Research Article
Dropout Prediction in MOOC Combining Behavioral Sequence Characteristics
@INPROCEEDINGS{10.1007/978-3-031-33614-0_18, author={Xiaoxuan Ma and Huan Huang and Shuai Yuan and Rui Hou}, title={Dropout Prediction in MOOC Combining Behavioral Sequence Characteristics}, proceedings={Big Data Technologies and Applications. 11th and 12th EAI International Conference, BDTA 2021 and BDTA 2022, Virtual Event, December 2021 and 2022, Proceedings}, proceedings_a={BDTA}, year={2023}, month={5}, keywords={MOOC Dropout Prediction Behavioral Sequence}, doi={10.1007/978-3-031-33614-0_18} }- Xiaoxuan Ma
Huan Huang
Shuai Yuan
Rui Hou
Year: 2023
Dropout Prediction in MOOC Combining Behavioral Sequence Characteristics
BDTA
Springer
DOI: 10.1007/978-3-031-33614-0_18
Abstract
In the past decade, online education platforms led by MOOC have developed rapidly around the world, bringing great changes to the education industry. MOOC aim to provide high-quality, free and open courses for global learners. However, different from the traditional classroom education, MOOC suffers from a significant high dropout rate due to its online mode. In previous studies, researchers mostly use some well-designed features by handcraft. Such methods can be time-consuming and complicated. In this paper, we combine the unsupervised algorithm with machine learning algorithm to solve the problem of dropout prediction in MOOC. Our model use the sub-sequences identified in the participant’s behavior sequence as features, which simplifies the complexity of the features design. And a large number of experiments have been carried out on a public datasets, the experimental results show that the performance of the proposed method can be compared with the method using the high-dimensional and complex features used by other researchers.
