A LOM Research Agenda

Transcription

1 A LOM Rsarch Agnda Erik Duval Dpt. Computrwtnschappn, K.U.Luvn Clstijnnlaan 200A B-3001 Luvn, Blgium Wayn Hodgins Autodsk Inc. & Larnativity 258 Eucalyptus Rd. Ptaluma, CA 94952, USA ABSTRACT This papr prsnts a rsarch agnda on Larning Objcts. Th main intnt is to laborat on what th authors considr important issus for rsarch on larning objcts and thir us in ducation and training. Th papr focuss somwhat on mtadata rlatd issus, but dos not rstrict itslf to only thos aspcts that hav a dirct rlationship with mtadata. Catgoris and Subjct Dscriptors K.3 [Computing Miliux]: Computrs and Education; H.3.5 [Information Storag and Rtrival]: On-Lin Information Systms Wb-basd srvics; H.4.m [Information Systms]: Miscllanous Gnral Trms larning objcts Kywords mtadata, larning, training, ducation, knowldg managmnt, Larning, library and information scinc, information managmnt, contnt managmnt, adaptiv hyprmdia, larning tchnology standardization 1. INTRODUCTION In a vry gnral sns, it has rpatdly bn obsrvd [14] that th actual impact of ICT on ducation and training is rathr limitd. As an illustration, th Grand Challngs confrnc in 2002 idntifid A Tachr for vry Larnr: Scalabl Larnr-Cmtrd Education as on of th grand rsarch challngs in computr scinc. Th panl nvisiond building th tchnological infrastructur to support dynamic, ad-hoc communitis of liflong larnrs who intract within an nvironmnt of larning objcts through a crativ blnd of advancd computing tchnologis, high prformanc ntworks, authoring and collaboration tools [29]. It was stimatd that a Manhattan projct approach, with sustaind major funding ovr a dcad or longr, would b ndd to finally raliz this long standing dram. Sinc rcnt yars, much of th rsarch in this ara has focusd on th notion of rusabl multimdia contnt componnts, rfrrd to as larning objcts. Th driving forc stms from th notion that rus of such componnts can lad to important savings in tim and mony, and nhanc th quality of digital larning xprincs: th nd rsult would b fastr, chapr and bttr larning. In fact, diffrnt kinds of rus can b distinguishd: Copyright is hld by th author/ownr(s). WWW2003, May 20 24, 2003, Budapst, Hungary. ISBN multipl output/distribution formats, mdia, outputs; multipl purposs (training, prformanc support, documntation, markting, sals); multipl dlivry; multipl disciplins or markt sgmnts. A somwhat mor spcific trm is rpurposing, which can b thought of as th ability to us, without any (significant) changs, th sam pic of contnt for a purpos significantly diffrnt than that it was originally intndd for whn cratd. Early work in this ara includd th Educational Objct Economy [22, 4] and Ariadn [16, 1]. Sinc thn, and spurrd by th dvlopmnt of th Larning Objct Mtadata standard by th IEEE Larning Tchnology Standards Committ (IEEE LTSC LOM), numrous initiativs hav bn launchd in acadmic and corporat contxts - though ths two worlds rmain mor isolatd from ach othr than th authors considr ncssary or halthy [21]. This papr tris to idntify th main rsarch issus in this ara, grouping rlatd issus togthr, and indicating th main barrirs for widsprad larning objct rus at this momnt. As such, this proposal is part of a long tradition of paprs such as [17] and [25]. In th following sctions, w first dal with th notion of Larning Objcts (LO s) and Larning Objct Mtadata (or LOM). In sction 4, w dal with authoring aspcts of LO s and LOM. Sction 5 dals with accss to LO s. In ordr to nabl widsprad r-us, introprability issus ar xtrmly important. Thy ar dalt with in sction 6. Sctions 7.1 and 7.2 dal with issus that hav a high impact on th actual adoption in practic of a LO basd approach: th dvlopmnt of powrful tools and appropriat businss modls. 2. BEYOND DOCUMENTS 2.1 Introduction Thr is a widsprad tndncy to quat LO s with documnts, typically rprsntd as a fil or a st of fils. Th authors bliv that it is quit appropriat to considr documnts and fils as a form of output and dlivry of LO s. Howvr, it is vry rstraining to only think of LO s in this way. In fact, th mor flxibl and advancd applications of LO s go wll byond th simpl documnt paradigm. A simpl kind of xtnsion is to think of mor sophisticatd LO s than simpl, static documnts. A simulation, for instanc, allows for dynamic adaptation to usr intraction. Th arly work on th Educational Objct Economy focusd on th us of javabans as rusabl componnts that could intract with ach othr (s also

2 sction 6.2). This gos wll byond currnt practic, whr th unit of rus is typically a complt simulation, rathr than a componnt throf. Th us of javabans or othr componnt tchnologis alligns th concpt of LO s much mor with that of th notion of objcts in th objct orintd programming paradigm. 2.2 Rsarch Issu 1 - A Larning Objct Taxonomy According to th Larning Objct Mtadata (LOM) standard, a larning objct is any ntity, digital or non-digital, that may b usd for larning, ducation or training [9]. This dfinition allows for an xtrmly wid varity of granularitis. This mans that a larning objct could b a pictur of th Mona Lisa, a documnt on th Mona Lisa (that includs th pictur), a cours modul on da Vinci, a complt cours on art history, or vn a 4 yar mastr curriculum on wstrn cultur. In on sns, this is appropriat, as thr ar a numbr of common thms to contnt componnts of all sizs. In anothr sns though, this vagunss is problmatic, as it is clar that authoring, dploymnt and rpurposing ar affctd by th granularity of th larning objct. In ordr to addrss this problm, a larning objct taxonomy or a st of such taxonomis should b dvlopd to idntify diffrnt kinds of larning objcts and thir componnts. W hav dvlopd a first starting point for such a taxonomy. It is important to not that this taxonomy applis to multipl applications. Th first two lvls ar application domain indpndnt, and can for instanc also b dployd in th fild of tchnical documntation. Only th third and fourth lvl ar spcific to th fild of larning. 1. Raw Mdia Elmnts ar th smallst lvl in this modl: ths lmnts rsid at a pur data lvl. Exampls includ a singl sntnc or paragraph, illustration, animation, tc. A furthr spcialization of this lvl (or complmntary taxonomy?) will nd to tak into account th diffrnt charactristics of tim-basd mdia (audio, vido, animation) and static mdia (photo, txt, tc.). 2. Information Objcts ar sts of raw mdia lmnts. Such objcts could b basd on th information block modl dvlopd by Horn [18]. Whil Horn s modl rfrs to txt and illustrations (as it is basd on pionring work in th mid 1960 s!), th plan is to gnraliz th concpts to dal with mor advancd and innovativ contnt. 3. Basd on a singl objctiv, information objcts ar thn slctd and assmbld into th third lvl of Application Spcific Objcts. At this lvl rsid larning objcts in a mor rstrictd sns than th aformntiond dfinition of th LOM standard suggsts. 4. Th fourth lvl rfrs to Aggrgat Assmblis that dal with largr (trminal) objctivs. This lvl corrsponds with lssons or chaptrs, which can in turn b assmbld into largr collctions, lik courss and whol curricula. Clarly, information objcts contain raw mdia lmnts. Larning objcts contain information objcts. Aggrgat assmblis contain larning objcts and othr aggrgat assmblis. Th smallr lvl of granularity in this taxonomy is ssntial, as w bliv that rpurposing can only b accommodatd by xplicitly idntifying th information objcts and th raw mdia lmnts thy contain. 2.3 Rsarch Issu 2: Larning Objct Componnt Architctur In ordr to raliz th full potntial of dynamic composition of larning objct componnts, it is ncssary to dvlop a flxibl architctur that nabls: structuring of larning objcts and thir componnts: An important gnral principl in hyprmdia systms is th sparation of contnt, structur and prsntation. Structural aspcts of aggrgat larning objcts can b basd on th IMS Contnt Packaging spcification [5], th Educational Modling Languag (EML) [19], th Synchronizd Multimdia Intgration Languag (SMIL) [8] and DocBook [27]. Innovativ approachs to larning objct structuring should includ dynamically gnratd componnts, basd on som procssing of (smi-)structurd data. A simpl xampl is a larning objct on wathr forcasting that maks us of a st of currnt satllit imags rlvant to th location of th larnr, accssing simpl data about th larnr and th tim that th larning is to tak plac to rtriv a rlvant st of imags, so as to ralis an authntic and thus an ffctiv and fficint xprinc. This kind of contxt points to th nd for structurs that idntify rlvant componnts through sarch critria, rathr than just listing th idntitis of th componnts. A mor complx xampl would accss information about th rol of th larnr in th organisation, his prsonal goals and thos of th organisation, his agnda, tc. to gnrat a highly customizd larning objct that would b rlvant for th task at hand, and that would tak into account th constraints (tim, languag, cost, location, tc.) that influnc th particular contxt in which th larning is to tak plac. This approach to prsonalizd larning is closly rlatd with th fild of adaptiv (ducational) hyprmdia, intllignt tutoring systms, tc. [11]. Thr ar som succsss in rlativly constraind domains [20], but it is clar that much rsarch is ndd bfor gnrally applicabl, larg-scal implmntations can b dvlopd. intractions btwn larning objcts and thir componnts: Th original EOE approach focusd on JavaBans, whr such intractions wr basd on a common API [22]. Th us of javabans or othr componnt tchnologis aligns th concpt of larning objcts much mor with th quivalnt notion of objcts in th objct orintd programming paradigm. Consquntly, this kind of bhaviour can b thought of as a spcific application of gnral softwar nginring componnt basd approachs. In this spcific contxt, thr ar many parallls btwn rus of softwar artifacts in gnral, and larning objct rus in particular. Evn though this subjct has a long rsarch history in softwar nginring, larg-scal applications in practic rmain rlativly fw. Morovr, thy ar oftn constraind to particular domains or to particular tchnological approachs. Morovr, not only has that approach not bn adoptd so widly in th contxt of larning objcts, dpr introprability also rquirs furthr considrations on intractions btwn for instanc th modls that undrly th bhaviour of larning objcts. An xampl that illustrats this rquirmnt is that of larning objcts that simulat diffrnt parts of th human body, and that nd to xchang data btwn thm [14].

3 Corporat Wid Raw Data Mdia Elmnts Modular Contnt Hirarchy Information Objcts Application Objcts ( Larning Objcts, Support Objcts, Markting, Rfrnc, tc.) Application Spcific Profils Aggrgat Assmblis ( Lssons, Chaptrs, Units, Brochur, tc.) Audi o Txt Procdur Procs Principl s Procdur Principl Concp illustratio t Procs Objctiv Objctiv Concp n s t Fact Summar Animatio Ovrvi y Fact n w Ovrvi Summar w y Enablin Trmin Simulatio g Objctiv al Objctiv n + MOST + - LEAST Larnativity Allianc - LEAST - Collctions (Storis, Courss, Books, Movis) Thm REUSABILITY CONTEXT + MOST +

4 3. LEARNING OBJECT METADATA 3.1 Introduction In th Larning Objct Mtadata standard, a hirarchical structur is dfind of 9 catgoris. Each of th catgoris groups rlatd data lmnts that covr spcific aspcts, such as tchnical or ducational charactristics [9]. It is important to not that, in LOM, all data lmnts ar optional and that th LOM structur can b xtndd (s also sction 3.3). Th two main rsarch issus w dal with in this sction rlat to th nd for mpirical analysis of actual mtadata usag (now that LOM bgins to b dployd on a larg scal), and to th nd for guidanc on th dvlopmnt and us of application profils that adapt LOM to th nds of a spcific community. 3.2 Rsarch Issu 3: Empirical Analysis Thr is a high dgr of subjctivity in many of th LOM data lmnts. This is somtims prcivd as a problm, but it is th opinion of th authors that thr is no nd to avoid subjctivity in th dscriptions of LO s; in fact, th subjctiv mtadata ar oftn among th mor intrsting ons. In othr words, th subjctivity of many mtadata lmnts is a fatur, not a bug. Th subjctiv mtadata ar oftn th most valuabl for incrasing th ffctivnss of sarchs and idntifying th most rlvant contnt for a givn individual or situation. As a simpl illustration, it may b mor rlvant to know that a particular prson rcommnds a particular LO (highly subjctiv mtadata) than to know th xact siz of that LO in kilobyts (highly objctiv mtadata). Howvr, organisations oftn provid good practic documnts, so as to giv guidanc to thir community of usrs. Typical xampls includ CANCORE [2] and SINGCORE [7]. As such documnts start to b dvlopd and applid, analysis is ndd of th similaritis and diffrncs btwn such guidlins, and thir rational (in rfrnc to th rquirmnts of th intndd communitis, or othrwis). Morov, it would b xtrmly usful to collct and analys mpirical data about th actual us that is mad of mtadata, by diffrnt classs of nd usrs, including: indxrs that gnrat mtadata, dscribing LO s; sarchrs for rlvant LO s, who typically dfin sarch critria ovr mtadata lmnts. Such analysis could furthr our undrstanding of actual us cass. Usful approachs includ log analysis of LO Rpositoris (s also sction 6.4), usability studis of LO(M) tools (s also sction 7.1), analysis of th actual contnt of LO rpositoris, such as th kind of LO s, th actual mtadata, th actual amount of rus, th actual annotations by usrs who provid fdback on thir rus of LO s, tc. A rar xampl of a simpl initial analysis along ths lins can b found in [15]. Anothr approach would b to analys th diffrncs and similaritis btwn mtadata authord by indpndnt indxrs. In principl, as thr can b mor than on LOM instanc for a LO, this kind of analysis can b carrid out a postriori. In practic, it sms lik thr ar not many LO s with multipl LOM dscriptions. Consquntly, it sms that, at this momnt, this approach will hav to b basd on spcifically st up xprimnts. Such xprimnts ar rathr similar to xprimnts on pr rviws of for instanc papr submissions [26]. 3.3 Rsarch Issu 4: Application Profils It was notd in sction 3.1 that LOM is xtnsibl. In ssnc, nw data lmnts can b introducd anywhr in th LOM hirarchy. An altrnativ xtnsion mchanism is th classification catgory for listing of sourcd taxonomic stairways in arbitrary classifications. An altrnativ mchanism to adapt LOM is that of application profils that nabl incrasd smantic introprability in on community, in a way that prsrvs ful compatibility with th largr LOM contxt. Th fundamntal tchniqus for th dfinition of application profils includ: giving lmnts a mandatory status: Thr is in fact a rathr larg dgr in th slction of mandatory lmnts in diffrnt application profils, such as ARIADNE [1], CANCORE [2], SCORM [6], and othrs. rstricting th valu spac of data lmnts: Th LOM standard is intndd to b broadly applicabl. This mans that som of th availabl options may not b rlvant in spcific contxts. A typical xampl is th data lmnt 2.3.1:Lif- Cycl.Contribut.Rol that includs valus lik ducational validator. In many contxts, that rol can b omittd. (On could argu that such irrlvant valus can just as wll b rtaind in th application profil. In fact, bcaus many of th tools rmain so clos to th spcification thy support - s also sction ths irrlvant valus ar oftn xposd to nd usrs, and thus compromis th usability of LOM.) imposing rlationships btwn lmnts: In th contxt of particular communitis, it may mak sns to impos additional rlationships btwn LOM data lmnts. As an xampl, th ARIADNE application profil mandats that, if 6.1:Rights.Cost quals ys, thn th st of data lmnts undr 2.3:LifCycl.Contribut with 2.3.1:LifCycl.Contribut.Rol qual to publishr or author bcoms mandatory. Clarly, th ida is that, if paymnts ar involvd, th rights holdr (th author or publishr in th ARIADNE contxt) should b known. not including som LOM lmnts: As all data lmnts ar optional, a community may dcid not to mak us of som lmnts. Altrnativly, th valu of a particular data lmnt may b fixd for a particular community: as an xampl, th ducational contxt will always b that of a univrsity for a community that only dals with that contxt. In that cas, th data lmnt nd not b prsntd to th nd usr for whom th contxt of us is thus rstrictd. idntifying taxonomis and classifications: LOM includs a catgory that supports th us of arbitrary taxonomic stairways, in gnral classification structurs, for a purpos that can b xplicitly dfind. In th ARIADNE application profil, for instanc, a dynamically updatd classification structur for th structuring of human knowldg is rfrncs [1]. Dvloprs can gnrat considrabl addd valu for thir communitis through application profils: th rsult can b a mor asily applicabl vrsion of LOM, with highr smantic introprability within a community, without compromising ovrall xchang of LOM instanc with thos who do not blong to th community. Howvr, thr ar important qustions about th propr ways to dfin application profils, about how to translat from on such profil to anothr on (through a LOM common dnominator, or dirctly?). Also, it oftn rmains unclar what kind of community

5 an application profil tris to srv, and how th charactristics and rquirmnts of that community hav influncd th dfinition of th profil. Clar guidlins on how to procd in this procss of application profil dfinition would b most usful and rlvant. 4. LO(M) AUTHORING 4.1 Introduction A numbr of issus nd to b bttr undrstood if larg scal LO (r-)us is to bcom a rality. In this sction, w focus on aggrgation (sction 4.2) and dcomposition (sction 4.4) in LO authoring, and on th notion of dsign for rus (sction 4.3). W also look at th rlatd issu of LOM authoring (sction 4.5). 4.2 Rsarch Issu 5 - Authoring By Aggrgation Traditionally, authoring tools mainly support th procss of authoring from thr points of dpartur: a blank documnt that nds to b filld with contnt, whr th structur of th LO is dfind during th laboration of that contnt; a tmplat that nds to b instantiatd, whr th structur of th LO is dfind a priori; an xisting LO that is ditd and modifid in th procss of authoring, and thn typically savd as a nw LO. Th main ida in our viw howvr is that LO s ar cratd by slcting contnt/information objcts from a rpository, usually with th significant assistanc of mtadata and profils to do so. Ths LO s can thn b assmbld into a nw LO. This can b rfrrd to as authoring-by-aggrgation. Th nw LO, as it provids nw contxt for th componnts, may nd to provid glu that taks th larnr from on componnt to anothr. A simpl xampl of this kind of facility is th way that prsntation authoring tools (lik Microsoft Powrpoint, or SliTX, tc.) allow for xisting slids to b includd in nw prsntations and thn add automatically nxt and prvious transitions btwn thos slids. Mor sophisticatd glu would nabl th author of th aggrgatd contnt to includ transitional matrial (lik In this chaptr, w will illustrat th concpt of rlativity that was introducd in chaptr V ), so as to giv guidanc to th larnr on how th componnts fit togthr in th aggrgat. This kind of glu is dalt with by squncing spcifications, that nabl th dfinition of larning paths. Ths larning paths ar thmslvs discrt componnts or objcts and as such can b stord sparatly, modifid indpndnt of th contnt, rusd AND of cours also hav thir own associatd mtadata to aid with discovry, sarch and rtrival. This can also b illustratd by rfrnc to th architctur modl prsntd in sction 2.2: th actual contnt is always authord at th objct or componnt lvl and from thr vrything is an assmbly of ths componnts. In this contxt, crating a LO is an assmbly procss which includs initially th dsign or planning of th assmbly (bluprint, larning path, tc.) and thn th cration of th actual assmbly itslf, formatting, dlivry, tc. 4.3 Rsarch Issu 6 - Dsign for Contnt Rus W mntiond in sction 4.2 that contnt authors should avoid xplicit rfrncs to othr componnts, as ths othr componnts may not b availabl in th contxt of rus. Continuing th xampl from that sction, a rfrnc to chaptr V maks littl sns if th contxt in which a componnt is rusd dosn t includ a chaptr V. W alrady mntiond that th aggrgat LO should provid this kind of glu information. This is on xampl of an issu that nds to b considrd whn contnt is dsignd to b rusabl. Thr ar many mor issus that nd to b considrd whn dsigning for rus : It should b asy to modify mathmatical symbols: th symbols usd oftn dpnd on th contxt, and thy should b consistnt within a givn contxt. So, if on contnt componnt rfrs to an angl in a diagram as α, and anothr has a visualization that rfrs to th sam angl as θ, thn th larnr will hav significant cognitiv challngs to map α to θ. Whras an xprt in th domain may b xpctd to b abl to do this kind of mapping without problms, th sam cannot b xpctd of a larnr. That is why contnt should b dsignd in such a way that it bcoms asy to transform thm from on symbolism to anothr. A rlatd issu is that of txtual labls in visual matrial: it should b simpl to rplac such labls with altrnativs, for instanc in a diffrnt languag, or using an altrnativ vocabulary. This is a problm that has bn studid in th contxt of multi-lingual usr intrfacs. Thr ar som simpl approachs that basically rly on sparation of th labls from graphical matrial, but it rmains quit difficult to dal with som of th mor subtl issus, such as th diffrncs in lngths of txtual labls or th diffrncs in rading dirction (lft-to-right vrsus right-to-lft) in diffrnt languags. It should b notd that such labls can thmslvs b considrd mtadata, that gt addd to th actual contnt whn th lattr is visualizd in a particular contxt. Gnralizing furthr, mthods nd to b dvlopd for adapting th look and fl of contnt. Whn diffrnt componnts ar aggrgatd togthr, th rsult should not look lik a collction of componnts from diffrnt origins. On could think of aggrgation tools that allow th author to apply a dsign tmplat to impos a spcific look and fl on th rsulting aggrgat. In a gnral sns, thn, dsigning contnt to b rusabl is largly a disciplin of not including any mor contxt than absolutly ncssary in th contnt itslf and instad adding in contxt by mans of th dsign of th LO, th ovrall assmbly and th Larning Path. Or, contxt can also b providd through th us of vry contxt spcific contnt such as xampls or xrciss which could rflct a vry spcific st of data, application tc. Th abov illustrats onc mor why w nd to hav a contnt modl such as that of sction 2.2, whr th rusabl componnts ar blow th LO lvl and ar ths information objcts or contnt objcts which ar pur or raw data in that thy hav littl to no contxt, no formatting and no styl. This styl and contxt is addd by combinations of th contxt of th dsign, th larning paths and/or th prsntation layrs via typical styl shts. Basic tchnologis that dal with som of th issus mntiond abov do xist. In fact, ths issus ar not that spcific to th domain of ducation and training, and approachs that support th sparation of contnt, prsntation and navigation in gnral can b applid to this contxt as wll. An important problm is that currnt authoring tools oftn ncourag usrs to produc contnt that mixs ths aspcts in a way that maks it difficult to sparat th diffrnt layrs of information aftrwards.

6 4.4 Rsarch Issu 7 - LO Dcomposition It is important to ralis that thr is a trad-off btwn rusability and addd valu of LO s in trms of granularity: smallr LO s ar mor asily rusabl, yt th addd valu on drivs from thir rus is lowr than that of largr LO s that tnd to b lss asily rusabl. That is why w nd to accomodat both largr (bcaus adding mor valu) and smallr (bcaus mor asily rusabl) LO s. Howvr, authors ar typically not so comfortabl with authoring rally small or larg LO s. Th xcption for th smallr LO s is that of photographrs that would typically focus on authoring singl picturs. In a mor gnral sns, many authors find it difficult to produc small contnt units with a wll-dfind, rstrictd scop. Thr ar som xampls of tools that allow authors to work on a lvl of granularity that thy ar comfortabl with, and thn dcompos that objct into componnts smi-automatically. In th cas of vido for instanc, scn cuts can b dtctd automatically, to suggst appropriat boundaris to sparat componnts. Txt sgmntation tools support th transformation of txt documnts into pdagogical hyprtxt [28]. Anothr xampl would tak an HTML fil with mbddd imags and xtract from that distinct componnts for th original HTML fil and th imags on th on hand, as wll as for all of th componnts on th othr hand. Th main point is that w nd bttr support for authoring of LO s by aggrgation (s sction 4.2) and for automatic dcomposition of LO s to xtract th componnts of a LO that was originally producd as an aggrgat. 4.5 Rsarch Issu 8 - LOM Gnration On of th most oftn hard critical rmarks about LOM is that contnt authors ar not willing to spnd th xtra amount of ffort to add mtadata to thir LO. Whil this may b lss tru for LO s that involv considrabl invstmnt, as th addd ffort to add mtadata thn bcoms almost ngligabl in th largr contxt, it dos prsnt a srious problm in mor artisanal sttings. W bliv that this problm can b ovrcom by tchniqus for (smi-)automatic gnration of mtadata. Svral sourcs of mtadata can b harvstd with this intnt: Th LO itslf can b procssd for mtadata. For instanc, it is a rlativly simpl procss to xtract from an HTML LO th titl, th languag usd, rfrncs to othr LO s, th nam of th author (oftn includd in th mtadata that ar insrtd by th authoring tool), tc. Morovr, sarch ngins lik Googl illustrat that xisting harvsting tchniqus can b quit powrful. Mor rsarch on mapping th rsults of ths tchniqus to th diffrnt mtadata lmnts in structurs such as LOM is ndd. Rlatd LO s cannot only b dscribd as such in th mtadata of th LO to b dscribd (using th rlation catgory in LOM). Furthr rsarch is rquird on how mtadata can propagat from on LO to a rlatd on. It sms obvious, for instanc, that th languag of a txtual componnt will most probably b th languag of th composit to which it blongs. Likwis, if thr is mtadata availabl about th cost involvd with componnt LO s, thn it sms saf to suggst that th cost involvd with th composit is at last th aggrgatd cost of all th componnts involvd. Also, xisting tchniqus for documnt clustring and txt mining nd to b rconsidrd in th contxt of xplicit mtadata, so that for instanc th mtadata of rlatd and alrady dscribd LO s can b usd to gnrat mtadata for nw LO s. Th author(s) of th LO is oftn th sourc of additional mtadata for th LO, as authors mostly produc LO s in on languag only, or in on contnt domain, or for on kind of audinc (say univrsity lvl), tc. Evn for authors that produc mor divrs LO s, th rang of rlvant valus for many lmnts can b rducd significantly whn charactristics of th author ar takn into account. Th contxt of LO authoring can also significantly rduc th numbr of rlvant options. If th mtadata authoring tool is launchd within th contxt of a cours for instanc, thn it sms rasonabl to suggst mtadata of th cours as starting valus for th mtadata of th LO. In addition, tmplats of rusabl mtadata can b cratd, whr many of th rlvant filds can b prfilld. Oftn, instantiating th tmplat will involv littl mor than simpl slction btwn a small numbr of rlvant valus for a fw rmaining filds. It is important to not that th psychological ffct of prsnting an automatically gnratd mtadata instanc and asking th nd usr to vrify that this dscription is corrct. This is a much lss intimidating proposal than bing prsntd with an mpty form that includs a larg numbr of mpty txt boxs to b filld in, as wll as many long lists of valus to b slctd from. 5. ACCESS 5.1 Introduction Assuming that a critical mass of LO s and associatd LOM instancs bcoms availabl, thr is an obvious nd to improv accss to th LO s. Most of th tools at this momnt ar basd on an lctronic form that nabls nd usrs to compos boolan combinations of sarch critria (but w know from digital library rsarch that this approach has srious usability problms) or on a simpl txt box (that fails to tak advantag of th structurd natur of LOM). What is ndd is mor rsarch on how to provid flxibl, fficint and ffctiv accss to a larg rpository of LO s, so that nd usrs can quickly zoom in on thos LO s that ar rlvant to thm. Th rsponsibility for this kind of slction can rsid in diffrnt placs: th tachr, trainr, profssor, tc. may slct th LO s. This slction typically taks plac a priori, i.. in advanc of th actual larning xprinc; th larnr may slct LO s, typically a priori in th cas of largr LO s (courss) or at th tim that th actual larning taks plac, in th cas of smallr LO s (a documnt in a modul for instanc); th larning managmnt systm may procss ruls that match charactristics of th larnr (styl, intndd larning outcom, alrady acquird knowldg and skills) against th LOM dscriptions, in ordr to slct th most appropriat LO s. 5.2 Rsarch Issu 9: Novl accss paradigms Thr is an urgnt nd for mor rsarch on novl accss paradigms that nabl an nd usr to zoom in on rlvant LO s without rquiring him or hr to go through a lngthy procss to formulat complx sarch critria, to valuat som of th rsults, rfin th sarch critria, tc. Two such paradigms ar brifly mntiond blow.

7 Information visualisation nabls nd usrs to manipulat controls ovr th mtadata, zoom in on potntially mor rlvant LO s and continuously kp an ovrviw of how additional sarch critria rstrict th rmaining numbr of LO s [13]. It is important to notic that this approach tris to mpowr nd usrs and kp thm in control of th slction procss, rathr than trying to transfr th locus of control to th Larning Managmnt Systm. A somwhat softr approach to LO slction rlis on social rcommnding tchniqus whr similaritis btwn nd usrs ar xploitd to suggst potntially rlvant LO s [30]. This approach has a numbr of intrsting faturs: This kind of suggstion xploits data about prvious intractions btwn usrs and LORs, and avoids th cognitiv burdn and tim involvd in browsing or sarching th LOR. It may suggst LO s that on would nvr find through xplicit sarchs, and thus opn th viw of th nd usr on th LOR. In both of ths approachs, LO s ar idntifid in a mor natural, lss laborious way than by filling in lctronic forms. 5.3 Rsarch Issu 10: Htrognous Sarchs A spcific and significant ara of rsarch is concrnd with sarchs across htrognous rpositoris of contnt, mtadata, larning paths and prsntation or styl information. Thr is a rally critical and so far apparntly unnoticd nd that this nw componnt basd contnt rfrnc modl rquirs: to b abl to sarch for contnt and for mtadata across any numbr of rpositoris which ar fdratd in th sns that thy can b tratd as a virtual singl rpository. Not that thr is a rquirmnt for standardization fforts in this ara to mak this work. 6. INTEROPERABILITY 6.1 Introduction Introprability can b dfind as nabling information that originats in on contxt to b usd in anothr in ways that ar as highly automatd as possibl [23]. Mor spcifically, in our contxt, introprability can b dfind as th ability for objcts from multipl and unknown or unplannd sourcs, to work or oprat tchnically whn put togthr with othr objcts. Exampls includ: contnt objcts from diffrnt original cration/authoring tools working togthr whn assmbld togthr into a LO. LO s and contnt objcts bing abl to work proprly whn movd from on infrastructur (oprating systm, LMS, tc.) to th nxt. Typically, this rquirs standardization of common protocols, formats, tc. This kind of introprability is a condition to ralis th vision of an opn, larg-scal LO infrastructur with sufficint critical mass. Introprability is rquird at diffrnt scals: btwn LO s (sction 6.2); btwn LO s and Larning Managmnt Systms (sction 6.3); btwn LOR s (sction 6.4); btwn mtadata schmas, undrstood in th rmaindr of this papr as th collction of intrrlatd data lmnts that structur a mtadata instanc (somtims rfrrd to as vocabularis of data lmnts (sction 6.5); mor gnral notion of smantic introprability, that would nabl crossing, among othrs, th boundaris of cultur in gnral and languag in spcific. 6.2 Rsarch Issu 11 - LO Introprability An important, and currntly somwhat nglctd, kind of introprability focuss on intractions btwn LO s. Th original EOE approach focusd on a JavaBans lik approach, whr introprability btwn LO s was basd on a common API [22]. Not only has that approach not bn adoptd so widly in th contxt of LO s, dpr introprability also rquirs furthr considrations on intractions btwn for instanc th modls that undrly th bhaviour of LO s. An xampl that illustrats this rquirmnt is that of LO s that simulat diffrnt parts of th human body xchang data btwn thm [14]. This kind of bhaviour can b thought of as a spcific application of gnral softwar nginring componnt basd approachs. In this spcific contxt, thr ar many parallls btwn rus of softwar artfacts in gnral, and LO rus in particular. Evn though this subjct has a long rsarch history in softwar nginring, larg-scal applications in practic rmain rlativly fw. Morovr, thy ar oftn constraind to particular domains or to particular tchnological approachs. 6.3 Rsarch Issu 12 - LMS Introprability A kind of introprability that has bn workd on much mor is that btwn a LO and a Larning Managmnt Systm (LMS). In fact, among th arlir work on industry standards in th domain of larning was th Aviation Industry CBT Committ (AICC) Computr Managd Instruction (CMI) spcification that dals with prcisly this kind of introprability. Additional work is ndd though, as som of th original assumptions undrlying that arly work no longr hold in th currnt world of distributd Wb applications. 6.4 Rsarch Issu 13 - LOR Introprability To dat, littl work has bn don on introprability btwn LO rpositoris. Th focus of this work should b on common protocols, so that clints can intract with a st of srvrs, rathr than bing dvlopd for intraction with on spcific srvr; srvrs can intract with on anothr, to xchang mtadata and LO s (as wll as potntially usr profils, and othr kinds of mta-mtadata ). Again, thr is a lot of mor gnral work in this ara that can b rusd and applid to th spcific contxt of LOR s: this includs for instanc Z39.50, and, most importantly, th Opn Archivs Initiativ (OAI): this initiativ dvlops standards that aim to facilitat th fficint dissmination of contnt. Th mtadata it rlis on ar basd on th Dublin Cor Mtadata Elmnt St. Th cor of OAI is a protocol for mtadata harvsting, that allows crawlrs to collct mtadata from a varity of sourcs An altrnativ, mor novl approach rlis on pr-to-pr tchnologis: among th main purposs is th dsir to lowr th barrirs to participat in th LO infrastructur. Initiativs in this ara includ LOMstr, Edutlla, Pool, tc. [24].

8 A vry intrsting way to dal with ths kinds of issus is to dvlop hybrid architcturs whr wrapprs around srvrs mak thm act lik supr-prs, gatways prs intract with svral kinds of prs from diffrnt ntworks, tc. 6.5 Rsarch Issu 14 - Schma Introprability As mtadata infrastructurs dvlop, th nd incrass to nabl automatic mapping btwn mtadata schmas. Th tchnology of mtadata rgistris sms to offr a promising approach to tackl this issu: th basic ida is that dscriptions of th intrrlationships btwn mtadata lmnts ar mad availabl for machin procssing. As an xampl, on can imagin that a rgistry would includ information about th dfinitions of data lmnts in th Dublin Cor Mtadata Elmnt St (DCMES) and th Larning Objct Mtadata (LOM) Bas Schma. It could also includ rfrncs from th DCMES data lmnts to th corrsponding LOM data lmnts. In this way, softwar agnts can automatically transform quris on DCMES data lmnts to quris on LOM data lmnts, or transform LOM instancs to DCMES instancs, tc. Evn though this approach is quit promising, it dos sm to b in its vry arly stags. For instanc, th dsign principls for mtadata rgistris ar not wll undrstood, and it sms likly that a divrs st of rgistry srvics for mtadata procssing will volv [10]. Also, rlativly fw oprational mtadata rgistris hav bn dvlopd, and most of thm ar usd by softwar agnts that hav bn dvlopd for that on rgistry only. Som arly standardization work has takn plac, most notably in th contxt of ISO 11179, which dfins data lmnts that dscrib mtadata schmas and thir data lmnts. Howvr, at th momnt, thr is for instanc no standardizd way to intract with rgistris, or to find out what thir scop is, tc. Mtadata rgistris could nabl introprability btwn mtadata standards, so that softwar agnts can transform btwn LOM, MPEG-7, DCMES, and othr mtadata standards; application profils, that can includ data lmnts from svral data lmnt sts (th EUN application profil includs DCMES and LOM data lmnts, as wll as som that hav bn dfind by th EUN itslf). In fact, th vry notion of rgistris can b xtndd furthr, to nabl not only mappings and crosswalks btwn data lmnts, but also btwn th valus that ar appropriat for ths data lmnts. This can b thought of as a vocabulary rgistry. Early work in this ara is currntly taking plac in th CEN/CENELEC ISSS Workshop on Larning Tchnologis [3]. Th CEN data rgistry nabls on to discovr for instanc what kind of valu spacs ar bing usd for particular data lmnts, and by whom. A typical xampl qury will rval that communitis us th Library of Congrss Subjct Hading, th Arts and Architctur Thsaurus, th Mdical Subjct Hadings, tc. for th Subjct lmnt in DCMES or for th classification catgory in LOM. Th main diffrnc btwn this kind of rgistry and th mor convntional on is that th numbr of valus for a data lmnt can b substantially largr than th numbr of data lmnts in a schma. Also, th intrrlationships btwn valus can b quit complx, in taxonomis, classification structurs or ontologis. 7. TOOLS AND BUSINESS MODELS 7.1 Rsarch Issu 15 - Tools It may sm odd at first sight to includ tools dvlopmnt as a rsarch issu, but it is important to mphasiz that many of th standards and spcifications undr dvlopmnt ar not mant as such for nd usrs, but rathr to nabl tool dvloprs to dsign and implmnt tools that provid advancd larning functionality to th nd usrs. This mans that w nd to dvlop ths tools in ordr to validat th standards against implmntations, and, mor importantly, against usr rquirmnts in ralistic xprimnts. Th problm is that th usability of th fw tools that hav bn dvlopd is oftn rathr poor... In many currnt tools, th LOM standard shins through th usr intrfac: this is crtainly a sign of th rlativly immatur status of ths tools - not so much in trms of functionality and stability, but much mor in trms of usability and ffctivnss. An obvious xampl is th classification catgory in LOM: it nabls th inclusion of taxonomic stairways of arbitrary classification structurs in a LOM instanc, with a rfrnc to th sourc of th classification, and an xplicit indication of th purpos of this classification. This is an xtrmly vrsatil and flxibl fatur, but not on that should b shown to nd usrs as such: rathr, w bliv that th tool dvloprs should dcid upon rlvant classiciations for thir community, and should build into th tool facilitis for th actual slction of th taxonomic stairways in th classification structurs - a fatur hard nough to implmnt in a usabl way as such! A numbr of rlatd application domains with a longr R&D history may provid guidanc on how to dvlop appropriat tools: th xampl of rus in softwar nginring was alrady mntiond bfor. Som of th mor profssions Intgratd Dvlopmnt Environmnts includ facilitis for rus of objct-orintd classs. Anothr application domain that can provid inspiration is that of tchnical documntation, whr rducd product cycls and incrasd prsonalization of products hav triggrd th adoption of novl approachs basd on structurd data and Contnt Managmnt Systms. 7.2 Rsarch Issu 16 - Businss Modls It is clar that LO production and (r-)us will only bcom widsprad onc businss modls of som kind will b dvlopd. It sms lik thr will b room for a wid varity of such modls, ranging from shar and rus for fr approachs, as pionrd by ARIADNE and also advocatd by th MIT Opn CoursWar initiativ, to mor commrcially orintd approachs. In th acadmic contxt, a rlatd qustion is that of apprciation of larning objct authoring and (r-)us. On suggstion is to trat authoring of larning objcts in a way that is similar to that of paprs in journals and confrncs. Quality could b takn into account by trating r-us of larning objcts in a way that is similar to th valuation of citation of scholarly work - similar warnings about inappropriat us of bibliomtrical data would also apply. Anothr rlatd qustion is that of th rlationship btwn th LO approach and Knowldg Managmnt in gnral, th mor so as th boundary btwn larning objcts and information objcts in gnral is quit fuzzy anyway in a world whr justin-tim larning dals with small granularitis that ar anchord in th day-to-day contxt of th larnr. Or, put anothr way, what kind of functionality dos a Larning Contnt Managmnt Systm (LCMS) nd to provid that a CMS in gnral dos not? 8. CONCLUSIONS Th intnt of this papr was to formulat a numbr of rsarch issus that th authors considr important to furthr th LO approach

9 to larning. W hop that this papr may b instrumntal in raising th ffctivnss and fficincy of rsarch in this ara, by making th diffrnt options to focus on mor apparnt, or, at last, by triggring a dbat on what th most rlvant rsarch issus for th fild ar at this momnt. 9. ACKNOWLEDGMENTS W ar dply indbtd to th many wondrful xprincs and dp insights that w gaind from working with many communitis, including th ARIADNE Foundation and th IEEE LTSC LOM Working Group. 10. REFERENCES [1] Ariadn. [2] Cancor. [3] Cn/cnlc isss larning tchnologis workshop. Dfault.htm. [4] Educational objct conomy. [5] IMS. [6] Scorm. [7] Singcor. MtaData/SingCORE/indx.jsp. [8] Synchronizd multimdia intgration languag (SMIL 2.0), Aug W3C Rcommndation, [9] IEEE Standard for Larning Objct Mtadata. Jun [10] Principls of mtadata rgistris [11] P. D. Bra, P. Brusilovsky, and R. Conjo, ditors. AH2002, Adaptiv Hyprmdia and Adaptiv Wb-Basd Systms, Scond Intrnational Confrnc, May [12] S. K. Card, J. D. Mackinlay, and B. Shnidrman. Radings in Information Visualization: Using Vision to Think. Jan books/radings-info-vis.shtml. [13] A. V. Dam. Nxt-gnration ducational softwar. In EdMdia2002: World Confrnc on Educational Multimdia, Hyprmdia & Tlcommunications, Jun graphics/edmdiakynot_fils/fram.ht%m. [14] E. Duval, E. Fort, K. Cardinals, B. Vrhovn, R. V. Durm, K. Hndrikx, M. W. Fort, N. Ebl, M. Macowicz, K. Warkntyn, and F. Hanni. Th ariadn knowldg pool systm. Communications of th ACM, 44(5):72 78, [15] E. Fort, M. Wntland-Fort, and E. Duval. Th ariadn projct (part 1): Knowldg pools for computr-basd and tlmatics-supportd classical, opn, and distanc ducation. Europan Journal of Enginring Education, 22(1):61 74, [16] F. G. Halasz. Rflctions on Notcards: Svn issus for th nxt gnration of hyprmdia systms. Communications of th ACM, 31(7): , July [17] R. E. Horn. Structurd writing as a paradigm. In A. Romiszowski and C. Dills, ditors, Instructional Dvlopmnt: Stat of th Art. Englwood Cliffs, N. J., rhorn/ HornStWrAsParadigm.html. [18] R. Kopr. Modling units of study from a pdagogical prspctiv - th pdagogical mta-modl bhind EML, Jun pd-mtamodl.pdf. [19] P. Maddocks. Cas study: Cisco systms vnturs into th land of rusability. Larning Circuits, March mar2002/maddocks.html. [20] F. Nvn and E. Duval. Rusabl larning objcts: a survy of lom-basd rpositoris. In Procdings of ACM Multimdia. ACM, Dc Accptd [21] J. Roschll, D. DiGiano, M. Koutlis, A. Rpnning, J. Phillips, N. Jackiw, and D. Suthrs. Dvloping ducational softwar componnts. IEEE Computr, 32(9):50 58, Sptmbr [22] G. Rust and M. Bid. Th indcs mtadata framwork: Principls, modl and data dictionary, Jun [23] S. Trnir, E. Duval, and P. Vandpitt. Lomstr: Pr-to-pr larning objct mtadata. In P. Barkr and S. Rblsky, ditors, Procdings of Ed-Mdia: World Confrnc on Educational Multimdia, Hyprmdia & Tlcommunications, pags AACE, AACE, Jun [24] J. van Ossnbruggn, L. Hardman, and L. Rutldg. Hyprmdia and th smantic wb: A rsarch agnda. Journal of Digital Information, 3(1), [25] H. M. Walkr, W. Ma, and D. Mboya. Variability of rfrs ratings of confrnc paprs. In Procdings of th 7th annual confrnc on Innovation and tchnology in computr scinc ducation, pags ACM Prss, [26] N. Walsh and L. Mullnr. DocBook: Th Dfinitiv Guid. Oct [27] S. H.. M. Wntland. Ophlia, objct-orintd pdagogical hyprtxt ditor for larning, instruction and authoring. In Procdings of Hyprmdia t Apprntissag, oct [28] B. York, A. van Dam, J. Ullman, E. Soloway, J. Pollack, A. Kay, and T. Kalil. A tachr for vry larnr. Jun challngs/slids/ducation.pdf. [29] G. Zacharia, A. Moukas, and P. Mas. Collaborativ rputation mchanisms in lctronic marktplacs. In HICSS, 1999.