This article proposes two innovative approaches, the one-channel learning image recognition and the three-channel learning image recognition, to convert student's course involvements into images for early warning predictive analysis. Multiple experiments with 5235 students and 576 absolute/1728 relative input variables were conducted to verify their effectiveness. The results indicate that both methods can significantly capture more at-risk students (the highest average recall rate is equal to 77.26%) than the following machine learning algorithms—support vector machine, random forest, and deep neural network—in the middle of the semester. In addition, the innovative approaches allow minor subtypes of at-risk student identification and provide visual insights for personalized interventions. Implications and future directions are also discussed in this article.

中文翻译：

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使用卷积神经网络识别学习图像以预警危险学生

本文提出了两种创新的方法，即单通道学习图像识别和三通道学习图像识别，以将学生的课程参与转化为图像，以进行预警预测分析。进行了5235名学生和576个绝对/ 1728个相对输入变量的多次实验，以验证其有效性。结果表明，与以下机器学习算法（支持向量机，随机森林和深度神经网络）相比，这两种方法都可以捕获更多的高风险学生（最高平均召回率等于77.26％）。学期。此外，创新的方法允许识别危险学生的次要类型，并为个性化干预提供视觉见解。