Bayesian multiple membership multiple classification logistic regression model on student performance with random effects in university instructors and majors.

ABSTRACT: Educational success measured by retention leading to graduation is an essential component of any academic institution. As such, identifying the factors that contribute significantly to success and addressing those factors that result in poor performances are important exercises. By success, we mean obtaining a semester GPA of 3.0 or better and a GPA of 2.0 or better. We identified these factors and related challenges through analytical models based on student performance. A large dataset obtained from a large state university over three consecutive semesters was utilized. At each semester, GPAs were nested within students and students were taking classes from multiple instructors and pursuing a specific major. Thus, we used multiple membership multiple classification (MMMC) Bayesian logist

SUBMITTER: Arreola EV 

PROVIDER: S-EPMC6992165 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature