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

D 2011

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

BAYER, Jaroslav, Hana BYDŽOVSKÁ, Jan GÉRYK, Tomáš OBŠÍVAČ, Lubomír POPELÍNSKÝ et. al.

Základní údaje

Originální název

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

Autoři

BAYER, Jaroslav (203 Česká republika, domácí), Hana BYDŽOVSKÁ (203 Česká republika, domácí), Jan GÉRYK (203 Česká republika, domácí), Tomáš OBŠÍVAČ (203 Česká republika, domácí) a Lubomír POPELÍNSKÝ (203 Česká republika, garant, domácí)

Vydání

first. Brno, Memics 2011 - Seventh Doctoral Workshop on Mathematical and Engeneering Methods in Computer Science, od s. 3-10, 8 s. 2011

Nakladatel

Brno University of Technology

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Česká republika

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Kód RIV

RIV/00216224:14330/11:00053570

Organizační jednotka

Fakulta informatiky

ISBN

978-80-214-4305-1

Klíčová slova anglicky

Data Mining; Weka; Pajek; Social Network Analysis

Štítky

best2

Příznaky

Recenzováno

Změněno: 16. 10. 2014 08:51, RNDr. Hana Bydžovská, Ph.D.

Anotace

V originále

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 obtained with 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. We demonstrate this general technique on different tasks that concern classification of successful/non-successful students at Faculty of Informatics MU.

Návaznosti

LA09016, projekt VaV

Název: Účast ČR v European Research Consortium for Informatics and Mathematics (ERCIM) (Akronym: ERCIM)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Účast ČR v European Research Consortium for Informatics and Mathematics

Citovat

BAYER, Jaroslav, Hana BYDŽOVSKÁ, Jan GÉRYK, Tomáš OBŠÍVAČ a Lubomír POPELÍNSKÝ. Improving the Classification of Study-related Data through Social Network Analysis. In Z. Kotásek, J. Bouda, I. Černá, L. Sekanina, T. Vojnar, D. Antoš. Memics 2011 - Seventh Doctoral Workshop on Mathematical and Engeneering Methods in Computer Science. first. Brno: Brno University of Technology, 2011, s. 3-10. ISBN 978-80-214-4305-1.

@inproceedings{954083,
   author = {Bayer, Jaroslav and Bydžovská, Hana and Géryk, Jan and Obšívač, Tomáš and Popelínský, Lubomír},
   address = {Brno},
   booktitle = {Memics 2011 - Seventh Doctoral Workshop on Mathematical and Engeneering Methods in Computer Science},
   edition = {first},
   editor = {Z. Kotásek, J. Bouda, I. Černá, L. Sekanina, T. Vojnar, D. Antoš},
   keywords = {Data Mining; Weka; Pajek; Social Network Analysis},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Brno},
   isbn = {978-80-214-4305-1},
   pages = {3-10},
   publisher = {Brno University of Technology},
   title = {Improving the Classification of Study-related Data through Social Network Analysis},
   year = {2011}
}

TY  - JOUR
ID  - 954083
AU  - Bayer, Jaroslav - Bydžovská, Hana - Géryk, Jan - Obšívač, Tomáš - Popelínský, Lubomír
PY  - 2011
TI  - Improving the Classification of Study-related Data through Social Network Analysis
PB  - Brno University of Technology
CY  - Brno
SN  - 9788021443051
KW  - Data Mining
KW  - Weka
KW  - Pajek
KW  - Social Network Analysis
N2  - 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 obtained with 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. We demonstrate this general technique on different tasks that concern classification of successful/non-successful students at Faculty of Informatics MU.
ER  -

BAYER, Jaroslav, Hana BYDŽOVSKÁ, Jan GÉRYK, Tomáš OBŠÍVAČ a Lubomír POPELÍNSKÝ. Improving the Classification of Study-related Data through Social Network Analysis. In Z. Kotásek, J. Bouda, I. Černá, L. Sekanina, T. Vojnar, D. Antoš. \textit{Memics 2011 - Seventh Doctoral Workshop on Mathematical and Engeneering Methods in Computer Science}. first. Brno: Brno University of Technology, 2011, s.~3-10. ISBN~978-80-214-4305-1.

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