Education

Driving STEM learning effectiveness: dropout prediction and intervention in MOOCs based on one novel behavioral data analysis approach

X. Xia and W. Qi

This study by Xiaona Xia and Wanxue Qi tackles the pressing issue of high dropout rates in online STEM education through an innovative dropout prediction model. By analyzing MOOC learning behavior data, the model effectively predicts dropouts and reveals useful intervention strategies to enhance STEM learning success.

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Abstract

This study addresses the high dropout rates in online STEM education by developing a novel dropout prediction model using MOOC learning behavior data. The model integrates convolutional and recurrent neural networks with a long short-term memory mechanism to predict dropouts based on explicit and implicit features of learning behavior. The study analyzes key temporal sequences of the learning process, identifies impacting factors, and deduces intervention measures. The results show high prediction accuracy and provide effective strategies for improving STEM learning effectiveness in MOOCs.

Publisher

Humanities & Social Sciences Communications

Published On

Mar 18, 2024

Authors

Xiaona Xia, Wanxue Qi

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