Learner Performance Prediction Indicators based on Machine Learning

Karim Sehaba

Abstract

This work is interested in the analysis of learners’ performances in order to define indicators to predict their results based on their interactions with a learning environment. These indicators should alert learners at risk, or their teachers, by highlighting their difficulties in order to help them get around them before it is too late. For this, we have defined a trace analysis approach based on the use of machine learning methods. This approach consists of preparing the plotted data automatically and manually, by selecting the attributes relevant to learning, then automatically extracting indicators explaining the learner’s results. Our work was applied to a data set resulting from a real training comprising 32593 learners producing 10 655 280 events. The accuracy of our predictions has reached around 80%. Rules extraction methods were also applied in order to explain the rules which govern the prediction indicator.

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Paper Citation


in Harvard Style

Sehaba K. (2020). Learner Performance Prediction Indicators based on Machine Learning.In Proceedings of the 12th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-417-6, pages 47-57. DOI: 10.5220/0009396100470057


in Bibtex Style

@conference{csedu20,
author={Karim Sehaba},
title={Learner Performance Prediction Indicators based on Machine Learning},
booktitle={Proceedings of the 12th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2020},
pages={47-57},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009396100470057},
isbn={978-989-758-417-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - Learner Performance Prediction Indicators based on Machine Learning
SN - 978-989-758-417-6
AU - Sehaba K.
PY - 2020
SP - 47
EP - 57
DO - 10.5220/0009396100470057