Student modeling is one of the most important processes in adaptive systems. Although learning is individual, a model can be created based on patterns in student behavior. Since a student model can be created for more than one student, the use of machine learning techniques in student modeling is increasing. Artificial neural networks (ANNs), which form one group of machine learning techniques, are among the methods most frequently used in learning environments. Convolutional neural networks (CNNs), which are specific types of these networks, are used effectively for complex problems such as image processing, computer vision and speech recognition. In this study, a student model was created using a CNN due to the complexity of the learning process, and the performance of the model was examined. The student modeling technique used was named LearnerPrints. The navigation data of the students in a learning management system were used to construct the model. Training and test data were used to analyze the performance of the model. The classification results showed that CNNs can be used effectively for student modeling. The modeling was based on the students’ achievement and used the students’ data from the learning management system. The study found that the LearnerPrints technique classified students with an accuracy of over 80%.

学生建模是自适应系统中最重要的过程之一。尽管学习是个人的，但可以根据学生行为的模式来创建模型。由于可以为一个以上的学生创建学生模型，因此在学生建模中对机器学习技术的使用正在增加。构成一组机器学习技术的人工神经网络（ANN）是学习环境中最常用的方法之一。卷积神经网络（CNN）是这些网络的特定类型，可有效用于复杂问题，例如图像处理，计算机视觉和语音识别。在这项研究中，由于学习过程的复杂性，使用CNN创建了一个学生模型，并检查了模型的性能。使用的学生建模技术被称为LearnerPrints。使用学习管理系统中学生的导航数据来构建模型。训练和测试数据用于分析模型的性能。分类结果表明，CNN可以有效地用于学生建模。该建模基于学生的成绩，并使用了来自学习管理系统的学生数据。研究发现，LearnerPrints技术对学生进行分类的准确率超过80％。该建模基于学生的成绩，并使用了来自学习管理系统的学生数据。研究发现，LearnerPrints技术对学生进行分类的准确率超过80％。该建模基于学生的成绩，并使用了来自学习管理系统的学生数据。研究发现，LearnerPrints技术对学生进行分类的准确率超过80％。