Estimating student persona through factorization of learning portfolio

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 assumes students in a persona take similar learning behavior, even in different years. To determine how strongly each student learning in the current programming course belongs to a specific persona, it examines the similarity of her learning portfolio to that of past students. It decomposes a matrix of each student portfolio into the product of 2 matrices; a matrix representing the weight of each student belonging to every persona and a matrix of persona learning behavior, using the nonnegative-matrix factorization. This paper illustrates the method to figure out the matrices effectively.

Archetype-Based Modeling of Persona for Comprehensive Personality Computing from Personal Big Data

“…Similar to the research above into persona modeling, in this research the prevalent persona model is also applied, especially in its elements. In addition to the research focusing on persona modeling, Phuong, et al, estimated the motivation and learning strategy of students for the improvement of an educational course [ 24 ]. Additionally, Friess explored incorporating personae into the heuristic evaluation of websites [ 25 ].…”