Data-mining and the quality of distance-education improvement

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F13%3A86086696" target="_blank" >RIV/61989100:27240/13:86086696 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.5593/SGEM2013/BB2.V1/S07.010" target="_blank" >http://dx.doi.org/10.5593/SGEM2013/BB2.V1/S07.010</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5593/SGEM2013/BB2.V1/S07.010" target="_blank" >10.5593/SGEM2013/BB2.V1/S07.010</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Data-mining and the quality of distance-education improvement

Popis výsledku v původním jazyce

Using LMS (Learning Management System) has become a common practice in the universities in which a large number of students are enrolled in particular courses. The LMS systems make it possible to store data that contain pieces of hidden information thatmay appear to be valuable and yield interesting results. In this paper we introduce the eLogika system and describe data-mining methods that make it possible to make that implicit information explicit. We describe the way of storing those data that are invisible to users but they contain interesting pieces of information on teaching logic, like students? abilities, typical mistakes and so on. The methods of preprocessing these data are described so that then we can apply data-mining methods in order toimprove the quality of teaching process. We focus in particular on association rules and clustering methods.

Název v anglickém jazyce

Data-mining and the quality of distance-education improvement

Popis výsledku anglicky

Using LMS (Learning Management System) has become a common practice in the universities in which a large number of students are enrolled in particular courses. The LMS systems make it possible to store data that contain pieces of hidden information thatmay appear to be valuable and yield interesting results. In this paper we introduce the eLogika system and describe data-mining methods that make it possible to make that implicit information explicit. We describe the way of storing those data that are invisible to users but they contain interesting pieces of information on teaching logic, like students? abilities, typical mistakes and so on. The methods of preprocessing these data are described so that then we can apply data-mining methods in order toimprove the quality of teaching process. We focus in particular on association rules and clustering methods.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GAP401%2F10%2F0792" target="_blank" >GAP401/10/0792: Temporální aspekty znalostí a informací</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

SGEM 2013 : 13th international multidisciplinary scientific geoconference : GeoConference on Ecology, Economics, Education and legislation : 16-22, June, 2013, Albena, Bulgaria : conference proceedings. [Book 5]. Volume I

ISBN

978-619-7105-04-9

ISSN

1314-2704

e-ISSN

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Počet stran výsledku

8

Strana od-do

77-84

Název nakladatele

STEF92 Technology Ltd.

Místo vydání

Sofia

Místo konání akce

Albena

Datum konání akce

16. 6. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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