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Authors: Igor Moreira Félix 1 ; Ana Paula Ambrósio 2 ; Priscila Silva Neves 1 ; Joyce Siqueira 1 and Jacques Duilio Brancher 3

Affiliations: 1 Universidade Federal de Goiás, Brazil ; 2 Federal University of Goiás, Brazil ; 3 Universidade Estadual de Londrina, Brazil

Keyword(s): Educational Data Mining, Moodle, Prediction, Tool, Virtual Learning Environment.

Related Ontology Subjects/Areas/Topics: Computer-Supported Education ; Information Technologies Supporting Learning ; Learning Analytics

Abstract: Educational data mining (EDM) aims to find useful patterns in large volumes of data from teaching/learning environments, increasing academic results. However, EDM requires previous and deep knowledge of data mining methods and techniques, involving several computing paradigms, preprocessing and results’ interpretation. In this paper, Moodle Predicta, an educational data mining desktop tool is presented. This software is developed in Java and enables non-expert data mining users to enjoy benefits from EDM, within the Moodle system. Divided in two modules, Moodle Predicta allows: (i) visualization of Moodle courses data; and (ii) predict students’ performance.

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Paper citation in several formats:
Moreira Félix, I.; Ambrósio, A.; Silva Neves, P.; Siqueira, J. and Duilio Brancher, J. (2017). Moodle Predicta: A Data Mining Tool for Student Follow Up. In Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU; ISBN 978-989-758-239-4; ISSN 2184-5026, SciTePress, pages 339-346. DOI: 10.5220/0006318403390346

@conference{csedu17,
author={Igor {Moreira Félix}. and Ana Paula Ambrósio. and Priscila {Silva Neves}. and Joyce Siqueira. and Jacques {Duilio Brancher}.},
title={Moodle Predicta: A Data Mining Tool for Student Follow Up},
booktitle={Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU},
year={2017},
pages={339-346},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006318403390346},
isbn={978-989-758-239-4},
issn={2184-5026},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU
TI - Moodle Predicta: A Data Mining Tool for Student Follow Up
SN - 978-989-758-239-4
IS - 2184-5026
AU - Moreira Félix, I.
AU - Ambrósio, A.
AU - Silva Neves, P.
AU - Siqueira, J.
AU - Duilio Brancher, J.
PY - 2017
SP - 339
EP - 346
DO - 10.5220/0006318403390346
PB - SciTePress

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