The outbreak of COVID-19 has caused significant disruption in all sectors and industries around the world. To tackle the spread of the novel coronavirus, the learning process and the modes of delivery had to be altered. Most courses are delivered traditionally with face-to-face or a blended approach through online learning platforms. In addition, researchers and educational specialists around the globe always had a keen interest in predicting a student’s performance based on the student’s information such as previous exam results obtained and experiences. With the upsurge in using online learning platforms, predicting the student’s performance by including their interactions such as discussion forums could be integrated to create a predictive model. The aims of the research are to provide a predictive model to forecast students’ performance (grade/engagement) and to analyse the effect of online learning platform’s features. The model created in this study made use of machine learning techniques to predict the final grade and engagement level of a learner. The quantitative approach for student’s data analysis and processing proved that the Random Forest classifier outperformed the others. An accuracy of 85% and 83% were recorded for grade and engagement prediction respectively with attributes related to student profile and interaction on a learning platform.

COVID-19 的爆发已对全球所有部门和行业造成重大破坏。为了应对新型冠状病毒的传播，必须改变学习过程和授课方式。大多数课程传统上都是通过在线学习平台以面对面或混合方式授课。此外，全球的研究人员和教育专家一直对根据学生的信息（例如以前的考试成绩和经历）来预测学生的表现有着浓厚的兴趣。随着使用在线学习平台的热潮，可以集成通过包括他们的互动（例如讨论论坛）来预测学生的表现以创建预测模型。该研究的目的是提供一个预测模型来预测学生的表现（成绩/参与度）并分析在线学习平台功能的影响。本研究中创建的模型利用机器学习技术来预测学习者的最终成绩和参与度。学生数据分析和处理的量化方法证明随机森林分类器优于其他分类器。成绩和参与度预测的准确度分别为 85% 和 83%，其属性与学习平台上的学生概况和互动相关。学生数据分析和处理的量化方法证明随机森林分类器优于其他分类器。成绩和参与度预测的准确度分别为 85% 和 83%，其属性与学习平台上的学生概况和互动相关。学生数据分析和处理的量化方法证明随机森林分类器优于其他分类器。成绩和参与度预测的准确度分别为 85% 和 83%，其属性与学习平台上的学生概况和互动相关。