Abstract:
Nowadays, the education system has been changed from teacher centered approach to learner-centered approach. Thus, students’ related
features need to analyze to predict students’ academic performance to
provide the active learning in educational system. In this system, Multiple
Linear Regression (MLR) is applied to predict the students’ academic
performance on the UCI Machine Learning Repository’s student
performance data set. The student’s academic performance prediction model
is built by using Multiple Linear Regression method. The purpose of this
system is to predict the students’ final grade based on previous grades and
relevant features. Feature selection method is used to reduce the number of
input variables that are believed to be the most useful to a regression model
in order to predict the target variable. For evaluating the result of prediction
model performance applied on students’ academic performance model use
four different measures: accuracy, precision, recall and f-measure. This
system is implemented using C# programming language with Microsoft
Visual Studio IDE and Microsoft SQL Server as Database Engine.
