relation: http://publicatio.bibl.u-szeged.hu/23817/
title: Moodle-based data mining potentials of MOOC systems at the University of Szeged
creator:  Kőrösi Gábor
creator:  Havasi Ferenc
subject: 01.02. Számítás- és információtudomány
description: In today's world virtual online educational platforms emerge literally on daily bases and many offer MOOC-based courses. With the appearance of MOOC, educational platforms have gained an additional boost, a new aspect in their evolutionary process, which has opened a new field of research thanking to the extraction of logging information within the frames of data mining. It has become clear that educators will be able to tailor their courses by merging the two previously mentioned fields and by carrying out MOOC-based data mining, targeting pedagogical aspects. This field of research seems promising and important, thus a faculty at the University of Szeged has created its own MOOC educational platform which has been set to facilitate data mining by implementing a wide range of logging algorithms. The data would be processed through a complex Artificial Intelligence program, which, in the short term, could reveal new and exciting pedagogical findings, while in the long run, the supervisors could put together a platform that would help and notify educators about relevant information. It would become possible to create adaptive educational materials, as well. This work aims at clarifying how such platforms function and what the steps of data collection and evaluation are.
publisher: IEEE
date: 2017
type: Könyv része
type: NonPeerReviewed
format: text
identifier: http://publicatio.bibl.u-szeged.hu/23817/1/2017_KorosiG-mipro_2017_proceedings.pdf
identifier:     Kőrösi Gábor;  Havasi Ferenc: Moodle-based data mining potentials of MOOC systems at the University of Szeged.    In:  2017 40TH INTERNATIONAL CONVENTION ON INFORMATION AND COMMUNICATION TECHNOLOGY, ELECTRONICS AND MICROELECTRONICS (MIPRO).   IEEE, New York (NY), pp. 755-760. (2017)  ISBN 9789532330922     
identifier: doi:10.23919/MIPRO.2017.7973523
relation: http://doi.org/10.23919/MIPRO.2017.7973523
relation: 31559478
language: eng
relation: info:eu-repo/semantics/altIdentifier/doi/10.23919/MIPRO.2017.7973523
rights: info:eu-repo/semantics/openAccess