PDF(Pubmed)
Abstract：
The COVID-19 pandemic has had a significant impact on students\' academic performance. The effects of the pandemic have varied among students, but some general trends have emerged. One of the primary challenges for students during the pandemic has been the disruption of their study habits. Students getting used to online learning routines might find it even more challenging to perform well in face to face learning. Therefore, assessing various potential risk factors associated with students low performance and its prediction is important for early intervention. As students\' performance data encompass diverse behaviors, standard machine learning methods find it hard to get useful insights for beneficial practical decision making and early interventions. Therefore, this research explores regularized ensemble learning methods for effectively analyzing students\' performance data and reaching valid conclusions. To this end, three pruning strategies are implemented for the random forest method. These methods are based on out-of-bag sampling, sub-sampling and sub-bagging. The pruning strategies discard trees that are adversely affected by the unusual patterns in the students data forming forests of accurate and diverse trees. The methods are illustrated on an example data collected from university students currently studying on campus in a face-to-face modality, who studied during the COVID-19 pandemic through online learning. The suggested methods outperform all the other methods considered in this paper for predicting students at the risk of academic failure. Moreover, various factors such as class attendance, students interaction, internet connectivity, pre-requisite course(s) during the restrictions, etc., are identified as the most significant features.
摘要：
COVID-19大流行对学生的学业成绩产生了重大影响。大流行的影响在学生中有所不同，但是一些大趋势已经出现。大流行期间学生面临的主要挑战之一是他们学习习惯的破坏。习惯了在线学习程序的学生可能会发现在面对面的学习中表现良好更具挑战性。因此,评估与学生成绩低下相关的各种潜在危险因素及其预测对于早期干预很重要。由于学生的表现数据包含不同的行为，标准的机器学习方法发现很难获得有益的实际决策和早期干预的有用见解。因此,这项研究探索了正则化集成学习方法，以有效地分析学生的表现数据并得出有效的结论。为此,对随机森林方法实施了三种修剪策略。这些方法基于袋外取样，子采样和子装袋。修剪策略会丢弃受学生数据中异常模式不利影响的树木，从而形成准确而多样的树木森林。这些方法是在从目前以面对面的方式在校园学习的大学生中收集的示例数据中进行说明的，他在COVID-19大流行期间通过在线学习进行了研究。建议的方法优于本文考虑的所有其他方法，可以预测有学术失败风险的学生。此外，各种因素，如上课出勤率，学生互动,互联网连接，限制期间的先决条件课程，等。,被确定为最重要的特征。
