TY - GEN
T1 - Predicting User Dropout from their Online Learning Behavior
AU - Shayan, Parisa
AU - van Zaanen, Menno
AU - Atzmueller, Martin
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - The Covid-19 pandemic, which required more people to work and learn remotely, emphasized the benefits of online learning. However, these online learning environments, which are typically used on an individual basis, can make it difficult for many to finish courses effectively. At the same time, online learning allows for the monitoring of users, which may help to identify learners who are struggling. In this article, we present the results of a set of experiments focusing on the early prediction of user drop out, based on data from the New Heroes Academy, a learning center providing online courses. For measuring the impact of user behavior over time with respect to user drop out, we build a range of random forest classifiers. Each classifier uses all features, but the feature values are calculated from the day a user starts a course up to a particular day. The target describes whether the user will finish the course or not. Our experimental results (using 10-fold cross-validation) show that the classifiers provide good results (over 90% accuracy from day three with somewhat lower results for the classifiers for day one and two). In particular, the time-based and action-based features have a major impact on the performance, whereas the start-based feature is only important early on (i. e., during day one).
AB - The Covid-19 pandemic, which required more people to work and learn remotely, emphasized the benefits of online learning. However, these online learning environments, which are typically used on an individual basis, can make it difficult for many to finish courses effectively. At the same time, online learning allows for the monitoring of users, which may help to identify learners who are struggling. In this article, we present the results of a set of experiments focusing on the early prediction of user drop out, based on data from the New Heroes Academy, a learning center providing online courses. For measuring the impact of user behavior over time with respect to user drop out, we build a range of random forest classifiers. Each classifier uses all features, but the feature values are calculated from the day a user starts a course up to a particular day. The target describes whether the user will finish the course or not. Our experimental results (using 10-fold cross-validation) show that the classifiers provide good results (over 90% accuracy from day three with somewhat lower results for the classifiers for day one and two). In particular, the time-based and action-based features have a major impact on the performance, whereas the start-based feature is only important early on (i. e., during day one).
KW - Data mining
KW - Dropout prediction
KW - User modeling
UR - http://www.scopus.com/inward/record.url?scp=85142693419&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-18840-4_18
DO - 10.1007/978-3-031-18840-4_18
M3 - Conference contribution
SN - 9783031188398
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 243
EP - 252
BT - Discovery Science - 25th International Conference, DS 2022, Proceedings
A2 - Pascal, Poncelet
A2 - Ienco, Dino
ER -