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A Semi-Supervised Learning Approach for Predicting Student's Performance: First-Year Students Case Study

机译：一种预测学生表现的半监督学习方法：一年级学生案例研究

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摘要

Students performance is an essential part of a higher learning institution because one of the criteria for a high -quality university is based on its excellent record of academic achievements. The first-year of the lecture is the student period of laying the foundation that will affect academic success because first-year plays an important role in shaping the attitudes and performance of students in the following years. In this study, a semi-supervised learning approach is used to classify the performance of first-year students in the Department of Mathematics, Universitas Indonesia. Student performance will be divided into two categories, namely medium and high. The sample in this study consist of 140 first-year students with 27 features. There are two processes used i.e. clustering and the classification process. In the clustering process, the data is divided into three clusters using K-Means Clustering and the Naïve Bayes Classifier is chosen to classify it. The performance of the proposed algorithms is stated by accuracy, sensitivity, and specificity value i.e. 96%, 92.86%, and 100% respectively.
机译：学生表现是高等教育机构的重要组成部分，因为高质量大学的标准之一是基于其出色的学习成绩。讲座的第一年是学生时期，这将影响学术成就，因为第一年在塑造接下来几年学生的态度和表现方面起着重要作用。在这项研究中，使用半监督学习方法对印度尼西亚大学数学系一年级学生的表现进行分类。学生表现将分为两类，即中级和高级。本研究的样本包括140位具有27个功能的一年级学生。使用了两个过程，即聚类和分类过程。在聚类过程中，使用K均值聚类将数据分为三个聚类，并选择朴素贝叶斯分类器对其进行分类。所提出算法的性能由准确性，敏感性和特异性值（即96％，92.86％和100）表示 ％ 分别。

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