Dynamic time warping in analysis of student behavioral patterns

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F12%3A86084590" target="_blank" >RIV/61989100:27240/12:86084590 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/12:86084590

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Dynamic time warping in analysis of student behavioral patterns

Popis výsledku v původním jazyce

E-learning systems store large amount of data based on the history of users' interactions with the system. These pieces of information are usually used for further course optimization, finding e-tutors in collaboration learning, analysis of students' behavior, or for other purposes. The paper deals with an analysis of students' behavior in learning management system. The main goal of the paper is to find, how selected methods can influence finding of behavioral patterns in learning management system and how we can reduce the amount of extracted sequences. The methods of process mining and sequential pattern mining were used for extraction of behavioral patterns. The authors present the comparison of selected methods for the definition of students' behavior with the focus to influence of dynamic time warping. Obtained patterns and relations between them are presented using complex networks; the visualization and pattern clusters extraction is optimized by spectral graph partitioning.

Název v anglickém jazyce

Dynamic time warping in analysis of student behavioral patterns

Popis výsledku anglicky

E-learning systems store large amount of data based on the history of users' interactions with the system. These pieces of information are usually used for further course optimization, finding e-tutors in collaboration learning, analysis of students' behavior, or for other purposes. The paper deals with an analysis of students' behavior in learning management system. The main goal of the paper is to find, how selected methods can influence finding of behavioral patterns in learning management system and how we can reduce the amount of extracted sequences. The methods of process mining and sequential pattern mining were used for extraction of behavioral patterns. The authors present the comparison of selected methods for the definition of students' behavior with the focus to influence of dynamic time warping. Obtained patterns and relations between them are presented using complex networks; the visualization and pattern clusters extraction is optimized by spectral graph partitioning.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: Centrum excelence IT4Innovations</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název statě ve sborníku

DATESO 2012 : databases, texts, specifications, and objects : proceedings of the Dateso 2012 Workshop : April 18-20, 2012, Zernov, Rovensko pod Troskami

ISBN

978-80-7378-171-2

ISSN

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e-ISSN

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Počet stran výsledku

11

Strana od-do

49-59

Název nakladatele

MATFYZPRESS

Místo vydání

Praha

Místo konání akce

Žernov, Semily

Datum konání akce

18. 4. 2012

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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