教育数据挖掘与学习分析——教育助教与教学学习
Educational Data Mining and Learning Analytics - Educational Assistance for Teaching and Learning
AI总结：
本文研究如何通过教育数据挖掘和学习分析技术，收集学生表现数据以识别潜在失败风险，并通过特征选择方法提升教学与学习效果。
摘要：
教育机构的教学与学习过程需要被监控和有效分析以促进改进。教学与学习是教育机构的重要组成部分，也是全球多数认证机构的评估标准。学习分析和教育数据挖掘是相对较新的领域。学习分析指的是收集教育环境中学生的大规模数据，并分析这些数据以预测学生未来的表现并识别风险。教育数据挖掘（EDM）则发展了分析学生在教育环境中产生的数据的方法，这些方法有助于理解学生及其学习环境。本研究的目标是收集大量学生在评估中的表现数据，以发现可能在最终考试中失败的学生。此分析将帮助理解学生的学习进度。本研究旨在利用分析结果来识别有风险的学生并提供改进建议。本研究还旨在收集和分析课程级别的评估结果以增强教学与学习过程。本研究旨在讨论两种特征选择技术，即信息增益和增益比率，并采用增益比率作为特征选择技术。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
