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Authors: Marlene Bültemann ; Katharina Simbeck ; Nathalie Rzepka and Yannick Kalff

Affiliation: Hochschule für Technik und Wirtschaft Berlin, Treskowallee 8, 10318 Berlin, Germany

Keyword(s): Green IT, Sustainable Learning Analytics, Learning Analytics, Artificial Intelligence in Education.

Abstract: As learning analytics increasingly relies on machine learning (ML) to provide insights and enhance educational outcomes, the environmental impact of these ML-driven tools has become a critical but underexamined issue. This study aims to fill this gap by investigating the energy consumption of various machine learning models commonly employed in learning analytics. This is by the execution of four distinct models — Support Vector Machines (SVM), Multi-Layer Perceptrons (MLP), Decision Trees (DT), and Logistic Regression (LogReg) — when applied to an educational data set. Our findings reveal significant disparities in energy consumption between these models, with SVM and MLP models consuming considerably more energy than their simpler counterparts. This research serves as a call for action for the learning analytics community to prioritize energy-efficient AI models, thereby contributing to broader sustainability goals in the face of climate change.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Bültemann, M.; Simbeck, K.; Rzepka, N. and Kalff, Y. (2024). Sustainable Learning Analytics: Measuring and Understanding the Drivers of Energy Consumption of AI in Education. In Proceedings of the 16th International Conference on Computer Supported Education - Volume 1: CSEDU; ISBN 978-989-758-697-2; ISSN 2184-5026, SciTePress, pages 272-279. DOI: 10.5220/0012547600003693

@conference{csedu24,
author={Marlene Bültemann. and Katharina Simbeck. and Nathalie Rzepka. and Yannick Kalff.},
title={Sustainable Learning Analytics: Measuring and Understanding the Drivers of Energy Consumption of AI in Education},
booktitle={Proceedings of the 16th International Conference on Computer Supported Education - Volume 1: CSEDU},
year={2024},
pages={272-279},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012547600003693},
isbn={978-989-758-697-2},
issn={2184-5026},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Computer Supported Education - Volume 1: CSEDU
TI - Sustainable Learning Analytics: Measuring and Understanding the Drivers of Energy Consumption of AI in Education
SN - 978-989-758-697-2
IS - 2184-5026
AU - Bültemann, M.
AU - Simbeck, K.
AU - Rzepka, N.
AU - Kalff, Y.
PY - 2024
SP - 272
EP - 279
DO - 10.5220/0012547600003693
PB - SciTePress

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