Keep It Up: In-session Dropout Prediction to Support Blended Classroom Scenarios

Nathalie Rzepka, Katharina Simbeck, Hans-Georg Müller, Niels Pinkwart

2022

Abstract

Dropout prediction models for Massive Open Online Courses (MOOCs) have shown high accuracy rates in the past and make personalized interventions possible. While MOOCs have traditionally high dropout rates, school homework and assignments are supposed to be completed by all learners. In the pandemic, online learning platforms were used to support school teaching. In this setting, dropout predictions have to be designed differently as a simple dropout from the (mandatory) class is not possible. The aim of our work is to transfer traditional temporal dropout prediction models to in-session dropout prediction for school-supporting learning platforms. For this purpose, we used data from more than 164,000 sessions by 52,000 users of the online language learning platform orthografietrainer.net. We calculated time-progressive machine learning models that predict dropout after each step (completed sentence) in the assignment using learning process data. The multilayer perceptron is outperforming the baseline algorithms with up to 87% accuracy. By extending the binary prediction with dropout probabilities, we were able to design a personalized intervention strategy that distinguishes between motivational and subject-specific interventions.

Paper Citation

in Harvard Style

Rzepka N., Simbeck K., Müller H. and Pinkwart N. (2022). Keep It Up: In-session Dropout Prediction to Support Blended Classroom Scenarios. In Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU, ISBN 978-989-758-562-3, pages 131-138. DOI: 10.5220/0010969000003182

in Bibtex Style

@conference{csedu22,
author={Nathalie Rzepka and Katharina Simbeck and Hans-Georg Müller and Niels Pinkwart},
title={Keep It Up: In-session Dropout Prediction to Support Blended Classroom Scenarios},
booktitle={Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU,},
year={2022},
pages={131-138},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010969000003182},
isbn={978-989-758-562-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU,
TI - Keep It Up: In-session Dropout Prediction to Support Blended Classroom Scenarios
SN - 978-989-758-562-3
AU - Rzepka N.
AU - Simbeck K.
AU - Müller H.
AU - Pinkwart N.
PY - 2022
SP - 131
EP - 138
DO - 10.5220/0010969000003182