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1 Orgnal ctaton: Crstea, Alexandra I. and De Moo, A. (2003) Desgner adaptaton n adaptve hypermeda authorng. In: Internatonal Conference on Informaton Technology : Codng and Computng (ITCC 2003), Las Vegas, US, Apr Publshed n: Internatonal Conference on Informaton Technology: Codng and Computng [Computers and Communcatons], Proceedngs. ITCC pp Permanent WRAP url: Copyrght and reuse: The Warwck Research Archve Portal (WRAP) makes ths work by researchers of the Unversty of Warwck avalable open access under the followng condtons. Copyrght and all moral rghts to the verson of the paper presented here belong to the ndvdual author(s) and/or other copyrght owners. To the extent reasonable and practcable the materal made avalable n WRAP has been checked for elgblty before beng made avalable. Copes of full tems can be used for personal research or study, educatonal, or not-for proft purposes wthout pror permsson or charge. Provded that the authors, ttle and full bblographc detals are credted, a hyperlnk and/or URL s gven for the orgnal metadata page and the content s not changed n any way. Publsher statement: 2003 IEEE. Personal use of ths materal s permtted. Permsson from IEEE must be obtaned for all other uses, n any current or future meda, ncludng reprntng /republshng ths materal for advertsng or promotonal purposes, creatng new collectve works, for resale or redstrbuton to servers or lsts, or reuse of any copyrghted component of ths work n other works. A note on versons: The verson presented here may dffer from the publshed verson or, verson of record, f you wsh to cte ths tem you are advsed to consult the publsher s verson. Please see the permanent WRAP url above for detals on accessng the publshed verson and note that access may requre a subscrpton. For more nformaton, please contact the WRAP Team at: publcatons@warwck.ac.uk

2 A.I. Crstea, A. de Moo, Desgner Adaptaton n Adaptve Hypermeda Authorng, ITCC 03, Las Vegas, US, IEEE Computer Scence Desgner Adaptaton n Adaptve Hypermeda Authorng Alexandra I. Crstea and Arnout de Moo Faculty of Mathematcs and Computng Scence, Informaton Systems Department Technsche Unverstet Endhoven, P.O. Box 513, 5600 MB Endhoven, The Netherlands a..crstea@tue.nl Abstract Recently, the mportance of creatng authorng support for adaptve hypermeda system desgn offerng multmodalty and personalzaton s becomng evdent [4][5][6][7][1]. In the process of desgnng such authorng support, we dscovered that gven the dffculty of adaptve hypermeda authorng,.e., n desgnng dfferent levels of abstracton, alternatves, multple lnks, etc., t would be benefcal to also attend to the authorng needs and adapt to the author. Therefore, ths paper descrbes for the frst tme an attempt of adaptaton not only to the student, but also to the desgner. 1. Introducton 1.1 Desgn of Adaptve hypermeda A hypermeda system s a database-lke software system, whch can be accessed usng a hypermeda tool, as for nstance the web va a web browser [1]. A hypermeda system s sad to be adaptve f t can automatcally adapt to goals, needs or, e.g., new condtons, whch can be deduced, for example, from actons the system user undertakes. For nstance, n adaptve courseware the adaptaton s reflected n the varous ways and orders n whch the study materal s presented to the dfferent students. Bascally, the more alternatves there are, the hgher the adaptaton degree. Ths flexblty s benefcal as long as t serves a specfc learnng goal, but can also lead to psychologcal pressure and unwanted effects. To fnd the exact balance between flexblty versus stablty and predctablty s extremely mportant and a challengng research n tself, but we are not gong to go nto detals about ths n ths paper From MyEnglshTeacher to MOT To study varous types of adaptaton, as well as methods for ther effcent desgn, we use an adaptve course system desgn tool. The present system extends a prevous tool for constructng adaptve hypermeda systems, My Englsh Teacher (MyET [8][12]) and ts successor My Onlne Teacher (MOT [13]). Although there are many tools for course development, there are only few other examples of tools tryng to create adaptve courseware, as shown n [3]. MyET allowed teachers to create concepts and concept maps to model ther courses. Based on these concept maps the teacher should be able to construct lessons, to be presented to the student n an adaptve way. The part of (ndependent) lesson constructon was not yet avalable n MyET and the way n whch concept maps could be created was too restrcted. Moreover, MyET used fles to store all the nformaton, whle a database s preferred. The goal was to extend MyET nto My Onlne Teacher (MOT v.2), for a more general audence. Ths s why the frst mplementaton testng s done va a Neural Networks course for thrd year students n Computer Scence. In desgn terms, the ssue was to make t possble to construct complete concept maps and lessons, stored n a database, and to demonstrate some smple features of automatcally bndng concepts for adaptaton purposes. 2. Goals 2.1. Intal goals An adaptve (web) hypermeda course s a hypermeda system that can be used by a student to learn about a certan subect va, e.g., a web browser. The basc feature of such a system s that t tres to nterpret the students current knowledge (and often, other student parameters and characterstcs as well) n order to adapt tself to hs learnng needs. Ideally, there s no need for a human teacher. The student can perform actons such as choosng topcs he wants to learn about, askng for more nformaton or solvng exercses. Dependng on the actons the student takes (for example the pages vsted, or the results of exercses) the course transparently adapts to the student s needs. We have delmted the man steps of the creaton of an adaptve lesson desgn system as beng the creaton of: 1. A tool for manpulatng concept maps. 2. A method for calculatng correspondence weghts between concept attrbutes. 3. A tool for constructng lessons based on a concept map.

3 A.I. Crstea, A. de Moo, Desgner Adaptaton n Adaptve Hypermeda Authorng, ITCC 03, Las Vegas, US, IEEE Computer Scence 2.2. Desgner Adaptaton Adaptaton to the teacher s smlar to adaptaton to the student, f we consder that adaptaton occurs wth regard to a goal (be t a learnng, or a desgn goal) or f t comes as a response to a need [9]. However, ths new type of adaptaton s qute dfferent, f we consder the typcal adaptaton to (student) user preferences. Such (desgner) user preferences adaptaton s not taken nto consderaton here. The paper shows the desgn and mplementaton of ths new varant of adaptvty, wthn the larger context of desgnng an authorng support system for adaptve hypermeda. The adaptaton of a course under desgn to the desgn goal can take many forms. Ths frst verson llustrates (sem-) automatc course lnkng. Thereby, we can clam to lay the bass for a course that bulds (or wrtes) tself. 3. Database desgn The database was to be mplemented accordng to the ER-dagram depcted n Fg. 1, representng the ntal statc UML classes, and whch can be dvded nto two parts: the concept doman, formed by the left sde of the dagram, and the course (or lesson herarchy) on the rght sde of the dagram. These two parts are connected by 3.1. Concept doman A concept contans one or more sub-concepts, whch are concepts on ther turn, hence nducng a herarchcal (tree) structure of concepts. Each concept s a set of concept attrbutes. These attrbutes hold peces of nformaton about the concept they belong to. Several knds of attrbutes are possble, correspondng to the dfferent attrbute nstances n the dagram. For example, a concept can have a ttle - attrbute, a descrpton -attrbute or example -attrbute. Concept attrbutes can be related to each other. Such a relaton, characterzed by a label and a weght, ndcates that ther contents treat smlar topcs. Exercses are modeled as specal concepts, because they have ther own herarchcal structure wthn the greater concept structure, whle they actually belong to one (non-exercse) concept Course A lesson contans sub-lessons, whch are lessons on ther turn, hence creatng a herarchcal structure of lessons. Sub-lessons wthn a lesson can be OR-connected (beng lesson alternatves) or AND-connected. To facltate ths, a lesson contans a lesson attrbute (L- Fgure 1. Intal ER-dagram means of the relaton between the C-Attrbute (concept attrbute) and the L-Attrbute (lesson attrbute). Attrbute n the dagram), whch n ts turn contans a holder for OR-connected sub-lessons (L-OR-Conn) or a holder for AND-connected sub-lessons (L-AND-Conn).

4 A.I. Crstea, A. de Moo, Desgner Adaptaton n Adaptve Hypermeda Authorng, ITCC 03, Las Vegas, US, IEEE Computer Scence The holder contans the actual sub-lessons n a specfed order. A lesson attrbute contans, besdes the sub-lesson holders, one or more concept attrbutes. Ths s the lnk wth the concept doman. The dea s that the lesson puts peces of nformaton that are stored n the concept attrbutes together n a sutable way for presentaton to a student. In the database mplementaton phase and even n the later phase of system mplementaton and addng the feature of calculatng relatedness relatons between concept attrbutes some changes were made to the ERdagram, but ths wll not be detaled n the current paper. 4. Calculatng relatedness relatons The assgnment descrbes so called relatedness relatons between concept attrbutes. Concept attrbutes are related when they share a common topc. It turned out to be more logcal to record ths relaton type at concept level, so that a relatedness relatons marks the exstence of a relaton between concepts. If the relatedness s nduced from an attrbute level, we took the desgn decson to keep the name of that attrbute as the semantc label for the respectve relatedness relaton. The system was requred to help the teacher n determnng the relatedness relatons by calculatng correspondence weghts between pars of concepts. There are several ways of computng such lnks, some symbolc, some sub-symbolc. For a smplfed llustraton of (sem- )automatc bndng, we took the desgn decson to base these correspondence weghts on the number of occurrences of the keywords of one concept n the attrbute contents of the other concept The ntal plan for relatedness calculaton We consder the concept map of the courseware to be determned by the tuple <C,L>, wherecrepresents the set of concepts and L the set of lnks, and a concept c C s defned by ts set of attrbutes, A c (where A c A mn ; A mn s the mnmal set of attrbutes requred for each concept to have 1 ). As all the sets above are fnte, they can be gven (relatve) dentfcaton numbers. Therefore, concept c s determned (and therefore can be referred to) by ts dentfcaton {1,,C} (where C=card(C)) and the attrbutes of concept are a [h], wth h {1,,A } and A A mn (where A =card(a c ) and A mn =card(a mn )). Moreover, a specal attrbute of each concept, a [2]={ (k [s]) s= 1,,K }, s the lst of keywords for concept (wth K the number of keywords of attrbute a [2] called 1 by the adaptve course desgn constrans, that am at creatng concepts annotated wth suffcent meta-data keyword of concept ). Ths keyword attrbute s oblgatory, therefore a [2] A mn. Wth the above notatons, we can express the number of occurrences of keyword k [s], wth s {1,,K } of concept {1,,C} n an attrbute a [h], wth h {1,,A } of concept {1,,C} as beng gven by occ (k [s], a [h]). If: maxocc (k [s], a [h])= count(words n a [h]); s the maxmum possble number of occurrences of k [s] n a [h], then occ (k [s], a [h]) /maxocc (k [s], a [h]) [0, 1]. When we add these values over all keywords of and all attrbutes of and dvde the result by the number of keywords of and by the number of attrbutes of we get a value whch s also between 0 and 1, ndcatng the level of correspondence between concept and concept. Therefore, for concepts, {1,,C} we can defne: occ (k [s],a [h]) correspondence_drected(,)= A K h 1 s 1 K * A maxocc (k [s],a [h]) As the name says, ths number only looks at one drecton of the relaton between concepts and. If we consder also the reverse drecton, a better measure for the correspondence between the concepts can also contan a weghted reverse correspondence, as follows: correspondence(,) = *correspondence_drected(,) + + *correspondence_drected(,) wth, [0,1] and + =1 If = =0,5 the followng relaton also holds: correspondence (,) = correspondence (,) To further fne-tune the relatedness calculaton an mportance weght can be assgned to each type of attrbute to be able to gve certan attrbutes (e.g., the ttle, a [1], or the keywords, a [2], attrbute) a stronger nfluence on the correspondence weght. Therefore, f we consder mportance(a [h]) [0,1] to be the value of the mportance (or weght) of attrbute a [h] and f: h=1,,a (mportance(a [h]) = 1; The formula for correspondence_drected becomes: correspondence_drected(,) = mportance(a [h])* occ (k [s],a [h]) A K h 1 s 1 maxocc (k [s],a [h]) K * A A more generalzed formula s: correspondence_drected(,) = mportance(a [h]) * occ A K h 1 s 1 K * A h 1 maxocc (k [s],a [h]) mportance(a [h]) (k [s],a [h])

5 A.I. Crstea, A. de Moo, Desgner Adaptaton n Adaptve Hypermeda Authorng, ITCC 03, Las Vegas, US, IEEE Computer Scence The defnton of correspondence(,) remans unchanged. The system wll then calculate correspondence(,) for each par of concepts and and suggest a relatedness relaton between the two concepts when ths value exceeds a certan threshold. The teacher can choose to add ths relatedness relaton or to gnore t. When the teacher decdes to add a relatedness relaton he can gve t a label and a weght (by default the correspondence weght, correspondence_drected(,), s proposed). s up to the course desgner to ensure the exstence of recognzable types, f desred). 5. Relatedness Computatons Implementaton Fgure 2 shows a screenshot of the lst of possble connectons the system automatcally fnds and ther suggested weghts for concept Theorem of Batch Perceptron Convergence from a Neural Networks course A second plan for relatedness calculaton Relatedness relatons wll have to have a type. Usng the formula as descrbed above, t s mpossble to see n what way two concepts are related, because the correspondence weghts of all keywords and attrbutes are used to get the correspondence weght. A better dea s therefore to calculate correspondence weghts per attrbute as follows. Wth the above notatons, for concepts, and attrbutes a [h] of : correspondence_drected(,, a [h]) = occ K s 1 maxocc (k [s],a [h] K (k [s],a [h]) The attrbute type of attrbute a can be used as a label (or even type) of the relaton, wth the weght of the relaton gven by correspondence_drected(,, a [h]). For example, concepts A and B can have a relatedness relaton of type ttle, wth the weght gven by correspondence_drected(a,b,a B [1]) and also a relatedess relaton of type text, wth the weght gven by correspondence_drected(a, B, text attrbute of B). In ths way, more than one relatedness relaton between two concepts can exst. However these relatons wll have dfferent types, ndcatng n what way the concepts are related. Optonally, a value: Fgure 2. Automatc relatedness relatons The course desgner (teacher) can accept or reect them, as well as change weghts or labels (Fg. 3). Ths screen appears after pressng add n the prevous one. correspondence(,,a [h])= ( correspondence_drected(,,a [h])+ + correspondence_drected(,, a [h ]) ) / 2 can be used for all concepts, and all attrbutes a [h] of and all attrbutes a [h ] of, a [h] and a [h ] havng the same type (.e., h=h ). Please note that concepts may not have attrbutes of the same type (f a [h] A mn, then the exstence of an attrbute of the same type, a [h ] A mn, gven the condton h=h s fulflled, s guaranteed; f a [h] A c A mn, such guarantee does not exst, and t Fgure 3. Addng relatedness relatons 6. Proect evaluaton

6 A.I. Crstea, A. de Moo, Desgner Adaptaton n Adaptve Hypermeda Authorng, ITCC 03, Las Vegas, US, IEEE Computer Scence 6.1 Plannng It turned out that lesson modelng (based on concept maps) was more dffcult then expected. At frst, concept maps and lessons seemed to be much alke, so we expected to use the same database structure for both. However, ths proved to be a bad dea and we had to make some great extensons to the database. Also, t became apparent that not all desred functonalty of the user nterface was stated n the URD. Especally the calculaton of the relatedness relatons turned out to be somewhat more complcated then expected. Even now, ths part s not really fulflled satsfactory. Furthermore, the frst versons of the nterface were not very enoyable to use, so changes had to be made a couple of tmes. For example, HTML-lsts used for dsplayng lessons and concept maps had to be replaced by collapsble lsts wrtten n JavaScrpt, etc. 6.2 Evaluaton of the system The delvered system satsfes n prncple all user requrements and s effcent n t s use. However, some crtcal remarks can be made. It s a mnmal system wth no extra s and some open ends. For example, the nterface s non-graphcal, whle the representaton of concept maps could possbly beneft a lot from usng graphcal elements. Some other features that are lackng are warnngs (e.g. when removng concepts), error messages (when performng llegal actons) and securty ssues (preventng users from vewng or changng other users concept maps or lessons). Fnally, much can be done to mprove the calculaton and typng of relatedness relatons. All these provde some work for the future. 7. Conclusons In ths paper we have presented a system for desgnng adaptve hypermeda, nstantated wthn an educatonal settng. We have brefly shown the desgn and mplementaton steps of ths system. We have argued from the start that the authors of adaptve hypermeda have a consderably dffcult task, compared to authors of regular hypermeda, for example. Based on ths assumpton, we have endeavored to construct a support system that adapts to the desgn goal. Therefore, the focus was on the adaptvty to the desgner. Ths s an extended use of ths term, f compared to the user adaptvty we are used to. Adaptaton to the desgner, the way we see t, can be varous: lnk -, content adaptaton, hnts, dfferent desgn levels, templates suggestng, but also recognton, collaboraton support [6]. However, n ths paper we have only shown a small demonstratve example of automatc lnkng. Moreover, as suggested n the evaluaton secton, there s space for mprovement even n automatc lnkng: nstead of determnstc functons, sub-symbolc clusterng technques can be used [10]. In ths way we have made a step towards hypermeda that bulds (or wrtes) tself. 8. Acknowledgements Ths research s lnked to the European Communty Socrates Mnerva proect "Adaptvty and adaptablty n ODL based on ICT" (proect reference number CP NL-MINERVA-MPP). 9. References [1] ADAPT: [2] 2L690: Hypermeda Structures and Systems, Lecturer: Prof. De Bra, [3] Bruslovsky, P.: Adaptve hypermeda, User Modelng and User Adapted Interacton, Ten Year Annversary Issue (Alfred Kobsa, ed.) 11 (1/2), 2002, [4] Calv, L. and Crstea, A.I.: Towards Generc Adaptve Systems Analyss of a Case Study, AH 2002, Adaptve Hypermeda and Adaptve Web-Based Systems, LNCS 2347, Sprnger, [5] Crstea, A.I. and De Bra, P.: Towards Adaptable and Adaptve ODL Envronments, E-Learn 02, Montreal, Canada, AACE, October 2002, pp [6] Crstea, A.I., Okamoto, T. and Kayama, M.: Consderatons for Buldng a Common Platform for Cooperatve &Collaboratve Authorng Envronments, E-Learn 02, AACE, October 2002, pp [7] Crstea, A.I. and Aroyo, L.: Adaptve Authorng of Adaptve Educatonal Hypermeda, AH 2002, Adaptve Hypermeda and Adaptve Web-Based Systems, LNCS 2347, Sprnger, [8] Crstea, A.I., Okamoto, T. and Belkada, S.: Concept Mappng for Subect Lnkng n a WWW Authorng Tool: MyEnglshTeacher: Teachers' Ste, ANNIE'00, ASME. [9] IMS (Instruct. Manag. System): [10] Kayama, M., Okamoto, T. and Crstea, A.I.: Exploratory Actvty Support Based on a Semantc Feature Map, AH 00, LNCS 1892, Sprnger, [11] Loeber, S. and Crstea, A.I. (2002), A WWW Informaton- Seekng Process Model, ISSEI 2002, CBMO workshop, July, 2002, England. [12] MyET: eachersste/ndex.html [13] MOT: [14] Wu, H., Houben,G.-J., De Bra, P.: AHAM: A Reference Model to Support Adaptve Hypermeda Authorng, Informatewetenschap 1998, Ed. E. De Smet, Antwerp, Belgum, 11 December 1998,