Prediction of Success or Fail of Students on Different Educational Majors at the End of the High School with Artificial Neural Networks Methods

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1 Predcton of Success or Fa of on Dfferent Educatona Maors at the End of the Hgh Schoo th Artfca Neura Netors Methods Sayyed Mad Maznan, Member, IACSIT, and Sayyede Azam Aboghasempur Abstract The man obectve of departments of educaton s provdng quaty educaton for students. One of the methods to acheve a hgh eve of quaty n educaton system s noedge dscovery for predcton of students' enroment n a specfc fed. Ths noedge s hdden n educatona and motvatona data. It s etractabe by data mnng technques. The present artce s desgned to ustfy the capabtes of data mnng methods n educaton by presentng a data mnng method. In ths research cassfcaton s used for evauatng the students' performance. There are many methods for predctng students' Performance n dfferent feds. Here artfca neura (ANN) s used. Wth ths method e etract the noedge hch descrbes the students' performance n the fna eams. Ths method s sutabe for dentfyng approprate fed for the students and they succeed n the fed. Inde Terms Actvaton functon, artfca neura netors, data mnng tranng, mutayer perceptron netors. I. INTRODUCTION Data mnng s the process hch as appeared n 1990's and t as ntroduced by Fayaz for the frst tme n the frst nternatona conference on noedge dscovery and data mnng n In 1959 the term Machne Learnng as ntroduced for the frst tme by Samue and t refers to the ays n hch a computer can gan noedge drecty from data. Pendy and Pa conducted ther study to students' performance by Seectng 600 students from dfferent facutes [1]. Then Heaz and Naghav have done research on the student's atttude toard cass attendance, study hours after unversty and etc. They used smpe near regresson anayss. In ths research e ntended to use a method n hch students can choose a fed easer and th ess anety and the probabty of success be greater at that fed. Ths s the most mportant matter for the students and parents. The technque used n ths research s artfca neura netor n data mnng and Matab softare as used for smuatons []. II. ARTIFICIAL NEURAL NETWORK STRUCTURE An artfca neura netor conssts of eements of ayers Manuscrpt receved June 10, 0; revsed August 0, 0. Sayyed Mad Maznan s th the Department of Eectrca and Computer Engneerng, Imam Reza Unversty, Mashhad, Iran (ema: maznan@eee.org). Sayyede Azam Aboghasempur s th the Scence and Research Unt, Isamc Azad Unversty, horasan Razav, Iran (ema: a.aboghasempur@yahoo.com). and eghts. The netor behavor s reated to the connectons beteen members. Generay, there are three types of neuron ayers n neura netors. Input ayer, hdden ayers, output ayer. There are snge and mutayer netors. Snge ayer organzaton n hch a unts are connected to one ayer s the most common type. In mutayer netors, unts are numbered accordng to ayers (nstead of tracng overa numberng).to ayers of a netor are connected th eghts and n fact th connectons [3]. There are severa types of connectons and eght connectons n neura netors. Forard: Sgnas move ony n one drecton. Each ayer output has no effect on that ayer. Bacard: Data are refereed from upper ayer nodes to oer ayer nodes. Latera: Each ayer output s used as the nodes nput of the same ayer. Fna Stage When you submt your fna verson, after your paper has been accepted, prepare t n to-coumn format, ncudng fgures and tabes. A. Mutayer Perceptron Netors In mutayer perceptron netors at frst eghts of the prevous ayers be corrected [3]. Perceptron netors consst of an nput ayer, severa hdden ayers, and an output ayer. The ayers have the foong condtons: Each ayer's neurons are ony connected to the net ayer neurons. Each Neuron s connected to a neurons of the net ayer. Input ayer neurons do no acton and ther eghts are constant and are equa to one. These neurons have no compresson functon. The operator propagaton s forard. A neurons ecept nput ayer are coectng neurons and each neuron can have an ndependent compresson functon. Each neuron can have ndependent bas. The number of hdden ayers s not non. B. Learnng Agorthm of Mutayer Perceptron Netors (Error Propagaton) Sgns and names In ths earnng agorthm the foong sgns are used [4]: tranng nput vector t target output vector the secton for ustfyng eghts for and the vaue of that s cacuated accordng to error nput unt( the dfference beteen the netor output and the target output) DOI: /IJIMT.0.V

2 the secton for ustfyng the error correcton eghts for v hch s cacuated after error propagaton from output ayer to the hdden unt z α earnng rate nput unt of v 0 bos n hdden unt z hdden unt Input unt z hch s shon by z_n z_n s shon n (1) v 0 z _ n X v (1) Output sgna actvaton of z s named z and t s cacuated accordng to () n hch f s actvaton. C. Actvaton Functon z f ) () ( z n Actvaton functon n neura netors must be contnuous, dfferentabe, and the dervatve must be unvoca descendng. One of the most common functons s the sgmod dua functon. Doman of ths functon s from 0 to 1 and the formua s e (3) 1 f 1 ep( ) (3) Another actvaton functon s bpoar sgmod functon. Ths functon doman s -1 to 1 and the formua s as the (4). f 1 (4) 1 ep( ) D. Learnng Stages of Error-Bac Propagaton Agorthm Stage 0: We gve an nta vaue to eghts and bases (very sma accdenta vaue) Stage 1: We contnue stages and 9 unt the stoppng condtons are estabshed Stage : For each tranng par (target and nput vaues) e do stages 3 and 8 1) Feed forard Stage 3: Each nput unt, =1,, n receves nput sgna and spreads t to a unts n the net ayer. Stage 4: Each hdden ayer z, =1,, p coect t s eghted nput sgna, accordng to (5) z _ n v v (5) 0 Then the actvaton functon s used for cacuatng the output, accordng to (6) formua and send the sgna to a net unts. n 1 z f ) (6) ( z n Stage 5: Each output unt Y, =1,, m coects It s eghted nput sgna accordng to (7) p y _ n z (7) 0 1 And then the actvaton functon s used for cacuatng the output, accordng to (8) ) Error propagaton y f ) (8) ( y n Stage 6: Each output unt Y, =1,, m receves a target mode hch s correspondng to nput tranng mode and cacuate the error accordng to (9) formua ' ( t y ) f ( y _ n ) (9) And then the eght correcton parameter s cacuated hch be used n update accordng to (10) formua a z (10) And then the bases correcton parameter s cacuated hch be used n 0 update accordng to (11) formua a (11) o And then send to prevous unt ater. Stage 7: Each hdden ayer coect t s deta outputs, accordng to (-1) formua m 1 _ n (1) Then t s mutped to actvaton functon dervatve to cacuate error data parameters. It s cacuated accordng to () formua: ' _ n f ( z _ n ) () Then eght correcton s cacuated hch n net stages be used for updatng v accordng to () formua: v a () And then bos correcton s cacuated hch ater be used for updatng v 0 accordng to () formua: 3) Weght and bos update v 0 a () Stage 8: Each output unt updates It s eghts and bases accordng to () formua: 46

3 0,..., p ( ne) ( od ) () Each hdden unt z, =1,, p updates ts eght and bases accordng to (17) formua: 0,..., n v ( ne) v ( od ) v (17) Stage 9: We chec stoppng condtons. We shoud be carefu that n mpementng ths agorthm, separate arrays must be used for output unts Detas (Deta n stage 6) and hdden unts Detas. E. Fna Condton and Eary Weghts Hepfu Hnts A perod s a compete cyce around earnng compe. Severa cyces are needed for tranng a bac propagaton neura netor. Usuay, tranng cyce s contnued to get a tota error average th sutabe east amount or zero (fna condton). Sometmes, average error doesn t change n a fe perods. So that neura netor earnng s fnshed. Generay, eary etent of eghs and bases are done by tte etent and randomy. It has been confrmed that choosng eary etent for eghs and bases converge to correct amounts. That s, f each etent s chosen, netor parameters are reguated. The dfference s that, he much dstance to eary eghs and bases s observed th ther correct mounts, perod numbers ncrease [5], [6]. When there s more than one ayer n perceptron netors, the agorthm shoud be generazed to a ayers. There s not an avaabe practca method for estmatng unt numbers (neurons) n each hdden ayer. Therefore, tra and error methods shoud be used to get a sutabe amount of error average. F. Rada Bass Functon Netors Through usng (18) equatons, rada bass functon netor can be cacuated and ony actvatng functon shoud be focused and the parts of the agorthm are the same as prevous part. That s, agorthm s used for measurng eghs and bases after error propagaton [7]. In above equaton, s the Eucdean dstance. 0,1,..., ; 0,..., n; 1,..., m Znet Z Y Y f Ynet f z f ( Znet ) f ( Ynet ) f ( z III. APPLIED MODEL IN THE STUDY ) (18) In ths study mut-ayer neura netors have been used for predctng scentfc and motvatona progresses among studed n varous feds of study. As a matter of fact, the used mode for ths predctng ncudes an nput, a hdden ayer and an output. After conductng the nputs and outputs to neura netor (neura netor tranng), the neura netor earns to create a sutabe correspondng repy to the same nputs by tang the nputs agan. A. Inputs The nputs contan to parts, course grades and educatona taent tests; both of them are epressed numercay. In course grades, the scores of a tetboos n frst grade of hgh schoo are gathered for a students and n educatona taent, the psychoogca tests, ased from students, are gven. The nputs of tetboos scores are as foos (a beteen 0 to 0): Regous and fe, Persan anguage, Persan terature, Arabc anguage, Engsh anguage, physcs and aboratory, chemstry, math, soca studes, scence, sport, fe ss, educatona and occupatona pannng. The nputs of educatona taent ncude the foong questons: The etent of ng or hatng the fed of study by the student (-1 to 1), he -1 ndcates that the student hates the fed and 1 means the students es the fed very much. The etent of encouragng or forcng to choose a fed for the student by parents (a number beteen -1 to 1), he -1 ndcates the student has to choose the fed ust because of parents forcng and number 1 means that the study chooses the fed as hs/her nterests. The etent of factes and schoos (-1 to1), he number -1 means that students n the fed of study have the east educatona factes and number 1 means that ths fed contans the most educatona factes. B. Outputs Our output n ths study s ust a number that s there s ust a neuron n out ayer that corresponds to tota average of students. In fact, ths number s obtaned through conductng a non- near functon (neura netor) on the nputs. Tota average s output, n fact output, students tota average, s predcted by usng neura netor and ts correspondng nputs. IV. SIMULATIONS AND EVALUATION The supposed neura netor oos e as foong fgure n hch the nputs are the scores and educatona taent tests and the outs pub s the tota average. Bac propagaton of error has been used for tranng the netor and aso Eucdean actvatng functon s used for creatng a Rada Bass Netor mode. Genera scheme of a mutayer neura netor s gven n Fg. 1 [8]. A. Conductng the Data and Errors n Proposed Neura Netors 60% nformaton of data bases are used for tranng neura netors and 40% for vadatng and testng the estmated fgures. The process ncudes enterng the data, dvdng them nto to tranng and testng parts, preprocessed normazng, 463

4 reguatng hdden ayers tranng functon and other parameters, tranng neura netor, confrmng and testng neura netor. Output error s for math fed that s sutabe and are gven n Fg. 3 and Fg. 4. Fg. 1. Mut ayers neura netor. Fg. 3. Output averages for many students. B. Normazng Inputs and Outputs Dvergng and ncorrecty tranng the netor decreases and tranng neura netors be qucer (ess tme or ess repettons) by scang nputs and outputs, n scang the nputs and outputs, the data are normazed n [-1, 1] (bpoar). Due to (19): norm ( rea ma mn mn ) 1 (19) here, norm s normazed etent of the parameter, rea s the rea parameter etent, mn s the east parameter etent and ma s the hghest parameter etent. Aso the data are dvded randomy to ncrease vadty n ths process. C. Error Measurement Mean squared error (MSE) s used as a too n measurng predctng precson. Due to (0): MSE N 1 ( P rea P N mn) (0) here, N s sampe numbers, P actua s rea or actua etent of parameter and P sm s smuated etent of the parameter [8]. Fg. 4. error square average. Output error s 10-3 for epermenta fed that s sutabe and are gven n Fg. 5 and Fg Fg. 5. Output averages for many students. V. SIMULATIONS The data n varous feds of study (math, humantes, epermenta, technoogca and occupatona) are gven to neura netor for tranng and eamnaton and n fact, e have 5 neura netors hch tranng them through specazed fed data. The data ere gathered actuay and from schoos n varous areas. The used topoogy n ths study s gven n Fg.. Fg. 6. error square average. Output error s 10-3 for humantes fed that s sutabe and are gven n Fg. 7 and Fg Fg.. Mut ayers neura netor. Fg. 3, Fg. 5, Fg. 7, Fg. 9, and Fg. 11 sho averages of student and Fg. 4, Fg. 6, Fg. 8, Fg. 10, and Fg. 1 sho error square average. Here, the resuts of smuaton for one fed are gven: Fg. 7. Output averages for many students. 464

5 Systems neura netors and nove measurng methods for mechanca earnng, ndcatng noedge and fnay conductng the obtaned noedge are gong to conduct output repes of compcated systems. In fact, neura netors are non- near toos n predctng varous data. Fg. 8. Error square average. Output error s for technoogca fed that s sutabe and are gven n Fg. 9 and Fg Fg. 9. Output averages for many students. Fg. 10. Error square average. Output error s 10-3 for occupatona fed that s sutabe and are gven n Fg. 11 and Fg VI. CONCLUSION In ths study, e predct scentfc and motvatona progress among students n varous fed of study through usng artfca neura netors; regardng to ths method the scores of frst grade hgh schoo and some educatona taent and motvatona questons as consdered as nputs. Error bac propagaton method th Eucdean actvatng functon has been apped for tranng neura netor eadng to Rada Bass Netors. Fnay, the data have been eamned on 5 neura netors, everyone has been a fred of study. The netors coud get the error ess than that s tte and sutabe. And eventuay the 5 traned netors ere eamned th one dea nput and average resuts are gven to them; that proposes the students to choose hch fed for contnung studes; n other ords n hch they be more successfu. REFERENCES [1] DANN: Genetc aveets, DANN Proect, August 010. [] Sprnger, do: / _6, ISBN , January 01. [3] NASA Neura Netor Proect Passes Mestone, NASA, Juy 010. [4] D. C. Cresan, U. Meer, J. Masc, and J. Schmdhuber, Mut-Coumn deep neura netor for traffc sgn cassfcaton, Neura Netors, 01. [5] I. E. Lvers and P. Pnteas, An mproved spectra conugate gradent neura netor tranng agorthm, Internatona Journa on Artfca Integence Toos, vo. 1, no. 1, 01. [6] C. Romero, S. Ventura, and E. Garca, Data mnng n course management systems: Moode case study and tutora, Computers and Educaton, vo. 51, no. 1, pp , 008. [7] I. E. Lvers and P. Pnteas, An mproved spectra conugate gradent neura netor tranng agorthm, Internatona Journa on Artfca Integence Toos, vo. 1, no. 1, 01. [8] M. Ha, E. Fran, G. Homes, B. Pfahrnger, P. Reutemann, and I. H. Wtten, The WEKA data mnng softare: An update, SIGKDD Eporatons, vo. 11, no. 1, Fg. 11. Output averages for many students. Sayyed Mad Maznan as born n Mashhad, Iran on 8 January He receved hs Bacheor degree n Eectroncs from Ferdos Unversty, Mashhad, Iran n 1994 and hs Master degree n Remote Sensng and Image Processng from Tarbat Modarres Unversty, Tehran, Iran n He ored n IRIB from 1999 to 004. He aso receved hs phd n Wreess Sensor Netors from Ferdos Unversty, Mashhad, Iran n 009. He s currenty assstant professor at the facuty of Engneerng n Imam Reza Unversty, Mashhad, Iran. He as the head of Department of Eectrca and Computer Engneerng from 009 to 01. Hs research nterests ncude Computer Netors, Wreess Sensor Netors and Smart Grds. Fg. 1. Error square average. Seyede Azam Aboghasempur s from Iran and ved n Bonourd. She as born n 1984, and got her B.Sc on softare engneerng n 009. Then she started teachng at Tranng and Educatona organzaton n hgh schoos as e as n unversty. She got frst ocaton on eectronc-content- producng beteen a Tranng and Educatona offces n Iran n 011. She as accepted on Research and Scence of Khorasan-Razav n M.sc n 011, hch as support by Tranng and Educatona organzaton. 465