mi, J.S., Xie, B.: Multi-scaled concept lattices based on neighborhood systems. InternationalJournal of Machine Learning and Cybernetics (2016).
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