Students' performance via satisfiability reverse analysis method with Hopfield Neural Network

Abstract

This finding presents a logic mining technique to model student's performance in terms of specialized attributes. In this study, k Satisfiability based reverse analysis method (kSATRA) will be proposed to extract the logical relationship among key student's attribute and will be used to classify or project the outcome of the current and future performance trend. The performance of the studied trends was narrowed down to three critical components: difficulties towards assigned subject, the ability to achieve the threshold marks and the decision to continue their study to higher level education. kSATRA is a method that utilized the beneficial feature of Hopfield Neural Network and k Satisfiability representation. The dataset used in this study includes data from several schools which contain insightful features. The robustness of kSATRA in extracting logical rule in student's performance will be evaluated based on the root mean square error (RMSE), mean absolute error (MAE) and mean percentage error (MAPE). The result obtained from the computer simulation demonstrates the effectiveness of kSATRA in representing the performance of the students.