Exploring Common and Individual Characteristics of Students via Matrix
  Recovering
  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.
