Hybrid Feature Extraction Model to Categorize Student Attention Pattern and Its Relationship with Learning
Hybrid Feature Extraction Model to Categorize Student Attention Pattern and Its Relationship with Learning
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DOI：
10.3390/electronics11091476
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发表时间：
2022-05
期刊：
影响因子：
2.9
通讯作者：
Sujan Poudyal;M. Mohammadi-Aragh;J. Ball
中科院分区：
文献类型：
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作者：
Sujan Poudyal;M. Mohammadi-Aragh;J. Ball
教学技术、电子学习资源和在线课程的增加为教育领域的数据挖掘和学习分析创造了机会。从这个领域获得了大量的数据，这些数据可以被分析和解释，以便教育工作者了解学生的注意力。在学生面前有自己的计算机的教室里，教师了解学生是否在专心是很重要的。我们收集了任务上和任务外的数据来分析学生的注意行为。教育数据挖掘从教育记录中提取隐藏的信息，我们正在使用它来对学生的注意力模式进行分类。混合方法用于组合各种技术，如分类、回归或特征提取。在我们的工作中，我们结合了两种特征提取技术：主成分分析和线性判别分析。提取的特征由线性支持向量机和核支持向量机用于注意力模式的分类。将分类结果与线性支持向量机和核支持向量机进行了比较。我们的混合方法在准确率、精确度、召回率、F1和kappa方面都取得了最好的结果。此外，我们还将注意力与学习联系起来。在这里，学习与考试和期末课程成绩相对应。采用皮尔逊相关系数和p值来确定年级和注意之间的相关性。
The increase of instructional technology, e-learning resources, and online courses has created opportunities for data mining and learning analytics in the pedagogical domain. A large amount of data is obtained from this domain that can be analyzed and interpreted so that educators can understand students’ attention. In a classroom where students have their own computers in front of them, it is important for instructors to understand whether students are paying attention. We collected on- and off-task data to analyze the attention behaviors of students. Educational data mining extracts hidden information from educational records, and we are using it to classify student attention patterns. A hybrid method is used to combine various techniques like classifications, regressions, or feature extraction. In our work, we combined two feature extraction techniques: principal component analysis and linear discriminant analysis. Extracted features are used by a linear and kernel support vector machine (SVM) to classify attention patterns. Classification results are compared with linear and kernel SVM. Our hybrid method achieved the best results in terms of accuracy, precision, recall, F1, and kappa. Also, we correlated attention with learning. Here, learning corresponds to tests and a final course grade. For determining the correlation between grades and attention, Pearson’s correlation coefficient and p-value were used.