Predicting the Final result of Student National Test with Extreme Learning Machine

Abstract

The level of student achievement is a benchmark of the quality assessment of a school. This student's assessment is based on the national final exam scores every year. When the national exam score increases, it will affect the number of students who will enroll in a school. It affects the number of classes to be opened in the registration of new student candidates. This study aims to predict student achievement based on the value of subjects that become the focus on the final national examination. One method of forecasting in the Artificial Neural Network (ANN) is the Extreme Learning Machine (ELM). The working principle in this method is basically the same as ANN method in general. Namely, there are input layer, hidden layer and output layer. By randomly assigning the input parameters, the ELM generates good generalization performance. By using 20-20-1 network architecture, this research has a result in a small RMSE value of 0.314.

Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

“…The non-linear relationship between cognitive and psychological variables that influence academic performance was analyzed by an ANN, which efficiently grouped students into different categories according to their level of expected performance [53]. Finally, an ELM (which is a particular type of ANN) predicted students' performance by considering the value of the subjects that focus on the final national exam [59].…”