Department of Computer Science, College of Science, Northern Border University, Arar, Saudi Arabia.
Faculty of Computing and Informatics, Universiti Malaysia Sabah (UMS), Labuan, Malaysia.
Comput Intell Neurosci. 2022 May 9;2022:4151487. doi: 10.1155/2022/4151487. eCollection 2022.
Student performance is crucial to the success of tertiary institutions. Especially, academic achievement is one of the metrics used in rating top-quality universities. Despite the large volume of educational data, accurately predicting student performance becomes more challenging. The main reason for this is the limited research in various machine learning (ML) approaches. Accordingly, educators need to explore effective tools for modelling and assessing student performance while recognizing weaknesses to improve educational outcomes. The existing ML approaches and key features for predicting student performance were investigated in this work. Related studies published between 2015 and 2021 were identified through a systematic search of various online databases. Thirty-nine studies were selected and evaluated. The results showed that six ML models were mainly used: decision tree (DT), artificial neural networks (ANNs), support vector machine (SVM), K-nearest neighbor (KNN), linear regression (LinR), and Naive Bayes (NB). Our results also indicated that ANN outperformed other models and had higher accuracy levels. Furthermore, academic, demographic, internal assessment, and family/personal attributes were the most predominant input variables (e.g., predictive features) used for predicting student performance. Our analysis revealed an increasing number of research in this domain and a broad range of ML algorithms applied. At the same time, the extant body of evidence suggested that ML can be beneficial in identifying and improving various academic performance areas.
学生表现对高等院校的成功至关重要。特别是，学业成绩是评价顶尖大学的指标之一。尽管有大量的教育数据，但准确预测学生表现变得更加具有挑战性。主要原因是各种机器学习 (ML) 方法的研究有限。因此，教育工作者需要探索有效的工具来建模和评估学生表现，同时认识到弱点以提高教育成果。本研究调查了预测学生表现的现有 ML 方法和关键特征。通过对各种在线数据库进行系统搜索，确定了 2015 年至 2021 年期间发表的相关研究。选择并评估了 39 项研究。结果表明，主要使用了六种 ML 模型：决策树 (DT)、人工神经网络 (ANNs)、支持向量机 (SVM)、K-最近邻 (KNN)、线性回归 (LinR) 和朴素贝叶斯 (NB)。我们的结果还表明，ANN 优于其他模型，具有更高的准确度水平。此外，学术、人口统计学、内部评估以及家庭/个人属性是用于预测学生表现的最主要的输入变量（例如，预测特征）。我们的分析揭示了该领域研究的数量不断增加，以及应用的广泛的 ML 算法。同时，现有的证据表明，ML 可以有助于识别和改善各个学术表现领域。
