Artificial Intelligence Algorithms to Predict College Students’ Dropout: A Systematic Mapping Study

Henrique Rodrigues, Eduardo Santiago, Gabriel Wanderley, Laura Moraes, Carlos Eduardo Mello, Reinaldo Alvares, Rodrigo Santos

2024

Abstract

Higher Education Institutions (HEIs), including universities, colleges, and faculties, must develop strategies to mitigate students’ dropout rates in undergraduate courses. This is crucial for fulfilling their social role, delivering high-quality professionals to society, contributing to economic development, and preventing the resource wastage. In this context, artificial intelligence (AI) algorithms have emerged as powerful tools capable of predicting dropout rates and identifying undergraduates at risk. This study aims to investigate and discuss the state-of-the-art in applying AI algorithms to address students’ dropout. To achieve this objective, a systematic mapping study (SMS) was conducted, encompassing 223 studies at first. Finally, 23 studies were selected for in-depth analysis to explore the effectiveness of AI algorithms in predicting students’ dropout. Furthermore, we identified key methodological design issues associated with the application of these AI algorithms, including common features and challenges in implementing these methodologies. This study contributes by providing practitioners and researchers with an overview of the main challenges faced by AI algorithms in predicting students’ dropout, highlighting issues related to modeling, experimental methodology, and problem framing.

Paper Citation

in Harvard Style

Rodrigues H., Santiago E., Wanderley G., Moraes L., Eduardo Mello C., Alvares R. and Santos R. (2024). Artificial Intelligence Algorithms to Predict College Students’ Dropout: A Systematic Mapping Study. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 344-351. DOI: 10.5220/0012348000003636

in Bibtex Style

@conference{icaart24,
author={Henrique Rodrigues and Eduardo Santiago and Gabriel Wanderley and Laura Moraes and Carlos Eduardo Mello and Reinaldo Alvares and Rodrigo Santos},
title={Artificial Intelligence Algorithms to Predict College Students’ Dropout: A Systematic Mapping Study},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={344-351},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012348000003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Artificial Intelligence Algorithms to Predict College Students’ Dropout: A Systematic Mapping Study
SN - 978-989-758-680-4
AU - Rodrigues H.
AU - Santiago E.
AU - Wanderley G.
AU - Moraes L.
AU - Eduardo Mello C.
AU - Alvares R.
AU - Santos R.
PY - 2024
SP - 344
EP - 351
DO - 10.5220/0012348000003636
PB - SciTePress