Analyzing Student Programming Paths using Clustering and Process Mining

Anis Bey; Ronan Champagnat

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Paper

Analyzing Student Programming Paths using Clustering and Process Mining

Topics: Learning Analytics and Educational Data Mining; Machine Learning

In Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU, 76-84, 2022

Authors: Anis Bey and Ronan Champagnat

Affiliation: Laboratoire Informatique, Image, Interaction (L3i), La Rochelle University, La Rochelle, France

Keyword(s): Computer Science Education, Unsupervised Learning, Students' Behavior, Computer Programming, Behavioral Interactions.

Abstract: Learning programming is becoming more and more common across all curricula, as seen by the growing number of tools and platforms built to assist it. This paper describes the results of an empirical study that aimed to better understand students’ programming habits. The analysis is based on unsupervised classification algorithms, including features from previous educational data mining research. The k-means method was used to identify the behaviors of six students profiles. The main and interaction impacts of those behaviors on their final course scores are tested using analysis of covariance.

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Paper citation in several formats:

Bey, A. and Champagnat, R. (2022). Analyzing Student Programming Paths using Clustering and Process Mining. In Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU; ISBN 978-989-758-562-3; ISSN 2184-5026, SciTePress, pages 76-84. DOI: 10.5220/0011077300003182

@conference{csedu22,
author={Anis Bey. and Ronan Champagnat.},
title={Analyzing Student Programming Paths using Clustering and Process Mining},
booktitle={Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU},
year={2022},
pages={76-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011077300003182},
isbn={978-989-758-562-3},
issn={2184-5026},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU
TI - Analyzing Student Programming Paths using Clustering and Process Mining
SN - 978-989-758-562-3
IS - 2184-5026
AU - Bey, A.
AU - Champagnat, R.
PY - 2022
SP - 76
EP - 84
DO - 10.5220/0011077300003182
PB - SciTePress