论文标题
通过矩阵恢复探索学生的共同和个人特征
Exploring Common and Individual Characteristics of Students via Matrix Recovering
论文作者
论文摘要
平衡小组教学和个人指导是教育领域的重要问题。这个问题背后的性质是探索由多个学生共享的共同特征和每个学生的个人特征。事实证明，双簇方法成功地检测有意义的模式，目的是根据学生的特征来驱动小组说明。但是，这些方法忽略了学生的个人特征，因为它们仅关注学生的共同特征。在本文中，我们提出了一个框架，以同时检测学生的群体特征和个人特征。我们假设学生的特征矩阵由两个部分组成：一个是代表学生共同特征的低级矩阵；另一个是代表学生个人特征的稀疏矩阵。因此，我们将平衡问题视为矩阵恢复问题。实验结果显示了我们方法的有效性。首先，它可以检测出与最先进的双浮游算法相媲美的有意义的浮标。其次，它可以同时确定每个学生的个人特征。我们的算法的源代码和真实数据集都可以根据要求提供。
Balancing group teaching and individual mentoring is an important issue in education area. The nature behind this issue is to explore common characteristics shared by multiple students and individual characteristics for each student. Biclustering methods have been proved successful for detecting meaningful patterns with the goal of driving group instructions based on students' characteristics. However, these methods ignore the individual characteristics of students as they only focus on common characteristics of students. In this article, we propose a framework to detect both group characteristics and individual characteristics of students simultaneously. We assume that the characteristics matrix of students' is composed of two parts: one is a low-rank matrix representing the common characteristics of students; the other is a sparse matrix representing individual characteristics of students. Thus, we treat the balancing issue as a matrix recovering problem. The experiment results show the effectiveness of our method. Firstly, it can detect meaningful biclusters that are comparable with the state-of-the-art biclutering algorithms. Secondly, it can identify individual characteristics for each student simultaneously. Both the source code of our algorithm and the real datasets are available upon request.
