Prediction of Student Performance in Academic and Military Learning Environment: Use of Multiple Linear Regression Predictive Model and Hypothesis Testing

Abstract

The variance in students’ academic performance in a civilian institute and in a military technological institute could be linked to the environment of the competition available to the students. The magnitude of talent, domain of skills and volume of efforts students put are identical in both type of institutes. The significant factor is the physical training, students undergo in a military college. It is important to couple the dominating factor which is academic perceivable effort under a different environment with each students learning capability. This paper determine whether there is a relationship between students’ performance and influencing factors like academic aptitude, military or physical training, and the time spent on training need analysis (TNA) modules. A sample of 242 first year- undergraduate students from four different engineering programs (Marine, System, Civil, and Aeronautical) at Military College was used to explore this relationship. The multiple regression model used for predicting the students’ performance is adequate for independent variables of aptitude test score, time spent in physical training, and time spent in TNA modules. The values of R2 indicate that at least one of the predictor variables contributes to information for the prediction of the students’ performance. The model makes it possible to predict moderately the possibility of attrition in engineering program. This study verifies that military academy has a very defined and directed core engineering course load and TNA course load which every student must take. Therefore, choice of specific discipline have less impact than at civilian institutions. The early detection of students at academic risk is a useful instrument that can help to design mentoring strategies right from the end of admission process.