Application of E-Learning Assessment Based on AHP-BP Algorithm in the Cloud Computing Teaching Platform

Transcription

1 Applcton of E-Lernng Assessment Bsed on AHP-BP Algorthm n the Cloud Computng echng Pltform Chunfu Hu Donggun Unversty of echnology, Donggun, Gungdong, Chn Abstrct Wth the ncresng development of the Internet, students begn to lern quckly through nformton technology nd Internet technology. E-lernng s not only n mportnt prt of Chns hgher educton, but lso s n mportnt mens to mprove the qulty of educton, expnd the scle of educton, deepen eductonl reform, nd relze the eductonl equty mong dfferent clsses n socety. However, the problem of E-lernng s becomng ncresngly obvous, whch s how to ensure students effectve lernng, n mportnt topc n the feld of onlne educton. In order to solve ths problem, we frst buld n E- lernng pltform bsed on cloud computng, nd ntroduce the softwre nterfce nd functon. he pltform s dfferent from the trdtonl techng pltform, hvng strong ntercton. Secondly, the dt mnng technology s used to nlyze nd collect the record dt n the process of E- lernng, so s to estblsh the evluton system of the E- lernng comprehensve cpblty. hen, we propose n E- lernng blty evluton model bsed on n AHP-BP neurl network lgorthm. We use BP neurl network to predct the evluton results of 1000 students, nd compre them wth the results obtned by the AHP method, so s to llustrte the effectveness of the method proposed n ths rtcle. Fnlly, through experments we cn see tht the predcton results of the BP neurl network nd the evluton results obtned by the AHP method re smlr. hs proves the effectveness of the AHP method on the evluton of the E-lernng comprehensve cpblty. At the sme tme, the BP neurl network method cn be used to del wth lrge number of evluton results, whch cn sve tme, wthout losng ccurcy. Index erms Cloud computng; E-lernng; AHP-BP lgorthm; Assessment I. INRODUCION Wth the ncresng development of the Internet, students begn to lern quckly through nformton technology nd Internet technology. E-lernng s not only n mportnt prt of Chns hgher educton, but lso n mportnt mens to mprove the qulty of educton, expnd the scle of educton, deepen eductonl reform, nd relze the eductonl equty mong dfferent clsses n socety. However, the problem of E-lernng s becomng ncresngly obvous, whch s how to ensure students effectve lernng, n mportnt topc n the feld of onlne educton. Globlly, scholrs hve conducted much reserch n the feld of onlne educton. Moore [1] proposes method to motvte lerners though vrous forms of onlne nterctve ctvtes, so s to effectvely reduce the lerners sense of dstnce nd seprteness. Hltz [2] beleves tht vrous knds of nterctve ctvtes re the key fctors of students n E-lernng. Gunwrden [3] thnks tht the nterctve level drectly ffects the effectveness of onlne lernng. Krel [4] emphszes tht socl nterctve ply s n mportnt role n the feld of E-lernng. Chen L [5] estblshes the AHP model of onlne nterctve techng. Accordng to the defnton, sgnfcnce nd methods of the reserch, Zhong Zhxn [6-9] puts forwrd fve mportnt elements of E-lernng; techers, lerners, currculum, technology nd ctvtes. Co Lnglng [10] uses the socl network nlyss method to crry out systemtc nlyss of the ntercton model nd dt structure of E-lernng. Accordng to the chrcterstcs of the dlogue, prtcpton, support nd control of E-lernng, lterture [11] dvdes the E-lernng nto four types, ech n detl. Zhou Ynn [12] dscusses the ntegrton of E- lernng nd vew of knowledge nd studyng. Wth the computer nd network technology, Go Xngyun [13] estblshes the E-lernng nlyss system, nd mkes systemtc evluton of students nd techng resources. In vew of the lernng gols, ledershp style, nd ctvty mechnsm of vrtul clss mngement, Zhng Lxn [14] presents vrtul clss mngement strteges. In the feld of onlne educton, lernng behvor of lerners s knd of utonomous lernng, chrcterzed by hgh level of rndomness. herefore, the onlne communcton nd smple tests hve been unble to ccurtely evlute the lernng behvors nd lernng outcomes. here s n urgent need to explore the onlne evluton model for dstnce lernng behvor, so s to ensure tht the onlne techng cn be ccurtely montored. here hve been mny methods to ssess the E-lernng, such s the dt envelopment nlyss method, fuzzy comprehensve evluton method, AHP (nlytc herrchy process) model, grey correlton evluton method nd OPSIS method [15,16]. However, the lmttons of vrous methods wll ffect the fnl evluton results. So, ccordng to the chrcterstcs of onlne educton, nd combnng the dvntges of vrous evluton methods, new method of comprehensve evluton s needed. o sum up, n order to solve ths problem, we frst buld n E-lernng pltform bsed on cloud computng, nd ntroduce the softwre nterfce nd functon. he pltform s dfferent from the trdtonl techng pltform, hvng strong ntercton. Secondly, the dt mnng technology s used to nlyze nd collect the record dt JE Volume 11, Issue 8,

2 n the process of E-lernng, so s to estblsh the evluton system of the E-lernng comprehensve cpblty. hen, we propose n E-lernng cpblty evluton model bsed on AHP-BP neurl network lgorthm. We use BP neurl network to predct the evluton results of 1000 students, nd compre them wth the results obtned by the AHP method, so s to llustrte the effectveness of the method proposed n ths rtcle. Fnlly, through experments we cn see tht the predcton results of the BP neurl network nd the evluton results obtned by the AHP method re smlr. hs proves the effectveness of the AHP method on the evluton of the E-lernng comprehensve cpblty. At the sme tme, the BP neurl network method cn be used to del wth lrge number of evluton results, whch cn sve tme wthout loss of ccurcy. II. CLOUD COMPUING AND E-LEARNING he trdtonl multmed network clssroom s usully comprsed of projector nd the hrdwre network system. However, the consumpton, mntennce nd upgrdes of hrdwre s problemtc nd costly for the school. he use of E-lernng nterctve softwre bsed on cloud computng cn completely vod these problems. Wth the help of ths softwre, Power Pont, CD, expermentl demonstrton system nd the techng methods cn be relzed wth the modern nterctve techng mode of voce, mge, text, nd nmton. he pplcton nterfce of the E-lernng pltform s shown n Fgure 1. In the nternl nterctve softwre, there s n E- lernng nterctve pltform bsed on cloud computng. ht s to sy, the nterctve softwre s n the pplcton lyer of the ntercton pltform. he E-lernng pltform s supplement to the trdtonl mode of educton. In ddton, t s the nhertnce nd development of the trdtonl educton mode. he pltform ncludes the followng modules: A. he user regstrton nd logn Users regster n the system through ther ID. At the sme tme, the users must gve ther nme nd gender whch s used n the E-lernng pltform. hrough the system vldton, the user becomes legtmte user. hen, the user cn log on the E-lernng pltform, query the resources, nd lernng the relted courses. B. he dt downlod he relted resources re uploded to the server by the resource provder. Students cn downlod the necessry resources nd shre them wth other nodes. hs strtegy of resource downlod cn gve the network good sclblty, nd lso reduce server lod nd server bottleneck. C. he onlne test he E-lernng pltform ncludes n onlne test module, nd the test pper s stored n the server nd techers node. Accordng to the ccount permssons settngs, students cnnot downlod ther test pper to locl computer, so t cn only be completed onlne. hrough the students nterfce, they fnsh the test wthn the specfed tme, nd submt the test pper to the server. hen, the test results wll be sent to the clents. D. he onlne dscusson echers nd students cn use the form of text or voce to exchnge ther vews, so s to cheve rel-tme ntercton. he E-lernng nterctve pltform wth cloud computng s shown n Fgure 2. III. BASIC KNOWLEDGE OF ASSESSMEN MODEL In the feld of E-lernng, students lernng behvor s more expressed s knd of utonomous lernng wth hgh level of rndomness nd one-sded tendency. herefore, t hs been unble to ccurtely ssess the lernng behvor nd lernng outcomes of students through the smple onlne communcton nd testng. E-lernng s urgently needs to explore more sutble evluton model, so s to ensure tht the E-lernng cn ccurtely montor nd tmely gude students to lern. Lernng ssessment cn confrm the students lernng progress, mster the lernng level, nd montor the lernng behvor. herefore, t provdes the decson-mkng bss for the djustment nd control of the techng process. It s dvded nto dgnostc evluton, formtve evluton nd summry evluton. Next, we ntroduce the AHP method bsed on the BP neurl network comprehensve evluton model. Fgure 1. he pplcton nterfce of E-lernng nterctve softwre 28

3 Fgure 2. E-lernng nterctve pltform wth cloud computng A. AHP model he nlytc herrchy process (AHP) s smple, flexble nd prctcl mult-objectve decson method, whch combnes both qulttve nd quntttve clculton. It djusts the present optmzton method, whch cn only be used n the quntttve nlyss, nd mkes quntttve nlyss of the non-quntttve problems. On the whole, AHP provdes three reserch methods; the system herrchy nlyss method, 1-9 scle method, nd the feture vector method for rnkng weghts. he steps nd pplctons of AHP re modeled s follows: Step 1: Accordng to the sclng theory, structurng the multple comprson judgment mtrx s denoted s A. A= j n! n ( = 1, 2,!, n), j = 1, j = 1 (1) j Step 2: he judgment mtrx A s normlzed: n =! ( = 1, 2,!, n) (2) j j kj k = 1 Step 3: Clculte the sum of ech row of the judgment mtrx: n! = " j ( = 1, 2,!, n) (3) j= 1 Step 4: he! s normlzed: n! =! "! ( = 1, 2,!, n) (4) = 1 Step 5: Accordng to A! = " mx!, the mxmum chrcterstc root nd chrcterstc vector re clculted. Step 6: Consstency check. B. BP neurl network he Sgmod functon s used s the ctvton functon of the hdden nodes of the BP neurl network. he ctvton functon of the output node of the BP neurl network s dfferent dependng on the purpose of the pplcton. If the BP network s used for clssfcton, the output lyer node generlly uses the sgmod functon or hyperbolc tngent functon. Otherwse, f t s used for functon pproxmton, the output lyer node uses lner functon. Fgure 3 shows BP network structure wth two hdden lyers. Fgure 3. he BP network structure wth two hdden lyers he nput lyer hs M nput sgnls, nd ny one of the nput sgnls s expressed by m. he frst hdden lyer s I, tht s to sy, there re I neurons, nd ny one of them s expressed by. he second hdden lyer s J, tht s to sy, there re J neurons, nd ny one of them s expressed by J. he output lyer s P, tht s to sy, there re P neurons, nd ny one of them s expressed by P. he connecton weghts of the nput lyer nd the frst hdden lyer re expressed by w m. he connecton weghts of the frst hdden lyer nd the second hdden lyer re expressed by w j, nd the connecton weghts of the second hdden lyer nd the output lyer re expressed by w p. he comprehensve evluton process of the AHP-BP neurl network lgorthm s shown n Fgure 4. JE Volume 11, Issue 8,

4 Fgure 4. he comprehensve evluton process of the AHP-BP neurl network lgorthm ABLE I. HE EVALUAION INDEX SYSEM OF E-LEARNING COMPREHENSIVE CAPABILIY Frst level ndex F E-lernng comprehensve cpblty IV. Second level ndex C Lernng tttude Cpblty of ctvely prtcpte he use of E- lernng pltform Cpblty of coopertve lernng hrd level ndex B Interest n lernng he serousness of lernng Lernng effcency Ablty of solve problems Number of put questons Number of uploded fles Lndng frequency otl browsng tme Number of job submsson he number of prtcpte n the dscusson Ablty of communcte wth other lerners Effcency of collbortve lernng Vrble X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 SIMULAION EXPERIMEN AND RESUL ANALYSIS he evluton of E-lernng cpblty cn provde result of the evluton to students nd techers, nd cn lso help students to djust nd control ther E-lernng behvor, so s to cheve better lernng results. Accordng to the E-lernng envronment, the evluton ndex system of E-lernng comprehensve cpblty bsed on the AHP-BP neurl network lgorthm s shown n ble 1. We set up w nd L respectvely for the weght nd the underlyng evluton mtrx of thrd level ndctors,! nd respectvely for the weght nd score mtrx of second level ndctors, nd S s the evluton score of the comprehensve cpblty of E-lernng. Due to the lmted spce, we only hve the comprehensve evluton of the E-lernng cpblty for one student, nd the ssessment method of other students re smlr to ths one. Step 1: Accordng to w nd L, the score mtrx s clculted of the second level ndctors.! " = w % L = (0.435, 0.323, 0.242) % # # = (0.087,0.913,0,0,0)! " b = wb% Lb = (0.437, 0.362, 0.201) % # # = (0, , , , 0)! " c = wc % Lc = (0.416,0.395,0.189)% # # = (0.079,0.921,0,0,0)! " d = wd % Ld = (0.592, 0.307, 0.101) % # # = (0.857,0.143,0,0,0) Step 2: Accordng to! nd, the evluton scores S of the comprehensve cpblty of E-lernng re clculted. S = (!,! b,! c,! d)[ ", b, c, d] # % & = (0.444,0.353,0.108,0.095) " % & % & % & ( = (0.1285,0.5748,0.2658,0.0309,0) Step 3: At ths pont, we defne the comment set: V = { V, V, V, V, V}={ } he results of the sub ndexes re clculted. E = V! = ( )! 0.087,0.913,0,0,0 = E = V! = ( )! 0, , , , 0 = E = V! = ( )! 0.079,0.921,0,0,0 = E = V! = ( )! 0.857,0.143,0,0,0 = hen, the results of the evluton of the comprehensve cpblty of E-lernng re clculted. F = V! S = (90,80, 70, 60,50)! , , , , 0 = 78 Step 4: In ths wy, the students E-lernng comprehensve cpblty cn be obtned. At the sme tme, we gve the followng evluton report, s shown n ble II. 30

5 ABLE II. EVALUAION REPOR FOR A SUDEN Overll evluton: hrough ths stge of E-lernng, your personl comprehensve cpblty s n good level. Among them, the level of lernng tttude, the use of E-lernng pltform nd collbortve lernng cpblty s very hgh. However, the cpblty of prtcpton s lckng, nd I hope you wll be more ctve n prtcpton n the future. Evlutng ndctor Evluton score Comment Lernng tttude At hgher level, I hope you contnue to mntn Cpblty of ctve prtcpton At moderte level, I hope you wll more ctvely prtcpte n the ctvtes. he use of E-lernng pltform At hgher level, I hope you contnue to mntn Cpblty of coopertve lernng Good communcton sklls, I hope you tke ths dvntge Step 5: Fnlly, we use the BP neurl network to predct the evluton results of 1000 students, nd compre them wth the results obtned by the AHP method, so s to llustrte the effectveness of the method proposed n ths rtcle. We use the E-lernng comprehensve cpblty wth 990 students s smple of the BP neurl network, so s to trn the BP neurl network. hen, we predct the E-lernng comprehensve cpblty of the remnng 10 students. Accordng to the nformton n secton 3, the number of nput fctors of the neurl network s 12, the number of the output vlue s 1, nd the number of the hdden lyers s determned by the mnmum fttng error. After multple tmes of trnng, the fnl number of hdden lyers s 4, nd the fttng error s So, the model proposed n ths pper s BP neurl network. he fttng error nd predcton results re gven n Fgure 5 nd Fgure 6 respectvely. Fgure 6 shows tht the predcton results of the BP neurl network nd the evluton results obtned by the AHP method re smlr. hs proves the effectveness of the AHP method on the evluton of the E-lernng comprehensve cpblty. At the sme tme, the BP neurl network method cn be used to del wth lrge number of evluton results, whch cn sve tme wthout loss of ccurcy. V. CONCLUSION In ths rtcle, we frst buld n E-lernng pltform bsed on cloud computng, nd ntroduce the softwre nterfce nd functon. Secondly, dt mnng technology s used to nlyze nd collect the record dt n the process of E-lernng, so s to estblsh the evluton system of the E-lernng comprehensve cpblty. hen, we propose n E-lernng cpblty evluton model bsed on n AHP-BP neurl network lgorthm. Fnlly, through experments we cn see tht the predcton results of the BP neurl network nd the evluton results obtned by the AHP method re smlr. hs proves the effectveness of the AHP method on the evluton of the E-lernng comprehensve cpblty. REFERENCES [1] Moore. hree types of ntercton[j].amercn Journl of Dstnce Educton, 1989,(2):1-6. [2] Hltz, S.R. Onlne educton: perspectves on new envronment.[m]. New York: Preger, 1990: [3] Gunwrden. C, C.Lowe,. Anderson. Anlyss of globl onlne debte nd the development of n ntercton nlyss model for exmnng the socl constructon of knowledge n computer conferencng[j].journl of Eductonl Computng Reserch, 1997, (4): Fgure 5. he number of hdden lyer nodes s 4 n the neurl network trnng (N=990) Fgure 6. Comprson of predcton results [4] Krel Krejns, Pul A. Krschner, Wm Jochems. he Socblty of Computer-Supported Collbortve Lernng Envronments[J]. Journl of Eductonl echnology & Socety, 2002, (1): [5] Chen L, Co Lnglng. he wy nd chrcterstcs of dstnce lerners behvor n synchronous ntercton[j]. Dstnce educton n chn. 2006, (1): [6] Zhong Zhxn, Yng Le. On Lernng on Lne[J]. he modern dstnce educton. 2002, (1): [7] Zhong Zhxn, Yng Le. On Lernng on Lne(Contnued) [J]. he modern dstnce educton. 2002, (2): [8] Zhong Zhxn, Yng Le. On Lernng on Lne(Contnued) [J]. he modern dstnce educton. 2002, (3): [9] Zhong Zhxn, Yng Le. On Lernng on Lne(Contnued) [J]. he modern dstnce educton. 2002, (4): [10] CAO Lnglng. Intercton Potentl of Asynchronous Interctve ools: Reserch on Instructonl Intercton Structure Model n Asynchronous Intercton[J]. Open educton reserch. 2008, 1(14): [11] Lee Als, Zhng Qngln. he types nd chrcterstcs of onlne lernng [J]. Educton Scence. 2003, (1): [12] Zhou Ynn. he ecosystem perspectve of network lernng [J]. he modern dstnce educton. 2005, (6): [13] Go xngyun. Desgn nd mplementton of the feedbck functon of onlne lernng pltform [J]. Chnese educton nformton, 2012, (21): JE Volume 11, Issue 8,

6 [14] Zng Lxn, L Moln. Reserch on the Vrtul Clssroom Mngement Strteges from the Perspectve of Group Dynmcs[J]. Journl of dstnce educton, 2011, (6): [15] L Shme,Hn Qngln. he Reserch on Collegue echers Comprehensve Performnce Evluton Bsed on AHP Model[J]. Journl of Bejng nsttute of educton(nturl scence edton), 2008, 3(5): [16] Qo Wede. echng Ablty Evluton Bsed on AHP Informton echnology nd Currculum[J]. Chngzhou Rdo &elevson Unversty, 2007, (5): AUHOR Chunfu Hu receved hs M.Sc. n Computer Scence (2001) from Zhejng Unversty. Now he s full techer of softwre engneerng t the School of Computng, Donggun Unversty of echnology, Donggun, , Gungdong, Chn. (e-ml: hucf@dgut.edu.cn) Submtted 16 July Publshed s resubmtted by the uthors 25 August