基于机器学习的MOOC学习者弃学预测与预警系统实现

Implementation of MOOC Learner Abandonment Prediction and Early Warning System Based on Machine Learning

混合式学习环境下,存在学习者弃学现象,影响相关情况的预测以及预警布局变化性以及稳定性。因此,本文研究了基于机器学习的MOOC学习者弃学预测与预警系统。设计主控电源电路以及预测、预警模块,完成硬件的创建;在机器学习下建立总体架构指令,通过拓扑深度学习实现预警数据库设计,完成软件设计。系统测试表明:在相同的测试环境之下,对比于未应用机器学习的MOOC学习者弃学预测与预警系统测试组,应用的测试组最终得出的召回率相对较低。本文设计的系统具有更强的灵活性,预测、预警的效果较为精准,并能够更加精准地应变处理,具有实际的应用意义。

In the blended learning environment, there is a phenomenon of learners abandoning their studies, which affects the prediction of relevant situations and the variability and stability of early warning layout. Therefore, this paper studies the MOOC learner abandonment prediction and early warning system based on machine learning. Design the main control power supply circuit,prediction and early warning module, and complete the creation of hardware;The overall architecture instruction is established under machine learning, the early warning database design is realized through topology deep learning, and the software design is completed. The system test shows that under the same test environment, compared with the MOOC learner abandonment prediction and early warning system test group without machine learning, the final recall rate of the applied test group is relatively low. The system designed in this paper has stronger flexibility, the effect of prediction and early warning is more accurate, and can deal with emergencies more accurately, which has practical Application significance.