A feature weighted support vector machine and artificial neural network algorithm for academic course performance prediction
A feature weighted support vector machine and artificial neural network algorithm for academic course performance prediction
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DOI：
10.1007/s00521-021-05962-3
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发表时间：
2021-04-20
影响因子：
6
通讯作者：
Peng, Yonghong
中科院分区：
文献类型：
--
作者：
Huang, Chenxi;Zhou, Junsheng;Peng, Yonghong
学术表现是一个全球通用的衡量标准，在全世界不同的教学和学习环境中使用，并被视为学习成果的量化指标。可靠地估计学生的学习成绩的能力是很重要的，可以帮助学术人员，以改善提供支持。然而，人们认识到，学习成绩估计是不平凡的，并受到多种因素的影响，包括学生的参与学习活动和他们的社会，地理和人口特征。本文探讨了利用人工智能开发预测学生成绩的可靠模型的机会。具体来说，我们提出了两步的学习成绩预测，使用特征加权支持向量机和人工神经网络（ANN）学习。一个功能加权SVM，其中的重要性不同的功能的结果是使用信息增益比计算，被用来执行粗粒度的二进制分类（通过，P1，或失败，P0）。随后，详细的分数水平被划分为D到A+，并且ANN学习分别用于P1和P0类的细粒度多类训练。实验和我们随后的消融研究，这是从两个葡萄牙中学的学生数据集进行，证明了这种混合方法的有效性。
Academic performance, a globally understood metric, is utilized worldwide across disparate teaching and learning environments and is regarded as a quantifiable indicator of learning gain. The ability to reliably estimate student's academic performance is important and can assist academic staff to improve the provision of support. However, it is recognized that academic performance estimation is non-trivial and affected by multiple factors, including a student's engagement with learning activities and their social, geographic, and demographic characteristics. This paper investigates the opportunity to develop reliable models for predicting student performance using Artificial Intelligence. Specifically, we propose two-step academic performance prediction using feature weighted support vector machine and artificial neural network (ANN) learning. A feature weighted SVM, where the importance of different features to the outcome is calculated using information gain ratios, is employed to perform coarse-grained binary classification (pass, P1, or fail, P0). Subsequently, detailed score levels are divided from D to A+, and ANN learning is employed for fine-grained, multi-class training of the P1 and P0 classes separately. The experiments and our subsequent ablation study, which are conducted on the student datasets from two Portuguese secondary schools, have proved the effectiveness of this hybridized method.