Academic Achievement Predicting Model for Admission in Master Levels

The aim of the present study was to identify factors influencing the academic achievement of students admitted to postgraduate courses (MS) in engineering and management fields in e-learning courses and compile them in the form of a prediction model.
This study performed with a correlation design. The participants were all of accepted students in the e-learning center of Iran University of Science and Technology (IUST) at postgraduate level, who had recorded formal marks at least in one semester, by the end of the second semester of the academic year 2012-13. The method of entering to the postgraduate course, the kind of university in the graduate program(BS), BS major, and BS grade point average (GPA) for each of participants, have taken as predictor variables and the grade point average (GPA) of the lessons in MS has considered as the criterion variable. Data were analyzed using linear regression model.
Among the predictor variables entered into the analysis; BS major (Engineering), admission through university entrance exam (with negative weight), BS grade point average, BS university type (governmental) and BS major (Science), are respectively identified as the most influential factors or predictors of success in post graduate course.
Conclusion and Noticing the relatively low prediction power of the current variables set, some more powerful predictors should be applied if an accurate prediction for academic achievement is expected.