Authors:
Nathalie Rzepka
1
;
Katharina Simbeck
1
;
Hans-Georg Müller
2
and
Niels Pinkwart
3
Affiliations:
1
University of Applied Sciences Berlin, Treskowallee 8, 10318 Berlin, Germany
;
2
Department of German Studies, University of Potsdam, Am neuen Palais 10, 14469 Potsdam, Germany
;
3
Department of Computer Science, Humboldt University of Berlin, Unter den Linden 6, 10099 Berlin, Germany
Keyword(s):
Dropout Prediction, VLE, Blended Classroom.
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 outperform
ing 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.
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