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The Application of KNN Algorithm Based on Time Factor in the Warning Mechanism of Dismissal in Colleges and Universities

机译:基于时间因素的KNN算法在高校离职预警机制中的应用

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Many colleges and universities have employed a mechanism to dismiss students based on their academic status. However, traditional criterions only evaluate students' scores and credits without being conscious of the causes of poor academic performance, such as poor psychological health. In order to promote educational equality, we propose a method that evaluates students' psychosomatic well-being based on their behaviors. Our method will offer educators an opportunity to provide timely counseling services to students who bear psychological sufferings. First of all, we examined the possible indicators of students' psychological conditions and selected some features by considering behaviors and psychology. Then, we used the improved KNN algorithm combining with factors of months to predict which student will fail to complete their studies in the universities based on historical data. From the above analysis, we aim at creating a system that monitors the students' behaviors and detects their negative emotions. The system will help the universities to cope with students who are struggling in a timely manner and create a healthier campus environment.
机译:许多高校采用了一种根据学生的学业状况将其解雇的机制。但是,传统的标准仅评估学生的分数和学分,而没有意识到学习成绩差的原因,例如心理健康差。为了促进教育平等,我们提出了一种根据学生的行为评估其心身健康的方法。我们的方法将为教育工作者提供机会,为遭受心理痛苦的学生提供及时的咨询服务。首先,我们研究了学生心理状况的可能指标,并通过考虑行为和心理学选择了一些特征。然后,我们使用改进的KNN算法结合几个月的因子,根据历史数据预测哪个学生将无法完成大学学习。通过以上分析,我们旨在创建一个监控学生行为并检测他们的负面情绪的系统。该系统将帮助大学及时应对正在苦苦挣扎的学生,并创造一个更健康的校园环境。

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