Abstract:

The main objective of higher education is to provide quality education to students. An approach to achieving the highest quality of education is by discovering a model for prediction and prevention regarding the variables affecting the retention rates of students. Student retention is a crucial educational measurement metric, as retention rates accumulate as students re-enroll from one academic year to the next. Institutions obtain retention if it provides appropriate support and teaching methods among the various practices to prevent student deferment. This paper examines the validated learning metrics acquired using AssessMe, an advanced software solution, in the Programming I and II courses at the Rochester Institute of Technology in Croatia and presents a predictive model for student retention management. Given new records of incoming students, the predictive model can produce an accurate prediction identifying students who require additional support. Moreover, the software tool aids in facilitating students' personal development.

Keywords:

Learning validation, synchronous assessment, academic integrity, data mining, retention, higher education.