SAPM:基于ANFIS的学生学习成绩指标预测

N. M. Zuviria, S. L. Mary, V. Kuppammal
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引用次数: 9

摘要

本文提出了一种基于学生在定期评估测试中的表现、出勤率和问题集复杂性来评估学生学业成绩指标的方法。这些是决定学生学习效率评价的主要特征。这些指标的影响在预测学生的最终成绩方面起着重要作用。自适应神经模糊推理系统的应用有助于建立学生学业成绩评价框架的模型。这种方法的结果可以用来根据他们的学术技能对学生进行分类,并有助于预测他们在期末考试中成功的概率。
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SAPM: ANFIS based prediction of student academic performance metric
A methodology for evaluating the academic performance metric of students is proposed in this paper based on their performance in periodic assessment tests, attendance and complexity of the question set. These are the major features determining the students learning efficiency evaluation. The impact of these metrics plays a major role in predicting the final grade of a student. The application of adaptive neuro fuzzy inference system helps to model the frame work for evaluating Student Academic Performance Metric(SAPM). The outcome of this methodology can be used to classify the students based on their academic skill and helpful in predicting the probability of their success in the final examinations.
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