Student’s Success Prediction Model Based on Artificial Neural Networks (ANN) and A Combination of Feature Selection Methods

Abstract

The improvements in educational data mining (EDM) and machine learning motivated the academic staff to implement educational models to predict the performance of students and find the factors that increase their success. EDM faced many approaches for classifying, analyzing and predicting a student’s academic performance. This paper presents a model of prediction based on an artificial neural network (ANN) by implementing feature selection (FS). A questionnaire is built to collect students’ answers using LimeSurvey and google forms. The questionnaire holds a combination of 61 questions that cover many fields such as sports, health, residence, academic activities, social and managerial information. 161 students participated in the survey from two departments (Computer Science Department and Computer Information Systems Department), college of Computer Science and Information Technology, University of Basra. The data set is combined from two sources applications and is pre-processed by removing the uncompleted answers to produce 151 answers used in the model. Apart from the model, the FS approach is implemented to find the top correlated questions that affect the final class (Grade). The aim of FS is to eliminate the unimportant questions and find those which are important, besides improving the accuracy of the model. A combination of Four FS methods (Info Gain, Correlation, SVM and PCA) are tested and the average rank of these algorithms is obtained to find the top 30 questions out of 61 questions of the questionnaire. Artificial Neural Network is implemented to predict the grade (Pass (P) or Failed (F)). The model performance is compared with three previous models to prove its optimality.

Toward Predicting Student’s Academic Performance Using Artificial Neural Networks (ANNs)

“…Attendance, archived courses Student performance [31] University entrance examination score, the average overall score of high school graduation, examination, the elapsed time between graduating from high school and obtaining university admission, location of student's high school, type of high school attended, gender Students' academic performance. [32] 12 input variables, classified into academic, parent, person, managerial and social Student pass or fail [33] Exam results and other factors, such as the location of the student's high school and the student's gender Student performance [34] Socioeconomic variables, school type variables, student's previous achievement variables, tutor's expertise variables Student performance [35] Students' internet accessing details including the total length of internet time, active periods, traffic, college entrance examination scores represent the students' initial knowledge level and learning ability, book-borrowing numbers, and birth dates, first midterm examination scores…”

“…There is a need to understand student success and the factors that contribute to this success and the overall performance at all levels. All the studies predict the students' success or failure, predict the student's performance, and investigate the factors that improve their success [20,23,26,30,32,34]. Some studies aimed to compare the ANN and other techniques to reach the same goal: predicted student performance including Linear Regression (LR) [22].…”

“…The process is vital before the developed network is used to predict classes of outcomes in a new set of data. It can then be used to predict future outcomes in cases where the outcome is still unknown [20,24,29,32].…”

Deep Neural Network Model for Identification of Predictive Variables and Evaluation of Student’s Academic Performance

“…However, predicting whether a student will pass or fail does not provide us a clearer picture of their academic performance. Another key technical flaw is their failure to evaluate both internal and external factors when evaluating the total influence of predictor factors on student data [20]. As a result, conventional classifiers aren't very good at predicting kids' academic success based on historical data [21].…”

Global geographical climate impacts on the spread and death of COVID-19 in Asia and America

“…It is the preferred instrument for several predictive applications of data mining, due to its strength, versatility and simplicity. The predictive neural networks are principally helpful for applications with complex underlying mechanisms, such as wind speed prediction model [11], forecasting of student's success [12], predicting surface settlement [13], global solar radiation prediction [14], and many more. ANN is widely used in predictive applications [15], for instance, the radial basis function (RBF) and multilayer perceptron (MLP) networks, are controlled in a way that the known target variable values is compared to the model-predicted results.…”