Abstract
This study focused on the statistical technique using the neural network, hybrid models and factor analysis on constructing the new factors affecting students learning styles of the survey done among university students in predicting academic performance. The data were collected using survey questionnaires and students’ academic records. The methodologies used were descriptive statistics, factor analysis, neural network and hybrid models technique using the following Learning algorithms; Levenberg-Marquardt (LM), Bayesian Regularization (BR), BFGS Quasi-Newton (BFG), Scaled Conjugate Gradient (SCG), Gradient Descent (GD) in artificial neural network model while for the second Hybrid model only the best two algorithms where use; Levenberg-Marquardt (LM), Bayesian Regularization (BR). The results showed ten new factors were successfully constructed using factor analysis and the proposed hybrid models show that though it took longer time and number of epochs to train the hybrid models by Bayesian Regularization Algorithms, and it gives more accurate predictions than both the Levenberg-Marquadrt, Scaled Conjugate Gradient, Gradient Descent and BFGS Quasi-Newton (BFG) Algorithms. In a nutshell, the finding indicates that Bayesian Regularization is the best learning algorithms in both Neural Network and Hybrid models for predicting students’ academic performance.
Highlights
The applications of Machine Learning methods to predict students' performance based on student's background and term examination performances has turn to be helpful for foreseeing the different performance in various level
It can be said that each group of variables represents a single underlying construct that is responsible for the observed correlations [3]
Validate and test the neural networks developed using the LM, BFGS Quasi-Newton (BFG), Gradient Descent (GD) and the Scaled Conjugate Gradient (SCG) algorithms, we have divided the data set in the following way: 70% of it for the training process, 15% for the validation process and the remaining 15% for the testing process
Summary
The applications of Machine Learning methods to predict students' performance based on student's background and term examination performances has turn to be helpful for foreseeing the different performance in various level Using such machine learning methods enables to timely predict the students who has a high chance of failing so that a remedy can be provided by a teacher to the student. The students’ academic performances are critical in ensuring that those significant roles the students’ play in the society are maintained This has motivated some higher institutions of learning to developed interest in predicting the paths of students, identifying which students will require assistance in order to graduate at the stipulated time or maintain their studies or even drop out of the school. This is brought about by the academic failure rate among students, which has fed to a large number of debates [2]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
