Selection of Candidates in the Absence of a College Entrance Exam

Abstract

Given the exceptional circumstances of the COVID-19 pandemic and the problems faced in conducting mass student examinations, this article explores alternatives to an in-person exam to select candidates hoping to gain admission to higher education institutions, based on available information about students and their schools. Census-based information on entrance evaluations for six annual cohorts is used to obtain a predictive model that incorporates candidates’ average grades and the historical performance of their schools of origin, measured through EXANI II. The results allow us to identify a model able to correlate to .69 the estimated and actual scores; additional scenarios were proposed with greater levels of prediction (r= .86). A discussion is offered on the implications of using high school grades, in light of their power to predict academic performance in higher education.

Keywords

• academic achievement
• entrance examination
• admission requirements
• prior learning evaluation
• regression analysis