Abstract—Estimation of motivation and learning strategy of
students is crucial for a teacher to engage them in programming.
Let us consider a persona, which is a virtual student
representing a student group similar in motivation and learning
strategy to learn programming. Personas enable the teacher to
predict student behavior during the programming education
course. The paper proposes a method to figure out the weight
each student belongs to a specific persona. It uses non negative
matrix factorization (NMF) to decompose a matrix of portfolio,
which is extracted from their real learning behavior, into the
product of 2 matrices. A matrix represents the weight of each
student belonging to certain personas. The other represents
persona features. For the NMF, determining persona feature
matrix is essential to achieve the good factorization. From the
learning behavior of 66 students, we found that the trends of
motivation features along the course, such as learning time, test
score, submissions before deadline is good indicator for the
feature matrix.
Index Terms—Programming course, motivation, learning
strategy, portfolio, persona, non-negative matrix
approximation.
Dinh Thi Dong Phuong and Hiromitsu Shimakawa are with the
Information and Engineering Dept. in Ritsumeikan University, Japan (e-mail:
phuong@de.is.ritsumei.ac.jp, simakawa@de.is.ritsumei.ac.jp).
Cite: Dinh Thi Dong Phuong and Hiromitsu Shimakawa, "Analyzing Learning Behavior of Student Persona toward Non-Negative Matrix Factorization," International Journal of Information and Education Technology vol. 5, no. 11, pp. 826-831, 2015.