Knauf, Rainer; Sakurai, Yoshitaka; Tsuruta, Setsuo ; Takada, Kouhei; Dohi, Shinichi

Transcription

1 Kauf, Raer; Sakura, Yoshtaka; Tsuruta, Setsuo ; Takada, Kouhe; Doh, Shch Persoalzed currculum composto by learer profle drve data mg Zuerst erschee : IEEE Iteratoal Coferece o Systems, Ma ad Cyberetcs (SMC 2009); (Sa Atoo, TX, USA) : Pscataay, NJ : IEEE, ISBN S DOI:

2 Proceedgs of the 2009 IEEE Iteratoal Coferece o Systems, Ma, ad Cyberetcs Sa Atoo, TX, USA - October 2009 Persoalzed Currculum Composto by Learer Profle Drve Data Mg Raer Kauf Faculty of Computer Scece ad Automato Ilmeau Uversty of Techology Ilmeau, Germay Yoshtaka Sakura, Setsuo Tsuruta, Kouhe Takada, Shch Doh School of Iformato Evromet Tokyo Dek Uversty Chba Ne To, Japa Abstract The paper s focused o modelg, processg, evaluatg ad refg processes th humas volved lke (ot oly, but also e-) learg. A formerly developed cocept called storyboardg has bee appled at Tokyo Dek Uversty (TDU) to model the varous ays to study at ths uversty. Alog th ths storyboard, e developed a Data Mg Techology to estmate success chaces of currcula. Here, e troduce a learer proflg cocept that represets the studets dvdual propertes, talets ad prefereces persoalzed data mg. Keyords modelg learg processes, storyboardg, persoalzed data mg, learer modelg I. INTRODUCTION Geerally, learg systems suffer from a lack of a explct ad adaptve ddactc desg. Uversty educato s especally effected by ths lack, because uversty professors are ot ecessarly educatoal experts. Oe ay of ddactc support s provdg a modelg cocept for ddactc desg, hch allos the atcpato of the learg processes. Sce e-learg systems are dgtal by ther very ature, ther troducto rases the ssue of modelg ddactc desg a ay that offers the possblty to apply Koledge Egeerg (KE) techques such as Mache Learg (ML) ad Data Mg (DM). For ths purpose, the modelg cocept eeds to be a at least sem-formal koledge represetato. A modelg cocept called storyboardg [] has bee developed formerly as a meas of modelg learg processes. Besdes provdg ddactc support, ths sem-formal model s settg the stage to apply KE techologes to verfy ad valdate the ddactcs behd a learg process. The verfcato may clude both logcal cosstecy ssues ad formally to check ddactc ssues. A storyboard provdes a road map for a lesso, a course, a subject to teach, or a complete study. Accordg to dfferet learg ad teachg prefereces, t cludes alteratve paths ad possble detours f certa cocepts to be leared eed reforcemet. Usg moder meda techology, a storyboard The authors grateful thak the Japa Socety for the Promoto of Scece (JSPS) for the geerous support of ths research by a Aard-Felloshp for Raer Kauf (Fello- ID S-08742). also plays the role of a server that provdes the approprate cotet materal he deemed requred. By storyboardg, ddactcs ca be refed accordg to revealed eakesses ad prove excellece. Successful ddactc patters ca be explored by applyg DM techques to the varous ays studets et through a storyboard ad ther related success. As a result, future structors ad studets may utlze these results by preferrg those ays through a storyboard, hch tured out to be the most promsg oes. I [2], a koledge mg techology, hch allos studets to utlze med experece of former studets to compose currcula th a optmal success chace, s troduced. Hoever, dvdual learg plas should ot oly be based o the success of former studets ho et smlar ays. Addtoally, dvdual propertes, talets ad prefereces should be cosdered. For example, some studets are more taleted for aalytcal challeges, some are more successful creatve or composg tasks, ad others may have a extraordary talet to memorze a lot of factual koledge. Cosequetly, e eed to clude dvdual learer profles to avod lavshg the studets th suggestos that do t match ther dvdual prefereces ad talets. The paper s orgazed as follos. Secto 2 troduces the storyboard cocept cludg the preset state of the curret developmet. Secto 3 provdes a overve o our Koledge Mg techque to compose optmal currcula for uversty studes. I secto 4, curret approaches of adaptato trough user modelg are dscussed ad summarzed. Secto 5 troduces ad dscusses the learer model that e derved for our applcato. I secto 6, e troduce a techology to use the derved learer model for a persoalzed currculum composto. Fally, secto 7 provdes a summary ad outlook. II. STORYBOARDING Our storyboard cocept as troduced [] ud later refed (see [2] for the latest verso). A storyboard s a ested herarchy of drected graphs th aotated odes ad aotated edges. Nodes are scees or epsodes. Scees are ot further structured, epsodes have a sub-graph as ts mplemetato. Also, there s exactly oe start ode ad oe ed ode each (sub-) graph. Edges specfy trastos /09/$ IEEE 2068 Authorzed lcesed use lmted to: TU Ilmeau. Doloaded o Aprl 26,200 at 08:56:05 UTC from IEEE Xplore. Restrctos apply.

3 betee odes ad may be sgle-color or b-color. Nodes ad edges ca carry attrbutes. A storyboard may be see as a model of a atcpated recepto process that s terpreted as follos: Scees deote a o-decomposable learg actvty that ca be mplemeted ay ay, e.g. by the presetato of a (meda) documet, opeg a tool that supports learg (URL or e-learg system) or a formal actvty descrpto. Epsodes are defed by ther sub-graph. Graphs are terpreted by the paths, o hch they ca be traversed. A Start Node of a (sub-) graph defes the startg pot of a legal graph traversg. A Ed Node of a (sub-) graph defes the fal target pot of a legal graph traversg. Edges deote trastos betee odes. There are rules to leave a ode by a outgog edge: () The outgog edge must have the same color as the comg edge by hch the ode as reached. (2) If there s a codto specfed as the edge's key attrbute, ths codto has to be met for leavg the ode by ths edge. So the colors express the depedece of ays leavg a ode from the ay of arrvg there. Key attrbutes of odes specfy applcato drve formato, hch s ecessary for all odes of the same type, e.g. actors ad locatos. Key attrbutes of edges specfy codtos, hch have to be true for traversg o ths edge. Free attrbutes specfy hatever the storyboard author ats the user to ko: ddactc tetos, useful methods, ecessary equpmet, e.g. For further formato, the reader may see [2] or [3], e.g. III. CURRICULUM COMPOSITION BY DATA MINING A basc objectve of storyboardg s to use Koledge Egeerg techologes o the (sem-) formal process models [3]. I partcular, e am at ductvely learg successful storyboard patters ad recommedable paths. Ths s some sort of meta-learg,.e. the learg of learg koledge. It s performed by a aalyss of the paths here former studets et through the storyboard [2]. To sho the feasblty ad beeft of hgh level storyboardg for ts qualfed assstace of studets sufferg from the jugle of opportutes ad costrats uversty educato, a smple prototype as developed to evaluate currcula created or modfed by the studets advace of ther study [3][2]. For ths purpose, e troduced a cocept to estmate success chaces of currcula, hch are composed by studets at the School of Iformato Evromet (SIE) of the Tokyo Dek Uversty (TDU) ther currculum plag class the frst semester. For such currcula e developed a DM techque, hch s appled to storyboard paths that (former) studets et. Based o these examples, the success chace of teded paths ca be estmated [2]. The techque cossts to steps, () costructg a decso from the examples of former studets ad (2) applyg ths decso tree to the plaed currcula. It s descrbed more detaled [2]. Hoever, dvdual learg plas should ot oly be based o dvdual quattatve capablty (lke the Grade Pot Average at TDU) or the success of former studets ho et smlar ays. Addtoally, dvdual propertes, talets ad prefereces should be cosdered. For example, some studets are more taleted for aalytcal challeges, some are more successful creatve or composg tasks, ad others may have a extraordary talet to memorze a lot of factual koledge. Therefore, e cluded dvdual learer profles to avod lavshg the studets th suggestos that do t match ther dvdual prefereces ad talets. IV. LERNER MODELING APPROACHES There are several geeral approaches the lterature, hch ca be classfed accordg to the follog subsectos. A. Modelg a Competece State I Overlay models, the learer s koledge L s cosdered as a subset of the teacher s koledge T. The dfferece T - L s cosdered as the learer s lack of koledge. The learg objectve s T. There s o mechasm to dfferetate betee the koledge the learer has ot yet grasped ad the koledge the studet has ot yet bee exposed. It does ot employ strateges to help case of mscoceptos. Dfferetal models are a more structured varat of the latter. They separate the etre doma koledge to leared by the studet (T L) ad ot-leared by the studet (T - L). Perturbato models are refed dervate of both, hch cludes mscoceptos ( ko correctly ) or errors. Costraed-based models represet both the doma ad the learer s koledge as a set of costrats. Costrats represet basc cocepts or rules of the doma. Costrats volated by the learer are recorded. Hoever, all competece state models request a explct represetato of the topcal subject koledge. Therefore, they are ot approprate for uversal models for maagg learg actvtes. B. Modelg the Learg Process Models of ths class represet studets belefs coceptual graphs. Graph reasog algorthms detfy a belef b out of a pre-defed set B = {b, b 2,, b }. These models ca be used to predct the kd of mstakes the studet ll/s lkely to make. These models also eed a represetato of doma koledge ad thus, are ot approprate for our purpose. C. Modelg Performace Trats A step aay from represetg a cocrete doma toards uversalty s a classfcato of domas to classes of sklls, hch are challeged, he learg the partcular doma koledge. Ths stll requres some doma aalyss, but o explct represetato of the koledge to be leart. Hoever, e ll sho later that ths aalyses ca be med, too. The most famous model of ths class s Garder s theory of Multple Itellgeces [5] 2. Accordg to Garder, a learer s tellgece caot be cosdered as a hole (ad rated by a sgle IQ), but varous dmesos, amely () Lgustc tellgece, (2) Logcal-mathematcal tellgece, (3) Muscal tellgece, (4) Bodly-kesthetc tellgece, (5) Spatal 2 The reader may ejoy vstg (accessed o March 7, 2009) Authorzed lcesed use lmted to: TU Ilmeau. Doloaded o Aprl 26,200 at 08:56:05 UTC from IEEE Xplore. Restrctos apply.

4 (vsual, creatve-artstc) tellgece, (6) Iterpersoal (socal) tellgece, ad (7) Itrapersoal (emotoal) tellgece. D. Modelg Learg Styles Modelg learg styles has the advatage ot to deped o the doma ad s, therefore, promsg for our purpose. The most researched theory th ths category s the Felder- Slverma Learg Style model [6] 3 as llustrated Table. Table : Felder-Slverma Learg Styles Actve Reflectve Tryg somethg out Thk about materal Socal oreted Impersoal oreted Sesg Exstg ays Cocrete materal Careful th detals Vsual pctures Sequetal detal oreted sequetal progress from parts to the hole Itutve Ne ays Abstract materal Not careful th detals Verbal spoke ords rtte ords dffculty th vsual style Global overall pcture o-sequetal progress relatos/coectos Each learer s characterzed by a specfc preferece for each of these four dmesos: Abstracto: Sesory learers prefer cocrete, practcal, ad procedural formato. They look for the facts. Itutve learers prefer coceptual, ovatve, ad theoretcal formato. They look for the meag. Percepto: Vsual learers prefer graphs, pctures, ad dagrams. They look for vsual represetatos of formato. Verbal learers prefer to hear or read formato. They look for explaatos th ords. Iferece: Actve learers prefer to mapulate objects, do physcal expermets, ad lear by tryg. They ejoy orkg groups to fgure out problems. Ths s a more ductve ay to lear. Reflectve learers prefer to thk thgs through, to evaluate optos, ad lear by aalyss. They ejoy fgurg out a problem o ther o. Ths s a more deductve ay to lear. Perspectve: Sequetal learers prefer to have formato preseted learly ad a orderly maer. They put together the detals order to uderstad the bg pcture emerges. Global learers prefer a holstc ad systematc approach. They see the bg pcture frst ad the fll the detals. E. Modelg Cogtve Trats Recet modelg approaches go oe step closer to the geess of learg behavor ad model cogtve trats such as 3 Oe may check hs learg style at (accessed o March 7, 2009) doe [7]. Ths approach also ejoys the advatage to be doma depedet. [7] troduces a Cogtve Trat Model (CTM), hch cludes three tems, amely () Workg Memory Capacty (WMC), (2) Iductve Reasog Ablty (IRA), ad (3) Dverget Assocatve Learg Ablty (DALA). From a more techcal pot of ve, WMC s comparable th a Radom Access Memory (RAM), but huma orkg memory s ot just a memory, but has some processg abltes as ell, IRA refers to Iductve Iferece respectvely Data Mg, f oe refras from 00% hypotheses cosstecy th the data, ad DALA refers to Aalog Iferece ( assocatve ) respectvely Case Based Reasog (CBR) th takg to accout dffereces ( dverget ). For these trats, the doma-depedece ad persstece over tme s sho [7]. Furthermore, behavor patters are descrbed, hch dcate the partcular trat, ad they are gathered a learg system as ell as by eb-based psychometrc tools. Also, [7] derves so called Mafestatos of Trats (MOT), hch are typcal (ot oly learg) behavor patters. From these MOTs, fve typcal behavor patters (mplemetato patters) are derved, hch ca be detfed by a aalyss of the learers teracto th the mache. As a example, Table 2 shos the mplemetato patters for WMC. Table 2: Implemetato Patters for WMC Workg Memory Capacty lo hgh o-lear avgatoal lear avgatoal patter patter 2 costat reverse rare (or oe) reverse avgato avgato 3 uable to lear from able to lear from excursos excursos 4 uable to perform able to perform smultaeous tasks smultaeous tasks 5 effcet retreval from effcet retreval from log term memory log term memory F. Mxed Modelg Approaches There are also commoly used approaches hch are of a mxed type such as the IMS Learer Iformato (LIP) 4 ad the model of Mödrtscher ad Bruslovsk 5, hch suffer from both the eed of doma represetato ad cotag dvdual learer formato that s dffcult to apprase our applcato. V. DERIVED LERNER MODEL Performace based models bag the rsk, that the oe or other performace s a result of hard trag, but ot a atural talet. Vce versa, a bad performace a partcular task does ot ecessarly mea, the performer s geerally ot able. It 4 See accessed o February 2, See dard_2004a.pdf, accessed o February 9, Authorzed lcesed use lmted to: TU Ilmeau. Doloaded o Aprl 26,200 at 08:56:05 UTC from IEEE Xplore. Restrctos apply.

5 may also have ts reasos beg too tred, beg metally occuped by aythg else, or just beg ot terested. Hoever, the tegrato to our applcato (hch s descrbed belo) heavly depeds o a profle acqusto by a questoare test by most successful studets each subject. I [7] the developmet of ole tests s reported. Hoever, e dd ot have the opportuty so far to spect these tests ad to check, hether they are approprate for our applcato scearo. Also, e are ot sure, hether these eb-based tools are avalable over the log tme of expermetato. Wth respect to the requremet to be doma depedet for use uversal learg systems, learg style models also seem approprate for our applcato. Learg styles are exactly the pot e am at, he modelg learg processes. O the other had, our applcato (currculum plag) seems to be more drve by competece trats, hch are doma-related, but do t eed a explct represetato of the doma koledge. We have a tedecy to assume that learg styles eed to be focused for fe graed learg processes (a lecture, a course) ad competece trat models are more approprate f hgher level (coarse graed) learg processes (such as a complete study) modelg. So far, e cosder both our model, hch s defed as a array of attrbute-value pars that cotas 7 tellgece attrbutes ad 4 learg style attrbutes. Both ca be apprased by questoares that are avalable to the publc the eb (see foototes 2 ad 3). To make the dmesos of both sources comparable to each other ad see the quattatve relatos, e ormalzed them a ay that they all have the same rage of values. The tellgece dmesos rage from 0 to 40. The learg style dmesos rage from - to + (opposte algebrac sg for opposte styles). The ormalzato ca be doe by v = result/40 for the tellgece dmesos ad v=(result+)/22 for the learg style dmesos. Fally, our learer model looks as sho Table 3. Table 3: Derved learer profle d Lgustc tellgece 0 v d 2 Logcal-mathematcal tellgece 0 v 2 d 3 Muscal tellgece 0 v 3 d 4 Bodly-kesthetc tellgece 0 v 4 d 5 Spatal tellgece 0 v 5 d 6 Iterpersoal tellgece 0 v 6 d 7 Itrapersoal tellgece 0 v 7 d 8 Actve vs. Reflectve style 0 v 8 d 9 Sesg vs. Itutve style 0 v 9 d 0 Vsual vs. Verbal style 0 v 0 d Sequetal vs. Global style 0 v Oe could argue the use of explct questog techques s ot approprate. They bag the rsk of cultural bas, acquescet resposes, ad ter-group bas. I fact, a trace of the learers teracto s more relable tha questoare results, because t s drve by oly the learers terest learg ad t s lmted to exactly hat e at to ucover, the learers cogtve capactes ad prefereces. I our applcato, e just have the trace of studets through the storyboard,.e. the studets paths alog th ther performace level each subject. Ideed, performace levels subject do t say aythg about learg prefereces. The oly ay to derve learg related propertes from t s attachg guessed propertes of successful learers to each subject. There are subject-related successful studets, hch have a hgh ablty uderstadg the doma of ths partcular subject. Ths dcates that ther trats are approprate for uderstadg especally ths subject especally the ay ths subject s taught by ts partcular teacher. The assumpto s that there s a lk betee typcal "competece trats" (accordg to Gardeer) ad subjects that typcally challege the oe or other "kd of tellgece" more tha others ad typcal teachg methods (accordg to Felder ad Slverma) ad subjects that are typcally taught th these methods. Learers th a hgh logc-mathematcal tellgece may typcally be top Mathematcs ad Physcs, learers th a hgh spatal (vsual, creatve-artstc) tellgece may be good Geometry ad Cvl Egeerg, learers th a hgh terpersoal tellgece may be good Socal Scece ad so o. Accordgly, learers th a more vsual learg style may be top subjects, for hch pcture-lke materal s predomatly, learers th a more actve learg style may be top subjects, hch teamork s predomat ad so o. We do ot presume to apprase these lks, because ths as qute arbtrary. Therefore, e developed the dea to me ths lk by cosderg the successful studets each subject. Ho to guess profles of studets ad lk t to subjects? There s o ay to ask all studets to reveal dvdual data by questoares. I partcular, studets, ho have somethg to hde (a bad performace), ll ot cooperate. Hoever, studets th a very excellet performace a certa subject, ho are avalable (.e. stll at the uversty) may be cooperatve. So e could ask oly the very successful studets of each subject th maxmum grade pots to fll the questoares o the multple tellgeces dstrbuto ad the learg styles as metoed above o a volutary bass. Wth ther data, e could look, hether there s a correlato betee learg success a partcular subject ad the profle of ts successful learers. If so, a typcal success profle P(s) for each subject s ca be estmated by the average profle of ts most successful learers L(s):={l: learer l receved maxmum Grade Pots subject s ad provded 207 Authorzed lcesed use lmted to: TU Ilmeau. Doloaded o Aprl 26,200 at 08:56:05 UTC from IEEE Xplore. Restrctos apply.

6 questoare asers} th dvdual successful learers l L(s), =... L(s) profles accordg to the above profle defto as a -dmesoal array p(l )=[d, d 2,, d ]: p( s) = L( s) L( s) d = L( s) d = 2 L( s) d = Ths calculato has to be doe for each subject separately ad the set of most successful studets dffers from subject to subject, of course. The dea behd s to me a "typcal success profle" for each subject separately. All (other) learers l L(s) profles p(l )=[d, d 2,, d ] are estmated as a (success-) eghted average value of each profle dmeso over all subjects the studet took so far. The eght factor s the success the related subject; t should be for subjects th best marks ad 0 subjects hch the studet faled. Let S = {s, s 2,, s m } be the set of subjects the learer l took so far, succ j be the success degree of learer l subject s j th.00, f learer receved subject j mark S 0.80, f learer receved subject j mark A 0.60, f learer receved subject j mark B succ j = 0.40, f learer receved subject j mark C 0.20, f learer receved subject j mark D 0.00, f learer receved subject j mark E p(s j )=[d j, d j 2,, d j ] be the profle of the subject s j med from ts most successful learers as descrbed above. The, a learer s profle p(l )=[d, d 2,, d ] ca be estmated (med) from the subject profles p(s j )=[d j, d j 2,, d j ] of subjects that the learer took (s j S ) by S j ( succ * ) j = j d S j p( l ) : = ( succ * ) j = j d2 S succ j S j j = ( succ * ) j = j d Fally, e have a profle p(l )=[d, d 2,, d ] for each studet ad a profle for each subject p(s j )=[d j, d j 2,, d j ]. The latter oe s related to ts most successful learers. The etre approach s a mxed techology,.e. t uses both questoares for the studets th very sgfcat data (most successful studets) studets traces for all other studets. The very best studets of each subject are very sgfcat for our techology, because ther profle s obvously very approprate for a partcular subject taught ad examed a partcular ay by ts partcular teacher. Therefore, they form the typcal success profle of ths subject. VI. INTEGRATION OF THE MODEL Frst of all, e have to establsh a cogtve profle for each volved learer by partly the questoares for a fe of them ad the computato of ther aalyss result for all other learers as explaed above. The geeral ay to evaluate a submtted currculum pla as brefly outled secto 3 s uchaged. Hoever, the evaluato of a submtted currculum ad the suggesto to optmze t s doe oly th those (former) studets (hose paths are represeted the decso tree), hch have a smlar learer profle. What s smlarty, our applcato settg? Studets have a smlar profle, f they have smlar talets ad smlar eakesses. I other ords, ther profles are smlar, f the quattatve relatos -betee the profle attrbutes are about the same. Techcally, to profles are smlar, f ther (- dmesoal) vectors pot almost the same drecto th the -dmesoal Eucldea Space. For easy comparso of smlartes, t should be just a scalar value. There are several approaches to determe a scalar smlarty betee to vectors X = [x, x 2,, x ] ad Y = [y, y 2,, y ]: () Cose Coeffcet 2* (2) Dce-Coeffcet (3) Jaccard-Coeffcet (4) Overlap-Coeffcet k k = 2 k = * x y * * m( + k 2 k = * m(, ), ) * For (2), (3), ad (4) zk, f zk 0 =. zk 0, otherse The Dce Coeffcet ad Jaccard Coeffcet measure are ot approprate for our applcato for to reasos. () They cosder o-zero values oly. I our applcato, zero values ca occur ad have a meag, too. (2) Smlartes are ot comparable, because the value rage of the smlarty depeds o the values the vectors. I our applcato, e at to compute a umber of most smlar profles. Thus, smlartes must be comparable th each other. The Overlap-Coeffcet s more or less a coutg of dmesos, hch oe vector has a loer value tha the other oe. Also, t does ot cosder the degree of the 2072 Authorzed lcesed use lmted to: TU Ilmeau. Doloaded o Aprl 26,200 at 08:56:05 UTC from IEEE Xplore. Restrctos apply.

7 dfferece. Ths Coeffcet may be good for statemets about the overall Itellgece, but ot for comparg the more detaled complete profles. Fally, the Cose - Coeffcet meets our requremets. Smlarty of profles should mea smlar relatos -betee ts compoets. Ths s exactly, hat the Cose Coeffcet measures, the cose of the agel betee the vectors, hch s for detcal vectors ad zero for orthogoal oes. To clude the profles ad ther smlartes the currculum evaluato (see secto 3), e modfy the currculum evaluato procedure as follos. The procedures to costruct a decso tree ad to use ths tree for currculum evaluato stay uchaged. The oly dfferece s that e costruct the decso tree exclusvely from learers th profles that are most smlar to the oe uder evaluato. Ho to compose the subset S sm S most smlar studets? Oe ay s to state a trgger value of smlarty sm m, at ad belo hch all paths belog to S sm : S sm := { p: sm(p, p eval ) sm m } From our pot of ve, ths s a lttle arbtrary. Such a value s dffcult to determe. Maybe, after gag suffcet data from practce, ths may be approprate. So far t s ot. A ay aroud the above problem s to use the k- earest eghbor method (k-nn method) ad state a umber k of studets, ho s paths are most smlar to the submtted path: S sm =k p sm S sm p (S \ S sm ): sm(p sm,p eval ) sm(p,p eval ) Ths s qute arbtrary, too. Also, determg a reasoable value for such umber s at best possble after havg a suffcet amout of data, too. Addtoally, ths approach does ot ork, t there are too fe studets, ho s paths form the decso tree. A soluto that avods all above metoed drabacks, s to state a porto (a percetage prc) ho s paths are most smlar to the submtted oe: S sm = prc/00 * S p sm S sm p (S \ S sm ): sm(p sm,p eval ) sm(p,p eval ) By dog the latter, both of the follog performace features are guarateed: () The estmato of success chaces s based o dvdual prefereces, talets, ad eakesses. (2) The suggesto of a remag learg path (subjects recommeded to optmze the success of study) s adapted to dvdual propertes, because t s calculated o the base of examples th a smlar profle. VII. SUMMARY AND OUTLOOK The research reported here s focused o modelg, processg, evaluatg ad refg processes th humas volved lke (ot oly, but also e-) learg. A formerly developed cocept called storyboardg s brefly troduced. Alog th a storyboard applcato, e developed a techology to estmate success chaces of currcula, hch are composed by studets. Hoever, dvdual learg plas should ot oly be based o dvdual quattatve capablty or the success of former studets ho et smlar ays. Addtoally, dvdual propertes, talets ad prefereces should be cosdered. Here, e addressed ths pot by troducg to ths techology. Extesos of ths techology by dvdual cotexts lke () the educatoal hstory before eterg the uversty, (2) career plas,.e. a desred kd of posto after the uversty study, ad (3) socal ssues such as famly status, resdetal area, ad so o, are uder developmet Cotext Based Reasog (CxBR) Techologes are cosdered to derve optmal currcula. ACKNOWLEDGMENT The authors grateful thak the Prof. Kshuk, Athabasca Uversty Athabsca, Caada. He s a teratoally respected expert Learg Systems geeral ad ther adaptvty ad persoalzato partcular. He joed our commo ork at TDU for a couple of days ad provded very valuable hts ad suggestos ad a oderful overve o the latest state of the art ths feld. REFERENCES [] K.P. Jatke ad R. Kauf, Ddactc desg though storyboardg: Stadard cocepts for stadard tools, 4th Iteratoal Symposum o Iformato ad Commucato Techologes, Workshop o Dssemato of e-learg Techologes ad Applcatos, Cape To, South Afrca, 2005, Ne York: ACM Press, ISBN X, pp [2] R. Kauf, R. Böck, Y. Sakura, S. Doh, ad S. Tsuruta, Koledge mg for supportg learg processes, The 2008 IEEE Iteratoal Coferece o Systems, Ma, ad Cyberetcs (SMC 2008), Sgapore, 2008, IEEE Catalog umber CFP08SMC-USB, ISBN , Lbrary of Cogress: , IEEE, Pscataay, NJ, USA, pp [3] Y. Sakura, S. Doh, S. Tsuruta, R. Kauf, R., Modelg Academc Educato Processes by Dyamc Storyboardg, press, Joural of Educatoal Techology & Socety, vol. 2, ISSN (ole) ad (prt). Iteratoal Forum of Educatoal Techology & Socety (IFETS), [4] R. Kauf, Y. Sakura, S. Tsuruta, Applyg Koledge Egeerg Methods to Ddactc Koledge - Frst Steps toards a Ultmate Goal, Proc. of 2008 IEEE World Cogress o Computatoal Itellgece (WCCI 2008), Iteratoal Jot Coferece o Neuroal Netorks (IJCNN 2008), Specal Sesso o Computatoal Itellgece Approaches for E-Learg, Hog Kog, IEEE Catalog Number CFP08IJS-CDR, ISBN , ISSN , pp [5] H. Garder, Frames of Md: The Theory of Multple Itellgeces, Basc Books, 993. [6] R.M. Felder, L.K. Slverma, Learg ad teachg styles egeerg educato, Egeerg Educato, 78(7), 988, pp [7] T.-Y. L, Cogtve Trat Model for Adaptve Learg Evromets, Ph.D. Tess, Massey Uversty, Palmersto North, Ne Zealad, Authorzed lcesed use lmted to: TU Ilmeau. Doloaded o Aprl 26,200 at 08:56:05 UTC from IEEE Xplore. Restrctos apply.