Teaching and Learning process of an educational institution needs to be monitored and effectively analysed for enhancement. Teaching and Learning is a vital element for an educational institution. It is also one of the criteria set by majority of the Accreditation Agencies around the world. Learning analytics and Educational Data Mining are relatively new. Learning analytics refers to the collection of large volume of data about students in an educational setting and to analyse the data to predict the students' future performance and identify risk. Educational Data Mining (EDM) is develops methods to analyse the data produced by the students in educational settings and these methods helps to understand the students and the setting where they learn. Aim of this research is to collect large collection of data on students' performance in their assessment to discover the students at risk of failing the final exam. This analysis will help to understand how the students are progressing. The proposed research aimed to utilize the result of the analysis to identify the students at risk and provide recommendations for improvement. The proposed research aimed to collect and analyse the result of the assessment at the course level to enhance the teaching and learning process. The research aimed to discuss two feature selection techniques namely information gain and gain ratio and adopted to use gain ratio as the feature selection technique.
翻译:教育机构的教学和学习过程需要加以监测和有效分析,以提高教学质量; 教学和学习是教育机构的一个关键要素,也是全世界大多数认证机构制定的标准之一; 学习分析和教育数据挖掘比较新; 学习分析是指在教育环境中收集有关学生的大量数据,并分析数据,以预测学生的未来表现和查明风险; 教育数据挖掘(EDM)正在开发分析学生在教育环境中生成的数据的方法,这些方法有助于了解学生及其学习环境; 这项研究的目的是收集大量关于学生在评估评估中成绩的数据,以发现有失最后考试风险的学生; 这一分析将有助于了解学生的进展情况; 拟议的研究旨在利用分析结果确定有风险的学生,并提出改进建议; 拟议的研究旨在收集和分析课程评估的结果,以加强教学和学习过程; 研究的目的是讨论两种特征选择技术,即信息获取率和获得率,以及将获得率用作选择方法。