Prediction of Student Academic Performance Using a Hybrid 2D CNN Model
Prediction of Student Academic Performance Using a Hybrid 2D CNN Model
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DOI：
10.3390/electronics11071005
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发表时间：
2022-04-01
期刊：
影响因子：
2.9
通讯作者：
Ball, John E.
中科院分区：
文献类型：
--
作者：
Poudyal, Sujan;Mohammadi-Aragh, Mahnas J.;Ball, John E.
应用数据挖掘技术来分析教育数据和改善学习的机会正在增加。机构技术、电子学习资源以及在线和虚拟课程正在产生大量数据。这些数据可以被教育工作者用来分析和了解学生的学习行为。获得的数据是必须分析的原始数据，需要教育数据挖掘来预测有关学生的有用信息，例如学习成绩等。许多研究人员使用传统的机器学习来预测学生的学习成绩，在教学领域的背景下，对卷积神经网络（CNN）的架构进行的研究很少。我们通过结合两种不同的2D CNN模型来构建混合2D CNN模型来预测学习成绩。我们的样本包含1D数据，因此我们将其转换为2D图像数据以测试混合模型的性能。我们比较了我们的模型与不同的传统基线模型的性能。我们的模型在准确性方面优于基线模型，如k-最近邻，朴素贝叶斯，决策树和逻辑回归。
Opportunities to apply data mining techniques to analyze educational data and improve learning are increasing. A multitude of data are being produced by institutional technology, e-learning resources, and online and virtual courses. These data could be used by educators to analyze and understand the learning behaviors of students. The obtained data are raw data that must be analyzed, requiring educational data mining to predict useful information about students, such as academic performance, among other things. Many researchers have used traditional machine learning to predict the academic performance of students, and very little research has been conducted on the architecture of convolutional neural networks (CNNs) in the context of the pedagogical domain. We built a hybrid 2D CNN model by combining two different 2D CNN models to predict academic performance. Our sample comprised 1D data, so we transformed it to 2D image data to test the performance of our hybrid model. We compared the performance of our model with that of different traditional baseline models. Our model outperformed baseline models, such as k-nearest neighbor, naive Bayes, decision trees, and logistic regression, in terms of accuracy.