Predicting Students' Academic Performance Using Artificial Neural Networks: A Case Study
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(IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 5, 2010 Predicting Students' Academic Performance Using Artificial Neural Networks: A Case Study Ghaleb A. El-Refae Qeethara Kadhim Al-Shayea Faculty of Economics and Admin. Sciences Faculty of Economics and Admin. Sciences, MIS Dep. Al-Zaytoonah University of Jordan Al-Zaytoonah University of Jordan Amman, Jordan Amman, Jordan firstname.lastname@example.org email@example.com Abstract—Predicting students’ academic performance is computer orientation classes, use of computer-multimedia, critical for universities because strategic programs can be disposition toward computers, and majors. planned in improving or maintaining students’ performance. The goal of this study is to predict the factors affecting the Al-Tamimi and Al-Shayeb  investigated some factors university students' performance using Artificial Neural affecting student performance in the fundamentals of Networks (ANN) model. Various factors that may likely financial management course at United Arab Emirates influence the performance of a student were identified. University. Generalized Regression Neural Network (GRNN) is used to Ibrahim and Rusli  developed three predictive models predict the university students' performance. It is noticed a using SAS Enterprise Miner that are, artificial neural significant improvement in the prediction made by network, decision tree and linear regression. The result of GRNN due to its generalization property. The most this study showed that all of the three models produce more important predictor variable influencing performance is than 80% accuracy. It also showed that artificial neural consistently having the largest regression. Results showed that network outperforms the other two models. secondary school performance which is measured by scores in secondary school certificate examination, measured in a Oladokun, Adebanjo and Charles-Owaba  presented percentage form having the largest regression value. an artificial neural network model for predicting the likely performance of a candidate being considered for admission Keywords-component; regression, stdudent performance; into the university was developed and tested. Artificial neural networks; general regression network II. ARTIFICIAL NEURAL NETWORKS I. INTRODUCTION An artificial neural network (ANN) is a computational The prediction and explanation of academic performance model that attempts to account for the parallel nature of the and the investigation of the factors relating to the academic human brain. An (ANN) is a network of highly success and persistence of students are topics of utmost interconnecting processing elements (neurons) operating in importance in higher education . parallel. These elements are inspired by biological nervous McKenzie and Schweitzer  presented a study that was systems. As in nature, the connections between elements a prospective investigation of the academic, psychosocial, largely determine the network function. A subgroup of cognitive, and demographic predictors of academic processing element is called a layer in the network. The first performance of first year Australian university students. layer is the input layer and the last layer is the output layer. Between the input and output layer, there may be additional Alfan  determined the undergraduate students' layer(s) of units, called hidden layer(s). Fig. 1 represents the performance in the Faculty of Business and Accountancy, typical neural network. You can train a neural network to University of Malaya and the factors influencing the perform a particular function by adjusting the values of the performance of the undergraduate students. The result of the connections (weights) between elements. study shows that the predictor variables do explain the variance in the students' final cumulative grade point average. In addition, it was found that knowledge prior to entering the university such as economics, mathematics and accounting is crucial in assisting the students in undertaking the courses in both business and accounting program. The study also found that female students perform better than male students; whilst Chinese students perform better than Malay and Indian students. Su  evaluated the performance of university students who learned science texts by using, information communication technologies including animation, static figures, power point, and e-plus software. The results included the computation of the F-ratio, p-values, and Cohen’s effect-sizes of attitudes toward science and learning science in relation to the student’s gender, attendance of Figure 1. A typical neural network 97 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 5, 2010 For the researcher and the financial analyst, the main advantage of ANNs is that there is no need to specify the Di2 = ( x − ui ) ( x − ui ) , the squared distance between the functional relation between variables. Since they are T connectionist-learning machines, the knowledge is directly imbedded in a set of weights through the linking arcs among input vector x and the training vector u, x= the input vector, the processing nodes. In order to train a neural network ui=training vector i, the center of neuron i, spread=a constant properly one needs a large set of representative 'good controlling the size of the receptive region. quality’ examples. In the case of bankruptcy problems, the researcher should be cautious when drawing conclusions from neural networks trained with only one or two hundred cases, as observed in most previous studies . A. Generalized Regression Neural Network The GRNN was applied to solve a variety of problems like prediction, control, plant process modeling or general mapping problems . General regression neural network Specht , Nadaraya  and Watson , does not require an iterative training procedure as in back-propagation method. The GRNN is used for estimation of continuous variables, as in standard regression techniques. It is related to the radial basis function network and is based on a Figure 2. Generalized Regression Neural Network (GRNN) Architecture standard statistical technique called kernel regression. By definition, the regression of a dependent variable y on an III. EXPERIMENTAL RESULTS independent x estimates the most probable value for y, given x and a training set. The regression method will produce the A. Data estimated value of y, which minimizes the mean-squared This study was conducted at the faculty of Economics error. GRNN is a method for estimating the joint probability and Administrative Sciences, Al-Zaytoonah University of density function (pdf) of x and y, given only a training set. Jordan in Hashemite Kingdom of Jordan. Our sample Because the pdf is derived from the data with no consists of 208 students belonging to accounting preconceptions about its form, the system is perfectly department. The information for this study has been general. Furthermore, it is consistent; that is, as the training obtained from the register office at Al-Zaytoonah University set size becomes large, the estimation error approaches zero, of Jordan and which are maintained on a computerized with only mild restrictions on the function. In GRNN, database. instead of training the weights, one simply assigns to wij the target value directly from the training set associated with input training vector i and component j of its corresponding The Cumulative Grade Average Point (CGPA) is used as output vector . GRNN architecture is given in Fig. 2. an indicator to measure the performance of the university GRNN is based on the following formula : students'. ∞ The students' overall performance was hypothesized to ∫ y. f ( x, y ).dy be a function of the following factors: (1) Secondary school E[y | x] = −∞ performance is measured by scores in secondary school ∞ (1) certificate examination, measured in a percentage form (2) ∫ f ( x, y ).dy −∞ type of secondary school branch, (3) gender, and (4) boarding or non boarding student. where y is the output of the estimator, x is the estimator B. Results Analysis input vector, E[y|x] is the expected output value, given the A generalized regression neural network (GRNN) with a input vector x and f(x,y) is the joint probability density radial basis layer and a special linear layer and linear output function (pdf) of x and y. neurons was created using the neural network toolbox from The function value is estimated optimally as follows: Matlab 7.9 as shown in Fig. 2. Generalized regression neural n networks are a kind of radial basis network that is often used ∑ h .w i ij for function approximation. yj = i =1 n (2) The first layer has as many neurons as there are input/ ∑h target vectors. Each neuron's weighted input is the distance i between the input vector and its weight vector. Each i −1 neuron's net input is the product of its weighted input with where wij= the target output corresponding to input training its bias. Each neuron's output is its net input passed through vector xi, radial basis transfer function. Radial basis transfer function − Di2 is a neural transfer function which calculates a layer's output from its net input. If a neuron's weight vector is equal to the hi = e 2. spread 2 , the output of the hidden layer neuron, input vector (transposed), its weighted input will be 0, its net 98 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 5, 2010 input will be 0, and its output will be 1. The second layer The GRNN with cumulative grade point average CGPA also has as many neurons as input/target vectors. as a target and gender as input was been created. The spread value was chosen 0.5. The percent correctly predicted in the We used a spread slightly lower than the distance simulation sample is approximately 27 percent as shown in between input values, in order, to get a function that fits Fig. 6. individual data points fairly closely. A smaller spread would fit data better but be less smooth. Figure 3. A generalized regression neural network (GRNN) The GRNN with cumulative grade point average CGPA as a target and secondary school performances that is measured by scores in secondary school certificate examination, measured in a percentage form as input was been created. Then simulate the network with 208 inputs. The network outputs after simulation. The spread value was chosen 0.2. Figure 6. The percent correctly predicted in the simulation sample is approximately 76 percent as shown in Fig. 4. The GRNN with cumulative grade point average CGPA as a target and boarding or non boarding as input was been created. The spread value was chosen 0.5. The percent correctly predicted in the simulation sample is approximately 20 percent as shown in Fig. 7. Figure 4. The GRNN with cumulative grade point average CGPA as a target and type of secondary school branch as input was been created. The spread value was chosen 0.6. The percent Figure 7. correctly predicted in the simulation sample is approximately 36 percent as shown in Fig. 5. Figure 8. Figure 5. 99 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 5, 2010 It is obvious from the results, that the most affecting Faculty of Economics and Administrative Sciences at Al-Zaytoonah factor in academic university students' which is measured by University of Jordan, Amman – Jordan. His research interest is in the application of IT and IS in Business and Economics. CGPA is the secondary school performance. Fig. 8 shows the multiple regressions for the four Qeethara Kadhim Abdul Rahman Al-Shayea, has affecting factors. The spread value was chosen 0.2. The received Ph. D. in Computer Science, Computer percent correctly predicted in the simulation sample is Science Department, University of Technology, approximately 89 percent. Iraq, 2005. She received her M.Sc. degree in Computer Science, Computer Science Department IV. CONCLUSIONS from University of Technology, Iraq, 2000. She has received her High Diploma degree in information In this paper the general regression neural network is Security from Computer Science Department, used for the prediction of university student performance. University of Technology, Iraq, 1997. She has The advantage of using the GRNN in the prediction is its received B. Sc. Degree in Computer Science generalization property. The results of this study provide Department from University of Technology, Iraq, evidence which suggests that secondary school performance 1992. She joined in September (2001-2006), Computer Science Department, University of Technology, Iraq as assistant is the single most important variable associated with their professor. She joined in September 2006, Department of Management overall performance upon graduation from university. Other Information Systems Faculty of Economics & Administrative Sciences Al- variables such as type of secondary school branch, gender, Zaytoonah University of Jordan as assistant professor. She is interested in and boarding or non boarding student show a lesser degree Artificial intelligent, image processing, computer vision, coding theory and of significance in predicting performance as compared with information security. secondary school score. The results also indicate that the variables examined in this study provided a significant contribution in predicting performance when used jointly with secondary school performance variable. References  P. Fenollar, S. Roma´n and P. J. Cuestas, University students’ academic performance: An integrative conceptual framework and empirical analysis, British Journal of Educational Psychology, 77, pp. 873–891, 2007.  K. Mckenzie and R. Schweitzer, Who Succeeds at University? Factors Predicting academic performance in first year Australian university students, Higher Education Research and Development, Vol. 20, Issue 1, pp. 21-33, May, 2001.  E. Alfan, Undergratuate Students' Performance: The Case of University of Malaya, Quality Assurance in Education: An International Perspective, Vol. 13, Issue 4, pp. 329-343, 2005.  K. Su, An integrated science course designed with information communication technologies to enhance university students’ learning performance, Computers and Education, Vol. 51, Issue 3, pp. 1365-1374, Nov., 2008.  H. A. Al-Tamimi and A. R. Al-Shayeb, Factors Affecting Student Performance in the Introductory Finance Course, Journal of Economics and Administrative Science, Vol. 18, No. 2 Dec., 2002.  Z. Ibrahim and D. Rusli, Predicting Students’ Academic Performance: Comparing Artificial Neural Network, Decision Tree and Linear Regression, 21st Annual SAS Malaysia Forum, 5th September 2007.  V.O. Oladokun, A.T. Adebanjo, and O.E. Charles-Owaba, Predicting Students’ Academic Performance using Artificial Neural Network: A Case Study of an Engineering Course, The Pacific Journal of Science and Technology, Vol. 9, No. 1, pp. 72-79, May, 2008, web page available at:  J. C. Neves and A. Vieira, Improving Bankruptcy Prediction with Hidden Layer Learning Vector Quantization, European Accounting Review, Vol. 15, No. 2, pp. 253–271, 2006.  D. W. Patterson, Artificial Neural Networks, Theory, and Applications, Englewood Cliffs, NJ: Prentice-Hall, 1995.  D. F. Specht, A general regression neural network, IEEE Trans. Neural Networks, vol. 2, pp. 568–576, 1991.  E. A. Nadaraya, On estimating regression, Theory of Probab. Applicat., vol. 9, pp. 141–142, 1964.  G. S. Watson, Smooth regression analysis, Sankhya Series A, vol. 26, pp. 359–372, 1964.  M. T. Hagan, H. B. Demuth, M. Beale, Neural network design, PWS Publishing Company, Boston, 1996.  K. Kayaer and T. Yildirim, Medical Diagnosis on Pima Indian Diabetes Using General Regression Neural Networks, web page available at: www.yildiz.edu.tr/~tulay/publications/Icann-Iconip2003-2.pdf. AUTHORS PROFILE Ghaleb A. El-Refae, has a Ph. D. and M.A in Financial Economics form USA, M. Sc and B. Sc in Accounting. He is a professor and Dean of 100 http://sites.google.com/site/ijcsis/ ISSN 1947-5500