Analyzing Student Programming Paths using Clustering and Process Mining

Anis Bey, Ronan Champagnat

2022

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.

Paper Citation

in Harvard Style

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, pages 76-84. DOI: 10.5220/0011077300003182

in Bibtex Style

@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},
}

in EndNote Style

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
AU - Bey A.
AU - Champagnat R.
PY - 2022
SP - 76
EP - 84
DO - 10.5220/0011077300003182