With the application of big data, artificial intelligence, and other related technologies in the field of education, using machine learning to carry out early warning for course learning has become an effective means to improve teaching quality. However, in the scene of early warning, the samples are significantly less than the ordinary samples, and the general clustering or classification methods are difficult to achieve good results. Therefore, this paper proposes an early warning for course learning method based on SMOTE and OCSVM. First, collect and preprocess students’ college entrance examination information and online course learning information data. Second, use SMOTE algorithm to expanding the samples. Then, the OCSVM model is designed, the Gaussian kernel function is used, and the Lagrange multiplier is used to solve the optimization problem for the optimization objective. The qualified student samples are selected for learning, and the classifier is trained, so as to classify the student data and realize the early warning of course learning. Select recall and F1_Score to evaluate the model, and comparative experiments are carried out. From the experiment, it is clear that in most cases, the method proposed in this paper is superior to the original sample and traditional methods in recall rate and F1_Score.

中文翻译：

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基于SMOTE和OCSVM的课程学习预警方法

随着大数据、人工智能等相关技术在教育领域的应用，利用机器学习对课程学习进行预警已成为提高教学质量的有效手段。但在预警场景中，样本明显少于普通样本，一般的聚类或分类方法很难取得好的效果。因此，本文提出了一种基于SMOTE和OCSVM的课程学习预警方法。一是收集和预处理学生高考信息和在线课程学习信息数据。其次，使用 SMOTE 算法扩展样本。然后，设计OCSVM模型，使用高斯核函数，拉格朗日乘子用于求解优化目标的优化问题。选取合格的学生样本进行学习，训练分类器，对学生数据进行分类，实现课程学习的预警。选择recall和F1_Score对模型进行评估，并进行对比实验。从实验中可以看出，在大多数情况下，本文提出的方法在召回率和 F1_Score 上都优于原始样本和传统方法。