Comparison of MATLAB and SPSS Software in the Prediction of Academic Achievement with Artificial Neural Networks: Modeling for Elementary School Students (original) (raw)
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ARTIFICIAL NEURAL NETWORK (ANN) MODEL FOR PREDICTING STUDENTS’ ACADEMIC PERFORMANCE
The observed poor quality of graduates of some Nigerian Universities in recent times is traceable to non-availability of mechanism that would enable the University administrators to project into the future performance of the concerned students. This will guarantee the provision of better educational services as well as customize assistance according to students’ predicted level of performance. In this research, Artificial Neural Networks (ANNs) were used to develop a model for predicting the final grade of a university student before graduating such student. The data used in this study consists of thirty (30) randomly selected students in the Department of Computer Science, Tai Solarin University of Education in Ogun State, who have completed four academic sessions from the university. Test data evaluation showed that the ANN model is able to correctly predict the final grade of students with 92.7% accuracy. All ANN models used were trained and simulated using nntool of MATLAB (2008a) software. Index: Neural Network, Artificial Intelligence, Student Achievement Prediction, Student, Academic Performance
Predicting Student Academic Performance Using Artificial Neural Network
Journal of Review and Research in Sciences, 2021
Introduction: Predicting student academic performance plays an important role in academics. Classifying students using conventional techniques cannot give the desired level of accuracy, while doing it with the use of soft computing techniques may prove to be beneficial. Aim: This study aims to accurately predict and identify student academic performance using an Artificial Neural Network in educational institutions. Materials and Methods: Artificial Neural network was employed to compute the performance procedure over the MATLAB simulation tool. The performance of the Neural Network was evaluated by accuracy and Mean Square Error (MSE). This tool has a simple interface and can be used by an educator for classifying students and distinguishing students with low achievements or at-risk students who are likely to have low performance. Results: Findings revealed that the Neural network has the highest prediction accuracy by (98%) followed by the decision tree by (91%). Support vector machine and k-nearest neighbor had the same accuracy (83%), while naive Bayes gave lower prediction accuracy (76%). Conclusion: This work has helped to analyze the capabilities of an Artificial Neural Network in the accurate prediction of students' academic performance using Regression and feed-forward neural network (FFNN) as evaluation metrics.
This paper presents an approach for predicting student achievements using statistics and artificial neural networks (ANN), namely simple linear regression and radial basis function neural network (RBFNN) methods. The data is gained from 108 students from mathematics department in Islamic University, Bengkulu, Indonesia. The results of measurement are then compared to the value of the mean of square error (MSE). The results show that MSE 0.076 with model Y = 3.193 + 0.002 for simple linear regression and MSE 0.003, model Y = (1)T + (0.0021) with sum-squared error goal 0.01, and spread 1 for the RBFNN. The smallest MSE value indicates a good method for accuracy. Therefore, the RBFNN model illustrates the proposed best model to predict students’ achievement.
Toward Predicting Student’s Academic Performance Using Artificial Neural Networks (ANNs)
Applied Sciences, 2022
Student performance is related to complex and correlated factors. The implementation of a new advancement of technologies in educational displacement has unlimited potentials. One of these advances is the use of analytics and data mining to predict student academic accomplishment and performance. Given the existing literature, machine learning (ML) approaches such as Artificial Neural Networks (ANNs) can continuously be improved. This work examines and surveys the current literature regarding the ANN methods used in predicting students’ academic performance. This study also attempts to capture a pattern of the most used ANN techniques and algorithms. Of note, the articles reviewed mainly focused on higher education. The results indicated that ANN is always used in combination with data analysis and data mining methodologies, allowing studies to assess the effectiveness of their findings in evaluating academic achievement. No pattern was detected regarding selecting the input variabl...
This study aims to predict the final exam scores and pass/fail rates of the students taking the Basic Information Technologies – 1 (BIL101U) course in 2014-2015 and 2015-2016 academic years in the Open Education System of Anadolu University, through Artificial Neural Networks (ANN). In this research, data about the demographics, educational background, BIL101U course mid-term, final and success scores of 626,478 students was collected and purged. Data of 195,584 students, obtained after this process was analysed through Multilayer Perception (MLP) and Radial Basis Function (RBF) models. Sixteen different networks attained through the combination of ANN parameters were used to predict the final exam scores and pass/fail rates of the students. As a result of the analyses, it was found out that networks established through MLPs make more exact predictions. In the prediction of the final exam scores, it was determined that there is a low level of correlation between the actual scores and predicted scores. In the analyses for the prediction of pass/fail rates of the students, networks established through MLPs ensured more exact prediction results. Moreover, it was determined that the variables as mid-term exam scores, university entrance scores and secondary school graduation year were of highest importance in explaining the final exam scores and pass/fail rates of the students. It was found out that in the higher institutions serving for Open and Distance Learning, pass/fail state of the students can be predicted through ANN under favour of variables of students which have been found as most the important predictors.
1994
This paper explores the feasibility of neural computing methods such as artificial neural networks (ANNs) and abductory induction mechanisms (AIM) for use in educational measurement. ANNs and AIMS methods are contrasted with more traditi.onal statistical techniques, such as multiple regression and discriminant function analyses, for making classification or placement decisions in schools and colleges. Classification rates obtained with multiple regression and discriminant analysis were compared with ANN (back propagation) and AIM methods across a number uf plausible models of algebra proficiency that included measures of arithmetic ability, high school achievement, test anxiety, and gender. Analyses were conducted on a sample of 290 male and 310 female college freshmen for the entire sample and for each gender. At each stage 10 randomly selected subsets were used to train and test the neural computing methods. In general, ANN and AIM methods outperformed the more traditional methods. Results suggest that neural computing methods may lead to higher rates of classification accuracy, particularly when underlying models are nonlinear. Included are four tables, and one figure. (Contains 17 references.) (Author/SLD)
Predicting Students' Grade Scores Using Training Functions of Artificial Neural Network
The observed poor quality of graduates of some Nigerian Universities in recent times has been traced to non-availability of adequate mechanism. This mechanism is expected to assist the policy maker project into the future performance of students, in order to discover at the early stage, students who have no tendency of doing well in school. This study focuses on the use of artificial neural network (ANN) model for predicting students’ academic performance in a University System, based on the previous datasets. The domain used in the study consists of sixty (60) students in the Department of Computer and Information Science, Tai Solarin University of Education in Ogun State, who have completed four academic sessions from the university. The codes were written and executed using MATLAB format. The students’ CGPA from first year through their third year were used as the inputs to train the ANN models constructed using nntool and the Final Grades (CGPA) served as a target output. The output predicted by the networks is expressed in-line with the current grading system of the case study. CGPA values simulated by the network are compared with the actual final CGPA to determine the efficacy of each of the three feed-forward neural networks used. Test data evaluations showed that the ANN model is able to predict correctly, the final grade of students with 91.7% accuracy. Keywords: Students, Academic Performance, Neural Network, Training Functions, University, CGPA
Journal of Al-Qadisiyah for Computer Science and Mathematics
This study aimed to identify the most important factors affecting the academic achievement of a university student and then build a predictive model using these factors to predict the student's academic status. The ordinal logistic regression method was used to identify the influencing factors, and neural network technique to build the prediction model. It was applied to a sample of 188 students at Al-Yamamah University in the Kingdom of Saudi Arabia. The analyzed data were collected using a questionnaire of three main axes: representing student related factors, family, and academic factors. The results of the logistic regression model showed that 10 variables had a significant effect on the student's academic achievement: age, cumulative average at high school, the distance of residence, work besides studying, daily study hours, frequent absences, fear of exams, the economic level of the family, parents divorce, and family follow-up. As for the MLP network model with archit...