Estimating the flow state of students in a course allows evaluating their sentimental state and the challenges they are facing. In e-learning platforms, the evaluation of flow state is a complex task because it depends on the ability to extract the parameters that better reflect the activity and effort of students. In this scope, the current study proposes a method based on flow theory aiming to provide information about the students' flow state in a course that is taught in an e-learning environment. First, the interaction of students with an e-learning platform that comprises classical e-learning pages and a timeline tool is analyzed, using activity heatmaps and deep neural networks. Then, by taking also in account their grades, the flow state of students is calculated. The resulted data are validated with a statistical analysis that also utilizes student surveys. In order to guarantee that this method is applicable to various profiles, students from different faculties participated in this study. In a period where education is rapidly adapting to online lectures and e-learning platforms, the estimation of student's flow state in e-learning environments can provide useful feedback and data to students and educators.

评估学生在课程中的流动状态可以评估他们的情感状态和面临的挑战。在电子学习平台中，对流动状态的评估是一项复杂的任务，因为它取决于提取能够更好地反映学生的活动和努力的参数的能力。在此范围内，当前的研究提出了一种基于流动理论的方法，旨在在电子学习环境中教授的课程中提供有关学生流动状态的信息。首先，利用活动热图和深度神经网络，分析了学生与包括经典电子学习页面和时间轴工具的电子学习平台的互动。然后，通过考虑他们的成绩，计算学生的流动状态。通过统计分析验证结果数据，该统计分析也利用学生调查。为了确保此方法适用于各种配置文件，来自不同系的学生参加了这项研究。在教育迅速适应在线讲座和电子学习平台的时期，对电子学习环境中学生的流动状态的估计可以为学生和教育者提供有用的反馈和数据。