Course Recommendation from Social Data

D 2014

Course Recommendation from Social Data

BYDŽOVSKÁ, Hana a Lubomír POPELÍNSKÝ

Základní údaje

Originální název

Course Recommendation from Social Data

Autoři

BYDŽOVSKÁ, Hana (203 Česká republika, domácí) a Lubomír POPELÍNSKÝ (203 Česká republika, garant, domácí)

Vydání

Portugal, 6th International Conference on Computer Supported Education - CSEDU 2014, od s. 268-275, 8 s. 2014

Nakladatel

2014 SCITEPRESS – Science and Technology Publications

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Portugalsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

paměťový nosič (CD, DVD, flash disk)

Kód RIV

RIV/00216224:14330/14:00074736

Organizační jednotka

Fakulta informatiky

ISBN

978-989-758-020-8

Klíčová slova anglicky

Recommender System; Social Network Analysis; Data Mining; Prediction; University Information System

Štítky

firank_B

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 16. 10. 2014 08:49, RNDr. Hana Bydžovská, Ph.D.

Anotace

V originále

This paper focuses on recommendations of suitable courses for students. For a successful graduation, a student needs to obtain a minimum number of credits that depends on the field of study. Mandatory and selective courses are usually defined. Additionally, students can enrol in any optional course. Searching for interesting and achievable courses is time-consuming because it depends on individual specializations and interests. The aim of this research is to inspect different techniques how to recommend students such courses. This paper brings results of experiments with three approaches of predicting student success. The first one is based on mining study-related data and social network analysis. The second one explores only average grades of students. The last one aims at subgroup discovery for which prediction may be more reliable. Based on these findings we can recommend courses that students will pass with a high accuracy.

Citovat

BYDŽOVSKÁ, Hana a Lubomír POPELÍNSKÝ. Course Recommendation from Social Data. In Susan Zvacek, Maria Teresa Restivo, James Uhomoibhi and Markus Helfert. 6th International Conference on Computer Supported Education - CSEDU 2014. Portugal: 2014 SCITEPRESS – Science and Technology Publications, 2014, s. 268-275. ISBN 978-989-758-020-8.

@inproceedings{1120458,
   author = {Bydžovská, Hana and Popelínský, Lubomír},
   address = {Portugal},
   booktitle = {6th International Conference on Computer Supported Education - CSEDU 2014},
   editor = {Susan Zvacek, Maria Teresa Restivo, James Uhomoibhi and Markus Helfert},
   keywords = {Recommender System; Social Network Analysis; Data Mining; Prediction; University Information System},
   howpublished = {paměťový nosič},
   language = {eng},
   location = {Portugal},
   isbn = {978-989-758-020-8},
   pages = {268-275},
   publisher = {2014 SCITEPRESS – Science and Technology Publications},
   title = {Course Recommendation from Social Data},
   year = {2014}
}

TY  - JOUR
ID  - 1120458
AU  - Bydžovská, Hana - Popelínský, Lubomír
PY  - 2014
TI  - Course Recommendation from Social Data
PB  - 2014 SCITEPRESS – Science and Technology Publications
CY  - Portugal
SN  - 9789897580208
KW  - Recommender System
KW  - Social Network Analysis
KW  - Data Mining
KW  - Prediction
KW  - University Information System
N2  - This paper focuses on recommendations of suitable courses for students. For a successful graduation, a student needs to obtain a minimum number of credits that depends on the field of study. Mandatory and selective courses are usually defined. Additionally, students can enrol in any optional course. Searching for interesting and achievable courses is time-consuming because it depends on individual specializations and interests. The aim of this research is to inspect different techniques how to recommend students such courses. This paper brings results of experiments with three approaches of predicting student success. The first one is based on mining study-related data and social network analysis. The second one explores only average grades of students. The last one aims at subgroup discovery for which prediction may be more reliable. Based on these findings we can recommend courses that students will pass with a high accuracy.
ER  -

BYDŽOVSKÁ, Hana a Lubomír POPELÍNSKÝ. Course Recommendation from Social Data. In Susan Zvacek, Maria Teresa Restivo, James Uhomoibhi and Markus Helfert. \textit{6th International Conference on Computer Supported Education - CSEDU 2014}. Portugal: 2014 SCITEPRESS – Science and Technology Publications, 2014, s.~268-275. ISBN~978-989-758-020-8.

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