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Assessing the Impact of Class Attendance on Student?s Academic Performance using Data Mining

机译:使用数据挖掘评估班级出勤对学生学习成绩的影响

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Many institutions of learning encourage students to have good lecture attendance records. The belief is that an above average attendance rate will enhance student?s academic performance. However, very few studies have attempted to answer questions that relate to: the actual impact of good attendance record on student?s academic performance; the extent in quantitative terms of the effect of good attendance record on student?s academic performance. This study reports the findings from an experimental analysis of student?s attendance record and corresponding academic performance results using association rule mining. Based on the extracted patterns in rules from the five course assessed, it was discovered that the impact of class attendance on academic performance is very low. A student with >70 % attendance score can still fall into any grade between ?A-F?. This indicates that class attendance is not the major factor that determines student academic performance but other key factors such as the student participation in the class, personal study and group study. The result of this case study and the recommendations is expected to provide useful information for the managements of higher institutions of learning on appropriate perspective to adopt on class attendance policies and good motivation for distance and online learning programmes.
机译:许多学习机构都鼓励学生拥有良好的听课记录。相信高于平均水平的出勤率将提高学生的学习成绩。但是,很少有研究试图回答与以下问题有关的问题:良好的出勤记录对学生学习成绩的实际影响;从数量上讲,良好的出勤记录对学生学习成绩的影响程度。这项研究报告了使用关联规则挖掘对学生的出勤记录和相应的学业成绩进行实验分析的结果。根据从评估的五门课程中提取的规则模式,发现班级出勤对学习成绩的影响非常低。出勤率> 70%的学生仍然可以达到“ A-F”之间的任何等级。这表明上课率不是决定学生学习成绩的主要因素,而是其他关键因素,例如学生的课堂参与,个人学习和小组学习。该案例研究和建议的结果有望以适当的角度为高等学府的管理人员提供有用的信息,以采纳他们的课堂出勤政策以及远程和在线学习计划的良好动力。

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