Options for evaluating context-based adaptations of an adaptive collaboration environment 1

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1 Optins fr evaluating cntext-based adaptatins f an adaptive cllabratin envirnment 1 Jerg M. Haake Fakultät für Mathematik und Infrmatik FernUniversität in Hagen Hagen Germany jerg.haake@fernuni-hagen.de 1. Intrductin In mdern rganizatins wrk is t a large extent cllabrative. Especially knwledge wrk is increasingly perfrmed by distributed teams cperating acrss large, ften glbal distances. While cllabratin has becme ubiquitus, several challenges need t be addressed by cperatin supprt systems fr supprting it effectively. These challenges arise frm a variety f features that are characteristic fr cllabratin. Amng ther aspects, cllabrative tasks are ften ill-structured, emerge in the curse f the cllabrative prcess, and need t respnd flexibly t changing gals r situatins. Users participating in a cllabrative prject may find themselves in different physical envirnments r settings and may use a variety f different devices. Als, users are ften invlved in mre than ne prject at a time, raising the need fr frequent task r tl switches and fr rapid cgnitive adjustments t the wrk at hand. One apprach t deal with the abve prblem is cntext-based adaptatin f the cllabratin envirnment (Haake et al., 2010; Veiel, Haake & Luksch, 2009). Here, a cntext mdel capturing the current state f the cllabratin is maintained and used t select and execute adaptatin rules apprpriate t imprve cllabratin in the current situatin. Current appraches t evaluatin f cllabrative systems are mstly based n empirical studies (e.g. experimental studies, field studies, ethngraphic studies) and using qualitative data. Such appraches have sme prblems in this case: Firstly, these methds cannt be applied at runtime t assess the quality f adaptatin, identify deficiencies and vercme thse by imprving deficient adaptatin rules. Secndly, these methds require extensive human wrk and cannt easily be autmated. Thirdly, it is an pen issue which measures can r shuld be used t assess the quality f adaptatins. In this paper, several ptins fr evaluating cntext-based adaptatins are discussed. 1 This paper was presented at the Wrkshp Evaluatin Adaptiver Systeme held at the Mensch & Cmputer Cnference 2010, Universität Duisburg-Essen, Duisburg, September 12-15, 2010.

2 2. Objects f evaluatin A cntext-based adaptive cllabratin envirnment (Haake et al., 2010; Veiel, Haake & Luksch, 2009; Hussain & Haake, 2010) cnsists f the fllwing cmpnents: Prcess: defines hw members f a grup/rganizatin cllabratively deal with understanding adaptatins, defining adaptatins (i.e. define rules based n change requests, anntatins), mdificatin f adaptatins (i.e. imprve the rule base based n change requests, anntatins), and handling exceptins (i.e. und-red) (cf. Hussain & Haake, 2010); Runtime adaptatin envirnment: supprts bth, the executin f the adaptatin cycle (mnitr state evaluate rules apply rules), and the executin f the abve prcess mdel; Adaptable applicatins: prvide an adaptatin and state sensing interface t the runtime adaptatin envirnment s that cntext can be cnstructed and maintained, and adaptatins can be perfrmed; Rule base: cntains the current set f adaptatin rules; and Stakehlders: End-users, Develpers (e.g., rule designers). Team members Develpers Cmputer-mediated interactin Task f the team Runtime adaptatin envirnment Prcess Adaptable aplicatins Rule Base Figure 1: Adaptive cllabratin envirnment The aim f a cntext-based adaptive system is ultimately t supprt better wrk f its users (i.e. better task perfrmance). It reaches this gal by prviding better interactin means/affrdances matching the needs f the cllabratin situatin at hand (i.e. adaptatin remves bstacles r prvides affrdances); cllabratin plicies, encded in adaptatin rules, express the rganizatins agreement hw better interactin means/affrdances matching the needs f the cllabratin situatin at hand can be achieved; applying suitable cllabratin plicies/adaptatin rules better interactin is facilitated r enabled as bth (1) cllabratrs fllwing such behavir meet their clleagues

3 expectatin and supprt mre fluid cllabratin, and (2) the system prviding matching supprt and interactin capabilities fr such behavir and interactin; leading t better supprt fr the wrk/tasks f its users. As a means fr assessing whether an adaptive system helps t reduce the gap between situatinal needs and system behavir, ne may use the cncept f expectatin cnfrmance. Expectatin cnfrmance measures t what degree users understand and anticipate system behavir. A high degree f expectatin cnfrmance means that the mental mdel f the users match actual system behavir. A lw degree means that users expect the system t wrk differently, thus leading t surprises, anger, and subptimal perfrmance. One may imprve this situatin either by crrecting the user s mental mdel, r by adjusting system behavir. In a cllabrative system, where users cllabrate via cmputer-mediated cmmunicatin, users are cnfrnted with system behavir caused either entirely by the system s prgramming r by ther users activities. Thus, a mismatch indicated by lw expectatin cnfrmance may als be due t unexpected cllabratr activities indicating a scial cnflict r insufficient applicatin f the team s cllabratin plicies. Due t the nature f CMC such alternatives are difficult t discriminate. We may thus assume that any activity indicating lw expectatin cnfrmity r a mismatch is caused by either system deficits r insufficient plicies r inapprpriate adherence t plicies, all t be addressed by the team. Accrding t Engelbart (1992) we can categrize activities f users as A-level activities: activities that are cncerned with reaching the tasks r gals f an rganizatin; B-level activities: activities that are cncerned with imprving the way f reaching the tasks r gals f an rganizatin with A-level activities. These activities describe hw wrking in the rganizatin is imprved; and C-level activities: activities that are cncerned with imprving the way f imprvement with B-level activities (i.e. imprving the way t imprve the way wrk is perfrmed). Fr assessing cntext-based adaptive systems, a distinctin between A-level und B-level seems sufficient. On the ne hand, we cnsider peratins n artifacts related t the task dmain f the users as A-level activities. E.g., sftware engineers create, mdify, delete, discuss, test etc. sftware artifacts (e.g. classes, methds) and the respective bjects and peratins f the cntext-adaptive system are cnsidered A-level bjects and peratins. On the ther hand, we cnsider peratins n artifacts related t cntext, adaptatin rules, and their applicatin as B- level activities, as they aim at imprving the cllabratin between the users. E.g., sftware engineers may und adaptatins, red them, create change requests fr adaptatin behavir and may be invlved in rule discussin and revisin, and the respective bjects and peratins f the cntext-adaptive system are cnsidered B-level bjects and peratins. It is these B-level activities that can serve as indicatrs fr mismatch respectively lw expectatin cnfrmity.

4 3. Alternative appraches fr evaluating adaptatins Fllwing the abve discussin, successful adaptatins can, in principle, be measured by assessing the quality f the interactin respectively wrk perfrmance befre and after an adaptatin; r assessing the quality f adaptatins by user cgnitive mdels; r analyzing the ccurrence f B-level activities; r assessing the acceptance f an adaptatin in its user cmmunity (assuming that beneficial adaptatins are accepted while adaptatins with negative cnsequences are rejected); r assessing the quality f the prcess used t deal with adaptatins and adaptatin imprvement. 3.1 Assessing the quality f the interactin befre and after an adaptatin The first alternative prvides an bjective measure fr imprvement. Imprvement can stem frm better interactin leading t better prduct quality r decreased effrt, thus increasing efficiency. Unfrtunately, assessing the quality f interactin requires a nrmative interactin mdel representing gd r ptimal interactin against which a real interactin lg can be cmpared. A few examples shw that there seem t be n glbal indicatrs r prcess mdels prviding gd r ptimal interactin in all cases. E.g., while in requirements engineering a lw number f cnflicts may be seen as negative (indicatin fr a dead cmmunity), in cnsensus-based decisin making a lw number f cnflicts may be seen as psitive. 3.2 Assessing the quality f adaptatins by user cgnitive mdels Gals, peratrs, methds and selectin rules (GOAMS) is a methd t predict human behavir using the Human Prcessr Mdel (Card, Mran & Newell, 1983). López-Jaquer et al. prpsed an apprach t assess the user interface adaptatins fr single user interactive systems (López-Jaquer, Mnter & Gnzález, 2009) using migratin cst and adaptatin benefit. Migratin cst is defined by the wrk discntinuity and cgnitive lad t the user caused by the adaptatin. Adaptatin benefit is defined in terms f match t the user preferences, frequency f adaptatin and user feedback f the adaptatin. In a cntext-based adaptive cllabratin envirnment, a similar GOAMS analysis can be dne cnsidering the team characteristics (e.g., cumulative cgnitive lad, match t team preferences etc.) alng with afrementined user characteristics. 3.2 Analyzing the ccurrence f B-level activities B-level activities in an adaptive cllabratin envirnment are cncerned with imprving the way f perfrming wrk in terms f sequences f A-level activities. In an adaptive envirnment, such imprvements stem either frm adaptatins (i.e. the system imprves the way wrk is perfrmed by applying changes encded in adaptatin rules) r frm imprving adaptatins (i.e. users imprving adaptatin rules r unding negative adaptatins). Thus, the extent f B-level activities can be seen as an indicatin f hw gd the adaptatins match the expectatins f the users, and hw much ptential fr imprvement is presently indicated by the users feeling that their B-level activities are needed.

5 Since every peratin takes effrt, and users have nly limited resurces in terms f time and attentin, every B-level activity they perfrm reduces the extent t which they can perfrm A- level peratins needed t achieve their rganizatinal tasks and gals. Thus, we have a zer sum situatin, where A- and B-level activities cmpete. While A-level activities can be seen as prductive (in the sense f cntributing t perfrm the dmain wrk), B-level activities cntribute t imprving cllabratin and thus may imprve prductivity. Thus, ne cannt simply say that an rganizatin shuld ptimally spend 100% n A-level activities t be ptimally prductive (since it may miss imprvement pprtunities). Likewise, ne cannt simply say that an rganizatin shuld ptimally spend 100% n B-level activities t be ptimally prductive (since thugh it may imprve their way f wrk they wuld, in the meantime, get n dmain task dne). In reality, ne wuld like t see needed B-level activities t be perfrmed while investing the majrity f wrk in A-level activities. + = #A-Ops(user) * = #B-Ops(user) * + * * * * * * + * * Figure 2: Sample plt f A- and B-level activities f a user ver a time interval time Figure 2 shws an example f a pssible distributin f activities f a user ver a time interval. B-level activities include und, red, change requests n rules, and participatin in rule discussin and revisin. In the example shwn in figure 2, the number f A-level activities is in each perid higher than the number f B-level activities. In fact, nt in every perid did the user perfrm any B-level activities at all. Aggregating such plts fr all users leads t a cumulative graph shwing the abslute relatinship between A-level and B-level activities. Phases with high number f B-level activities indicate that here users wrk n imprvement f adaptatin rules while phases with A-level activities nly indicate phases f sle prductin wrk (withut disturbances, e.g. caused by the adaptive system r by external events). By adapting artifacts, such as adaptatin rules and tl cnfiguratins, we aim at changing the practice f teams thrugh the change f affrdances resulting frm changing tl cnfiguratin and thrugh negtiatin respectively jint cnstructin f rules encding team plicies. The cgnitive hysterese mdel f DeLne & McLean (1992) describes the uptake f new practices by users as requiring a high gain befre users switch t new, prfitable practices (and staying with them even when switching t the lder way wuld be better). We argue that autmatic adaptatin may help team t switch faster, as adaptatin is perfrmed withut user initiative, while still leaving users in cntrl f defining and imprving their plicies.

6 This verall view n A- and B-level activities ignres relatinships between B-level peratins. Semantically, B-level activities cnfrm t the state transitin diagram shwn in Figure 3. Using these dependencies, episdes f B-level activities belnging t the same adaptatin respectively adaptatin rule executin can be defined n the activity lg. Whenever an adaptatin activity is fund in the lg, ne can search fr subsequent activities cnfrming t the state transitins defined in figure 3. adapt start state adapted state change request und change request exists pen revisin timeut under revisin clse revisin red exceptin state timeut timeut state rule feedback Figure 3: State transitins due t adaptatins and fllwing B-level activities fllwing the prcess defined in (Hussain & Haake, 2010) This bservatin leads us t the definitin f different types f B-level episdes: pen adaptatin episdes: indicated by a sequence f start state adapted state (adapt peratin sequence, respectively). These indicate situatins, where an adaptatin was perfrmed and nbdy (yet) bjected. clsed adaptatin episdes: cnfrm t pen adaptatin episdes where nbdy bjected during the timeut interval. Thrugh the timeut, the timeut state was reached and, pssibly, psitive rule feedback was added t the rule befre reaching the end state cncluding this episde. pen exceptin episdes: indicated by a sequence f start state adapted state exceptin state (adapt und peratin sequence, respectively). These indicate situatins, where an adaptatin was undne and nbdy (yet) bjected. clsed exceptin episdes: cnfrm t pen exceptin episdes where nbdy bjects during the timeut interval. Thrugh the timeut, the timeut state was reached and, pssibly, rule feedback was added t the rule befre reaching the end state cncluding this episde. pen change request exist episde: sme change request n this rule applicatin exist (i.e., the state sequence ends with change request exists state), but rule revisin did nt yet start. When the revisin prcess starts, this episde becmes an pen under revisin episde (see belw).

7 pen under revisin episde: rule revisin started (i.e., the state sequence ends with under revisin state), but rule revisin did nt yet end. Mre peratins are t fllw in this episde in this future. clsed under revisin episde: rule revisin was clsed befre reaching the end state cncluding this episde. These types f episdes may help us in limiting the number f activities t be analyzed in the activity lg and in recgnizing psitive r negative situatins: Clsed episdes are, by definitin, nt subject t further activities. Thus, the episde recgnitin cmpnent can safely ignre the activities already assigned t clsed episdes, and des nt need t examine clsed episdes as candidates fr assigning further activities t them. The reasn f using a timeut mechanism is exactly t limit the number f pen episdes, where users d nt perfrm further activities. Thus, the number f pen episdes and unassigned activities is rather limited. Prductive use f adaptatin may be characterized by having nly a limited number f pen episdes, clsing episdes relatively fast (limiting time devted t B-Level episdes) and having large numbers f psitively evaluated adaptatins (represented by pen and clsed adaptatin episdes). Unprductive use f adaptatin may be characterized by having large numbers f pen episdes, especially pen change request exist episdes and pen under revisin episdes, as here users explicitly asked fr change and need t agree n future plicy. Large numbers f such episdes indicate that adaptatin rules/plicies are deficient, the users have nt yet reached a cmmn understanding and agreement n what such plicies shuld lk like, r that the envirnment is changing and mdificatin f adaptatin rules/plicies is needed. Alternatively, the revisin prcess r resurces dedicated t its executin may be insufficient. In terms f a learning curve, ne wuld expect the fllwing three phases t appear: 1. Initially, r after new team members jin, r after sme change happened, rules are mre ften regarded as nt meeting the needs f the users. Thus, they are mre ften undne, anntated with feedback, and changed. Cnsequently, mre pen episdes exist, especially pen exceptin episdes, pen change request exist episdes and pen under revisin episdes. Due t the new users r system, users need mre time in learning and applying system features, leading t lnger episdes and pssibly lnger und wars (tggling between adapted state and exceptin state). 2. After a while, imprved r added rules shuld lead t higher acceptance. Still, exceptins lead t sme und, but the number f changes and negative feedback decreases. Hwever, sme negative feedback indicating further imprvements remain. This leads t less pen episdes, episdes becme in average shrter, and fewer pen change request exist episdes and pen under revisin episdes are created. 3. Eventually, the team/grup has agreed n prven plicies and perfrms wrk accrdingly. Few und peratins pint t rare exceptins that are nt wrthwhile t address in changes. The team wrks smthly with a suitable system. Here, we see much less exceptin, change request and revisin episdes, and such episdes shuld becme shrter in average (cmpared t the previus phases).

8 One prblem f any ptimizatin is whether r hw lcal ptima are vercme? In ur apprach, ne culd inject plicies frm ther teams at apprpriate times (e.g., when wrk runs t smthly?). Alternatively, ne culd argue that the envirnment is cnstantly changing, and thus sufficient pressure fr cntinuing imprvement is pertained t the system. Finally, ne may als argue that change induces cst, and thus smetimes a lcal ptimum may be advantageus cmpared t the cst-benefit rati f further change. 3.3 Assessing the acceptance f an adaptatin in its user cmmunity This alternative examines neither utput nr B-level sequences, but nly interprets certain cmmunicatin acts as indicatrs fr acceptance r rejectin f adaptatins. Here, the fllwing indicatrs seem useful t indicate acceptance f an adaptatin: individual level acceptance qutient accept(user, rule, t1, t2): number f und +1 divided by number f executins +1 per rule in a given time interval accept(user, rule, t1, t2) > 0 means that the user rejected the majrity f the adaptatin ccurring in the time interval feedback qutient feedback(user, rule, t1, t2): number f negative feedback +1 divided by number f psitive feedback +1 per rule in a given time interval feedback(user, rule, t1, t2) > 0 means that the user rated the majrity f the adaptatin ccurring in the time interval psitively change functin delta_change(user, rule, t1, t2): the functin shwing the cumulative number f actins in a revisin cycle fllwing an und f the rule during the time interval delta_change(user, rule, t1, t2) = 0 means that n revisin cycle is perfrmed (either indicating that it is nt yet started r that it is nt needed) delta_change(user, rule, t1, t2) > 0 means that a revisin cycle is perfrmed exceptin functin delta_exceptin(user, rule, t1, t2): the functin shwing the number f und f this rule during the time interval, which are nt (yet) fllwed by a revisin cycle user feedback user_feedback(user, rule, t1, t2): the set f feedback frms n rule prvided by the user during the time interval. size(user_feedback(user, rule, t1, t2)) > 0 means that sme feedback frm was filled by the user n this rule during the given time interval team r grup level acceptance qutient accept(team/grup, rule, t1, t2): number f und +1 divided by number f executins +1 per rule in a given time interval. accept(team/grup, rule, t1, t2) > 0 means that the users in the team/grup rejected the majrity f the adaptatin ccurring in the time interval feedback qutient feedback(team/grup, rule, t1, t2): number f negative feedback +1 divided by number f psitive feedback +1 per rule in a given time interval. feedback(team/grup, rule, t1, t2) > 0 means that the users in the team/grup rated the majrity f the adaptatin ccurring in the time interval psitively

9 change functin delta_change(team/grup, rule, t1, t2): the functin shwing the cumulative number f actins in a revisin cycle fllwing an und f the rule during the time interval delta_change(team/grup, rule, t1, t2) = 0 means that n revisin cycle is perfrmed (either indicating that it is nt yet started r that it is nt needed). delta_change(team/grup, rule, t1, t2) > 0 means that a revisin cycle is perfrmed exceptin functin delta_exceptin(team/grup, rule, t1, t2): the functin shwing the number f und f this rule during the time interval, which are nt (yet) fllwed by a revisin cycle user feedback user_feedback(team/grup, rule, t1, t2): the set f feedback frms n rule prvided by the users frm the team/grup during the time interval. size(user_feedback(team/grup, rule, t1, t2)) > 0 means that sme feedback frm was filled n this rule during the given time interval sessin level the abve indicatrs defined fr sessins pen issue: definitin f sessin ne tl with its users beginning when 2 nd user jins and ending when 2 nd last user leaves? Culd be defined within an adaptatin rule? Culd include a start new sessin tag, but hw t detect the end? Which tls and their users wuld be included? In terms f a learning curve, ne wuld expect the fllwing three phases t appear: 1. Initial: Initially, r after new team members jined, r after sme change happened, rules are mre ften regarded as nt meeting the needs f the users. Thus, they are mre ften undne, anntated with feedback, and changed. 2. Intermediate: After a while, imprved r added rules shuld lead t higher acceptance. Still, exceptins lead t sme und, but the number f changes and negative feedback decreases. Hwever, sme negative feedback indicating further imprvements remain. 3. Flw: Eventually, the team/grup has agreed n prven plicies and perfrms wrk accrdingly. Few und peratins pint t rare exceptins that are nt wrthwhile t address in changes. The team wrks smthly with a suitable system. Nte, that after external change requiring change f cllabratin plicies, we wuld expect the team/grup t start with anther cycle n a lwer level (suggesting that nt all prven plicies need t be changed, but sme change may be required). Success in terms f acceptance culd then be indicated by reaching indicatr values apprpriate t the phase f the team/grup. Identificatin f such threshlds culd be perfrmed n the basis f interactin analysis f real cllabratin cases.

10 Phase Individual Team/grup initial lwer acceptance qutient lwer feedback qutient increasing change_functin (as users learn hw t use it) users make increasingly use f exceptins users make increasingly use f feedback frms frms intermediate flw increasing acceptance qutient increasing feedback qutient mderately decreasing change_functin slwly decreasing number f exceptins negative feedback frms decrease psitive acceptance qutient psitive feedback qutient lw change_functin lw number f exceptins lw number f negative feedback frms lwer acceptance qutient lwer feedback qutient increasing change_functin high number f exceptins increasing number f feedback increasing acceptance qutient increasing feedback qutient mderately decreasing change_functin slwly decreasing number f exceptins negative feedback frms decrease psitive acceptance qutient psitive feedback qutient lw change_functin lw number f exceptins lw number f negative feedback frms Nte, that after external change requiring change f cllabratin plicies, we wuld expect the team/grup t start with anther cycle n a lwer level (suggesting that nt all prven plicies need t be changed, but sme change may be required). Success in terms f acceptance culd then be indicated by reaching indicatr values apprpriate t the phase f the team/grup. Identificatin f such threshlds culd be perfrmed n the basis f interactin analysis f real cllabratin cases. 3.4 Evaluatin f the prcess The prcess mdel used t deal with adaptatin and imprving them culd be validated by perfrming walk-thrugh with test users. During such a walk-thrugh, users are led thrugh a predefined scenari illustrating the prpsed prcess. By semi-structured interviews user feedback and assessment n the prpsed prcess is elicited. If a mck-up is used in this prcess, sme feedback n usability issues can be cllected, t. Nte, this is a validatin based n user assessment, nt based n shwing that the prpsed prcess in reality leads t better utcme r efficiency. If bjective measures are t be used, a cntrlled experimental prcedure culd be used, where an experimental cnditin is cmpared t a cntrl cnditin. Hwever, while such prcedures have higher internal validity they suffer frm ptentially lwer external validity (i.e., ne cannt be sure that the effect is als shwing in the field, where ther factrs neglected in the lab study may turn ut t verpwer the lab effects).

11 4. Evaluatin f the runtime adaptatin envirnment The success f adaptatins is als subject t influences frm the runtime adaptatin envirnment used t perfrm adaptatins. The runtime adaptatin envirnment can be evaluated by functinal tests shwing that the system can actually supprt the use cases r scenaris used in the evaluatin f the prcess mdel; usability tests shwing that the system is usable; and field studies with acceptance tests (see abve) where interactin lgs f real usage are cllected and analyzed regarding the abve interactin measures. If the hyptheses are backed by the data, ne culd draw cnclusins regarding the learning curve and phase mdel. subjective feedback is cllected indicating acceptance. 5. Cnclusins In this paper, the issue f hw t evaluate cntext-based adaptatins in cllabratin envirnments was discussed. An analysis f adaptive cllabratin envirnments led t fur appraches: assessing the quality f the interactin respectively wrk perfrmance befre and after an adaptatin based n glbal perfrmance criteria fr grup wrk r cllabratin behavir was deemed t be inapplicable; analyzing the ccurrence and episdes f B-level activities based n a prcess specifying hw adaptatins are applied and imprved led t the definitin f three phases during adaptatin f cllabratin envirnments fr teams, and the definitin f crrespnding predicates useful t identify prblem areas; assessing the acceptance f an adaptatin in its user cmmunity (assuming that beneficial adaptatins are accepted while adaptatins with negative cnsequences are rejected) based n indicatrs fr individual and grup acceptance f an adaptatin. It is suggested that such indicatrs can be used t identify the state f cllabratin (initial, intermediate, flw) and t help identify cases where mre adaptatin is needed; assessing the quality f the prcess used t deal with adaptatins and adaptatin imprvement based n the walk-thugh methd r using ther types f empirical studies were discussed. In additin, the evaluatin f the cre cmpnent f a cntext-adaptive system was discussed: the runtime adaptatin envirnment culd be evaluated using functinal tests, usability tests, and field studies. While the current state f this discussin is at the cnceptual level, the next steps include the specificatin f cncrete measures and threshlds based n case data, and subsequently implementatin f the evaluatin methds and its testing.

12 6. Acknwledgements Thanks are due t the members f the Cntici prject at FernUniversität in Hagen, Dirk Veiel, Sajid Hussain and Stephan Luksch, fr many inspiring discussins. The Cntici prject was funded by the DFG under grant number HA 3130/ References DeLne, W.H., McLean, E.R.: Infrmatin systems success: the quest fr the dependent variable. Infrmatin Systems Research, 3, 1, (1992), Engelbart, D. C.: Tward high-perfrmance rganizatins: A strategic rle fr grupware. In Prceedings f the Grup-Ware '92 Cnference. Mrgan Kaufmann, San Mate, Calif., Haake, J. M., Hussein, T., Jp, B., Luksch, S., Veiel, D., Ziegler, J.: Mdeling and Expliting Cntext fr Adaptive Cllabratin. Internatinal Jurnal f Cperative Infrmatin Systems 19(1&2), pp , Hussain, S. S., Haake, J. M.: Prcess Supprt fr Cntext-based Adaptatins in a Cllabratin Envirnment. Internatinal Sympsium n Cllabrative Technlgies and Systems, 2010, pp Veiel, D., Haake, J. M., Luksch, S.: Extending a shared wrkspace envirnment with cntextbased adaptatins. Grupware: Design, Implementatin, and Use: 15th Internatinal Wrkshp, CRIWG 2009, LNCS, Springer: Heidelberg, 2009, pp Card S., Mran T.P., Newell A.: The Psychlgy f Human Cmputer Interactin. Lawrence Erlbaum Assciates 1983, ISBN López-Jaquer V., Mnter F., Gnzález P.: In Cmputer-Aided Design f User Interfaces VI. Springer Lndn, 2009, pp

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