Predictive models, as an idea, to advance the secondary to tertiary transition in science courses

Abstract

Investigating the transition between the secondary and the tertiary levels is a main theme in mathematics and science education. More so, this paper considers the transition that intersects with the after-effects of COVID-19, or the transition together with an educational context dominated by sociocultural differences and educational disadvantages. With this knowledge in mind, we investigated the effects of predictive mathematical models (multiple regression, logistic regression, and decision trees) to predict <i>at-risk</i> students at three time intervals (weeks one, three, and seven) in the semester. The idea was implemented with a first-year life science class of 130 students. Variables from an academic readiness questionnaire along with early assessment grades were used to build these models. Through a Monte Carlo cross validation method, the performance of the executed predictive models was assessed, and limitations were reported. We argue that the results obtained from predictive models can support both lecturers and students in the transition phase. The idea can be expanded to other courses in STEM fields and other educational contexts.