Improving the Classification of Study-related Data through Social Network Analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F11%3A00053570" target="_blank" >RIV/00216224:14330/11:00053570 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Improving the Classification of Study-related Data through Social Network Analysis

Popis výsledku v původním jazyce

The Information System of Masaryk University (IS MU) hosts applications utilized for managing study-related records, e-learning tools and those facilitating communication inside the University. This paper is concerned with improvement of results obtainedwith Excalibur, a tool for mining study-related data, when linked data have been added. These data describe social dependencies gathered from e-mail and discussion boards conversation. We first describe results based on the original (non-linked) data that are periodically saved into Excalibur data warehouse. Then focus on extraction of social dependencies namely relations and communication among students. We describe a method for feature extraction from the social dependencies. New features were explored by social network analysis and visualization tool Pajek and added to the original data. We show that such enriched data allows to significantly improve results obtained with data mining methods.

Název v anglickém jazyce

Improving the Classification of Study-related Data through Social Network Analysis

Popis výsledku anglicky

The Information System of Masaryk University (IS MU) hosts applications utilized for managing study-related records, e-learning tools and those facilitating communication inside the University. This paper is concerned with improvement of results obtainedwith Excalibur, a tool for mining study-related data, when linked data have been added. These data describe social dependencies gathered from e-mail and discussion boards conversation. We first describe results based on the original (non-linked) data that are periodically saved into Excalibur data warehouse. Then focus on extraction of social dependencies namely relations and communication among students. We describe a method for feature extraction from the social dependencies. New features were explored by social network analysis and visualization tool Pajek and added to the original data. We show that such enriched data allows to significantly improve results obtained with data mining methods.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/LA09016" target="_blank" >LA09016: Účast ČR v European Research Consortium for Informatics and Mathematics (ERCIM)</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

2011

Kód důvěrnosti údajů

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

Název statě ve sborníku

Memics 2011 - Seventh Doctoral Workshop on Mathematical and Engeneering Methods in Computer Science

ISBN

978-80-214-4305-1

ISSN

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e-ISSN

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Počet stran výsledku

8

Strana od-do

3-10

Název nakladatele

Brno University of Technology

Místo vydání

Brno

Místo konání akce

Lednice

Datum konání akce

1. 1. 2011

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

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