Technology and innovation empower higher educational institutions (HEI) to use different types of learning systems—video learning is one such system. Analyzing the footprints left behind from these online interactions is useful for understanding the effectiveness of this kind of learning. Video-based learning with flipped teaching can help improve student’s academic performance. This study was carried out with 772 examples of students registered in e-commerce and e-commerce technologies modules at an HEI. The study aimed to predict student’s overall performance at the end of the semester using video learning analytics and data mining techniques. Data from the student information system, learning management system and mobile applications were analyzed using eight different classification algorithms. Furthermore, data transformation and preprocessing techniques were carried out to reduce the features. Moreover, genetic search and principle component analysis were carried out to further reduce the features. Additionally, the CN2 Rule Inducer and multivariate projection can be used to assist faculty in interpreting the rules to gain insights into student interactions. The results showed that Random Forest accurately predicted successful students at the end of the class with an accuracy of 88.3% with an equal width and information gain ratio.

中文翻译：

---

使用视频学习分析和数据挖掘技术预测高等教育机构的学生表现

技术和创新使高等教育机构 (HEI) 能够使用不同类型的学习系统——视频学习就是这样一种系统。分析这些在线互动留下的足迹有助于理解这种学习的有效性。翻转教学的视频学习有助于提高学生的学习成绩。这项研究是对 772 名在 HEI 注册电子商务和电子商务技术模块的学生进行的。该研究旨在使用视频学习分析和数据挖掘技术预测学生在学期末的整体表现。来自学生信息系统、学习管理系统和移动应用程序的数据使用八种不同的分类算法进行分析。此外，进行数据转换和预处理技术以减少特征。此外，还进行了遗传搜索和主成分分析以进一步减少特征。此外，CN2 规则诱导器和多元投影可用于帮助教师解释规则以深入了解学生互动。结果表明，随机森林在同等宽度和信息增益比的情况下准确预测了课堂结束时的成功学生，准确率为88.3%。