BYDŽOVSKÁ, Hana and 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, p. 268-275. ISBN 978-989-758-020-8.
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Basic information
Original name Course Recommendation from Social Data
Authors BYDŽOVSKÁ, Hana (203 Czech Republic, belonging to the institution) and Lubomír POPELÍNSKÝ (203 Czech Republic, guarantor, belonging to the institution).
Edition Portugal, 6th International Conference on Computer Supported Education - CSEDU 2014, p. 268-275, 8 pp. 2014.
Publisher 2014 SCITEPRESS – Science and Technology Publications
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Portugal
Confidentiality degree is not subject to a state or trade secret
Publication form storage medium (CD, DVD, flash disk)
RIV identification code RIV/00216224:14330/14:00074736
Organization unit Faculty of Informatics
ISBN 978-989-758-020-8
Keywords in English Recommender System; Social Network Analysis; Data Mining; Prediction; University Information System
Tags firank_B
Tags International impact, Reviewed
Changed by Changed by: RNDr. Hana Bydžovská, Ph.D., učo 139544. Changed: 16/10/2014 08:49.
Abstract
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.
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