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This paper uses the binary logistic regression to show how exam policies affect students’ learning outcomes. Types of examinations employed by instructors are divided broadly into three, namely traditional, nontraditional, and project. Using data from an undergraduate business program, the study develops a binary logistic regression model predicting the effects of the three types of examinations on students’ learning outcomes. The results showed that the traditional (in-class) examinationhad the largest predictive powers on students’ learning outcomes. Nontraditional examination and project had significantly lesser predictive powers than traditional examination, with project having the least powers. The findings suggest, first, that instructors’ examination policies may be less impactful or have negative effects on learning outcomes; second, there can be a particular combination of traditional, nontraditional, and project examinations, which can most effectively boost students’ learning outcomes; third, students who participate in academic program with higher correctly classified estimates would be expected to acquire higher learning outcomes than students who participate in an academic program with significantly lower correctly classified estimates; fourth, examination policies can be deployed as a critical tool for students’ learning outcomes; and, fifth, a periodic evaluation of examination policies in an academic program may be useful.


This article was originally published in Creative Education, available at DOI: 10.4236/ce.2014.54027.

This work is licensed under the Creative Commons Attribution International License (CC BY 4.0)



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