基于深度学习的MOOCs辍学率预测方法

近年来大规模开放在线课程获得了较为广泛的关注。由于学习者学习方式不合理使得学习兴趣下降,学习效果不佳,MOOCs辍学率很高,针对这一问题,从学习者学习活动日志中自动抽取一段时间内连续特征,以学习者行为特征为自变量,建立MOOCs辍学预测模型。在KDD Cup 2015数据集上的实验表明,使用基于卷积神经网络的长短期记忆CNN_LSTM辍学预测模型,能够帮助MOOCs课程教师和设计者追踪课程学习者在不同时间步长的学习状态,从而动态监控不同阶段的辍学行为,模型的预测准确率高,这将为教师改进教学方法提供更合理的指导和建议。

A MOOCs dropout rate prediction method based on deep learning

In recent years, massive open online courses (MOOCs) have received extensive attention. Due to the unreasonable learning styles of learners, their interest in learning is declining and some learning effect is not good, so the dropout rate of MOOCs is very high. In order to solve this problem, we automatically extract continuous features over a period of time from learners’ learning activity logs, and establish a MOOCs dropout prediction model by taking learners’ behavior features as independent variables. Experiments on the KDD Cup 2015 dataset show that the dropout rate prediction model in the long short term memory in the convolutional neural network (CNN_LSTM) can help MOOCs curriculum teachers and designers track the learning states of course learners at different phases, and dynamically monitor the dropout behavior of different stages. The prediction accuracy of the model is high, so it can provide teachers with more reasonable guidance and advice on improving their teaching methods.