Towards an Online Incremental Approach to Predict Students Performance

Chahrazed Labba, Anne Boyer

2024

Abstract

Analytical models developed in offline settings with pre-prepared data are typically used to predict students’ performance. However, when data are available over time, this learning method is not suitable anymore. Online learning is increasingly used to update the online models from stream data. A rehearsal technique is typically used, which entails re-training the model on a small training set that is updated each time new data is received. The main challenge in this regard is the construction of the training set with appropriate data samples to maintain good model performance. Typically, a random selection of samples is made, which can deteriorate the model’s performance. In this paper, we propose a memory-based online incremental learning approach for updating an online classifier that predicts student performance using stream data. The approach is based on the use of the genetic algorithm heuristic while respecting the memory space constraints as well as the balance of class labels. In contrast to random selection, our approach improves the stability of the analytical model by promoting diversity when creating the training set. As a proof of concept, we applied it to the open dataset OULAD. Our approach achieves a notable improvement in model accuracy, with an enhancement of nearly 10% compared to the current state-of-the-art, while maintaining a relatively low standard deviation in accuracy, ranging from 1% to 2.1%.

Paper Citation

in Harvard Style

Labba C. and Boyer A. (2024). Towards an Online Incremental Approach to Predict Students Performance. In Proceedings of the 16th International Conference on Computer Supported Education - Volume 2: CSEDU; ISBN 978-989-758-697-2, SciTePress, pages 205-212. DOI: 10.5220/0012620300003693

in Bibtex Style

@conference{csedu24,
author={Chahrazed Labba and Anne Boyer},
title={Towards an Online Incremental Approach to Predict Students Performance},
booktitle={Proceedings of the 16th International Conference on Computer Supported Education - Volume 2: CSEDU},
year={2024},
pages={205-212},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012620300003693},
isbn={978-989-758-697-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Computer Supported Education - Volume 2: CSEDU
TI - Towards an Online Incremental Approach to Predict Students Performance
SN - 978-989-758-697-2
AU - Labba C.
AU - Boyer A.
PY - 2024
SP - 205
EP - 212
DO - 10.5220/0012620300003693
PB - SciTePress