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Authors: Aisyah Larasati ; Apif Miftahul Hajji and Anik Nur Handayani

Affiliation: Universitas Negeri Malang, Indonesia

Keyword(s): Learning Characteristics, Engineering Students, Data Mining Clustering Technique.

Abstract: Everyone has their own characteristic way of thinking that make them to have different ways to act. These characteristics also affect their behaviour in daily life, including their learning characteristics. This study aims to identify the learning characteristics pattern of engineering students using data mining clustering technique. This study uses questionnaire to collect data. The total number of students fill out the questionnaire are 2,934. After data preparation steps, only 1,914 responses (65.23% usable rate) are complete and can be used for further analysis. To identify the learning characteristics pattern, this study uses data mining clustering technique. The clustering techniques used in this study are K-means cluster, Kohonen cluster analysis, and two step cluster analysis. The results show that all three cluster techniques used in this study identify the frequency of a respondent does an independent study by solving practice exercise after learning a new material in the c lass, the frequency of a respondent studies the material he learnt after attending a class and the frequency of a respondent discusses the learning material are the top three important variables to differentiate each cluster. (More)

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Paper citation in several formats:
Larasati, A.; Hajji, A. and Handayani, A. (2019). Identification of Learning Characteristics Pattern of Engineering Students using Clustering Techniques. In Proceedings of the 2nd International Conference on Learning Innovation - ICLI; ISBN 978-989-758-391-9, SciTePress, pages 274-278. DOI: 10.5220/0008411002740278

@conference{icli19,
author={Aisyah Larasati. and Apif Miftahul Hajji. and Anik Nur Handayani.},
title={Identification of Learning Characteristics Pattern of Engineering Students using Clustering Techniques},
booktitle={Proceedings of the 2nd International Conference on Learning Innovation - ICLI},
year={2019},
pages={274-278},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008411002740278},
isbn={978-989-758-391-9},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Learning Innovation - ICLI
TI - Identification of Learning Characteristics Pattern of Engineering Students using Clustering Techniques
SN - 978-989-758-391-9
AU - Larasati, A.
AU - Hajji, A.
AU - Handayani, A.
PY - 2019
SP - 274
EP - 278
DO - 10.5220/0008411002740278
PB - SciTePress

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