Abstract
Formal Concept Analysis (FCA) is a prominent field of applied mathematics which is closely related to knowledge discovery, processing and representation. We consider the problem of distilling relevant conceptual structures from weblog data, more precisely, we investigate users’ behavioral patterns in an web based educational platform by using n-adic FCA (\(n=3, n=4\)). We focus in our research on log data gathered from e-learning platforms. Such systems are particularly interesting, since user’s behavioral patterns are closely related to their academic performance. We investigate user’s behavior by using similarity measures of various visited page chains. We exemplify the methods we have developed on a locally developed e-learning platform called PULSE. Data gathered from weblogs have been preprocessed and conceptual landscapes of knowledge have been built using FCA. Triadic FCA (3FCA) is used to investigate correlations between similar page chains and the time granule when a certain pattern occurs. Finally, we employ tetradic FCA (4FCA) to compare web usage patterns wrt. temporal development and occurence. As far as we know, this is the first attempt to use 4FCA in web usage mining.
D.-F. Haliţă—This paper is a result of a doctoral research made possible by the financial support of the Sectoral Operational Programme for Human Resources Development 2007–2013, co-financed by the European Social Fund, under the project POSDRU/187/1.5/S/155383- “Quality, excellence, transnational mobility in doctoral research”.
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Dragoş, SM., Haliţă, DF., Săcărea, C. (2016). Distilling Conceptual Structures from Weblog Data Using Polyadic FCA. In: Haemmerlé, O., Stapleton, G., Faron Zucker, C. (eds) Graph-Based Representation and Reasoning. ICCS 2016. Lecture Notes in Computer Science(), vol 9717. Springer, Cham. https://doi.org/10.1007/978-3-319-40985-6_12
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