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Multi-Criteria Decision-Making for Evaluation of Student Academic Performance Based on Objective Weights

机译:基于目标权重的学生评估学习成绩的多标准决策

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Student academic evaluation is part of the learning process in order to control the student's learning progress. The evaluation will show whether the student will pass or fail and for an instructor to guide for future evaluations on performance. There are criteria such as student's gender, student's age when they are registered in university, student's 1st semester GPA, etc., which exist in academic data can be utilized to get the student academic performance using multicriteria decision making. Multi-Objective Optimization by Ratio Analysis (MOORA) and Simple Multi-Attribute Rating (SMART) was two simple technique in multi-criteria decision making that the criteria weight can be determined objectively using entropy and gain values. This paper tries to evaluate the student academic performance using MOORA and SMART with criteria weight and sub-criteria weight resulted from entropy and gain. Decision output out of MOORA and SMART then compared with actual data using confusion matrix to discover the performance of those criteria and sub-criteria weight. The result showed that the performance of criteria weight with accuracy was 60.9 percent and the criteria of fourth-grade point average have the biggest impact on student academic evaluation with 0.1589 of weight. The result of this research can be used to help the instructor to determine the weight of student criteria for future recommendations and evaluations on student performance.
机译:学生学术评估是学习过程的一部分,目的是控制学生的学习进度。评估将显示学生是否会通过或不及格,并由一名指导老师为将来的表现评估提供指导。学术数据中存在诸如学生的性别,学生在大学注册时的年龄,学生的第一学期GPA等标准,可以使用多标准决策来利用学生中的学业成绩。通过比率分析(MOORA)和简单多属性评分(SMART)进行的多目标优化是多准则决策中的两种简单技术,可以使用熵和增益值客观地确定准则权重。本文尝试使用MOORA和SMART评估学生的学业成绩,其中标准权重和子准则权重是由熵和增益产生的。然后,使用混淆矩阵将MOORA和SMART的决策输出与实际数据进行比较,以发现这些标准和子标准权重的性能。结果表明,标准权重的准确度为60.9%,四年级平均分的标准对学生的学业评价影响最大,权重为0.1589。这项研究的结果可用于帮助教师确定学生标准的权重,以便将来对学生的表现进行推荐和评估。

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