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Authors: Eitel J. M. Lauría ; Edward Presutti ; Maria Kapogiannis and Anuya Kamath

Affiliation: Marist College, United States

ISBN: 978-989-758-291-2

Keyword(s): Early Detection, Stacked Ensembles, Learning Management Systems, Student Information Systems, Predictive Modeling, Supervised Learning.

Abstract: A stacked ensemble is a machine learning method that involves training a second stage learner to find the optimal combination of a collection of based learners. This paper provides a methodology to create a stacked ensemble of classifiers to perform early detection of academically at-risk students and shows how to organize the data for training and testing at each stage of the stacked ensemble architecture. Experimental tests are carried out using college-wide data, to demonstrate how the stack can be used for prediction.

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Paper citation in several formats:
Lauría, E.; Presutti, E.; Kapogiannis, M. and Kamath, A. (2018). Stacking Classifiers for Early Detection of Students at Risk.In Proceedings of the 10th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-291-2, pages 390-397. DOI: 10.5220/0006781203900397

@conference{csedu18,
author={Eitel J. M. Lauría. and Edward Presutti. and Maria Kapogiannis. and Anuya Kamath.},
title={Stacking Classifiers for Early Detection of Students at Risk},
booktitle={Proceedings of the 10th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2018},
pages={390-397},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006781203900397},
isbn={978-989-758-291-2},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - Stacking Classifiers for Early Detection of Students at Risk
SN - 978-989-758-291-2
AU - Lauría, E.
AU - Presutti, E.
AU - Kapogiannis, M.
AU - Kamath, A.
PY - 2018
SP - 390
EP - 397
DO - 10.5220/0006781203900397

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