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Students' Behavior Patterns in LMS eLogika Detected by FCA

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F17%3A10235980" target="_blank" >RIV/61989100:27240/17:10235980 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.5593/sgem2017/21/S07.079" target="_blank" >http://dx.doi.org/10.5593/sgem2017/21/S07.079</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.5593/sgem2017/21/S07.079" target="_blank" >10.5593/sgem2017/21/S07.079</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Students' Behavior Patterns in LMS eLogika Detected by FCA

  • Original language description

    The e-learning system eLogika serves for teaching logic. In the system there are study materials at students’ disposal, but it also provides online tests and promotes all the activities in which the students can obtain their credits. 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. The concepts retrieved by this method represent groups of students with a common behavior in the learning process. These concepts are further used in the e-learning system in order to inform students about their positive/negative tendencies with respect to their course results. Another way of utilizing these patterns is their application in order to improve the system to be more user-friendly. 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 two ways of data categorization that proved to be applicable in the FCA method with promising results.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/GA15-13277S" target="_blank" >GA15-13277S: Hyperintensional logic for natural language analysis</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2017

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    17th International Multidisciplinary Scientific GeoConference: SGEM 2017 : conference proceedings : 29 June-5 July, 2017, Albena, Bulgaria. Volume 17. Issue 21

  • ISBN

    978-619-7408-01-0

  • ISSN

    1314-2704

  • e-ISSN

    neuvedeno

  • Number of pages

    8

  • Pages from-to

    617-624

  • Publisher name

    STEF92 Technology Ltd.

  • Place of publication

    Sofia

  • Event location

    Albena

  • Event date

    Jun 29, 2017

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article