Towards User-Adaptive Information Visualization

Transcription

1 Towards Usr-Adaptiv Information Visualization Cristina Conati, Giuspp Carnini, Drck Tokr, Sbastian Lall Univrsity of British Columbia, Vancouvr, Canada. {conati, carnini, tokr, Abstract This papr summarizs an ongoing multi-yar projct aiming to uncovr knowldg and tchniqus for dvising intllignt nvironmnts for usr-adaptiv visualizations. W ran thr studis dsignd to invstigat th impact of usr and task charactristics on usr prformanc and satisfaction in diffrnt visualization contxts. Ey-tracking data collctd in ach study was analyzd to uncovr possibl intractions btwn usr/task charactristics and gaz bhavior during visualization procssing. Finally, w invstigatd usr modls that can assss usrs charactristics rlvant for adaptation from y tracking data. Introduction Rsarch in Information Visualization (InfoVis) has traditionally followd a on-siz-fits-all approach that dos not account for usr diffrncs. In rcnt yars, howvr, rsarchrs hav startd showing that usr-adaptiv intraction, i.. intraction adaptd by an intllignt intrfac to suit ach usr s spcific nds and abilitis, has th potntial to improv usrs xprinc during visualization procssing (.g. Gotz and Wn 2009, Ahn and Brusilowsky 2013). Still, dspit ths initial rsults, th ffcts of both usr diffrncs and diffrnt forms of adaptation rmain largly unxplord. This papr summarizs th rsults of ATUAV (Advancd Tools for Usr-Adaptiv Visualizations), an ongoing multi-yar projct aiming to uncovr furthr knowldg and tchniqus for dvising usr-adaptiv visualizations. Thr main qustions should b addrssd in any rsarch involving intllignt intrfacs that dlivr usradaptiv intraction: What usr diffrncs should b considrd for adaptation? How to adquatly adapt to ths diffrncs? Whn to adapt, in ordr to maximiz adaptation ffctivnss and rduc intrusivnss? To Copyright 2014, Association for th Advancmnt of Artificial Intllignc ( All rights rsrvd. addrss ths qustions, w conductd thr studis dsignd to achiv th following objctivs: Objctiv 1: invstigat whthr a varity of usr and task charactristics impact usr prformanc and satisfaction in diffrnt visualization contxts. Essntially, w wantd to idntify which charactristics hav nough impact on usr visualization xprinc to justify adapting to ths charactristics. (what to adapt to). Objctiv 2: Provid y-tracking data to b analyzd to undrstand if/how usrs and tasks charactristics affct usr attntion pattrns to spcific lmnts of a visualization, to idntify possibl targts for adaptation (how to adapt). Objctiv 3: Invstigat if y-tracking data can inform usr modls to prdict, in ral-tim, charactristics rlvant for adaptation (how and whn to adapt) Rlatd Work Our work on usr-adaptiv visualizations draws from rsarch in thr rlatd aras: analyzing influncs of usr traits on visualization ffctivnss, usr modling, and th us of y-tracking to build usr and task modls. Th influnc of usr traits on th ffctivnss of visualizations has bn studid for both cognitiv abilitis and prsonality-basd traits. Prcptual spd, visual working mmory, and vrbal working mmory cognitiv abilitis wr found to influnc both prformanc with and prfrncs for visualizations (Conati and Maclarn, 2008), (Vlz t al., 2005) (Tokr t al., 2012). Th locus of control prsonality trait was found to influnc prformanc on visualization tasks (Zimkiwicz t al., 2011). Studis linking usr traits to visualization ffctivnss motivat th nd to stimat thos traits during visualization us. Svral rsarchrs hav approachd this task by tracking usr intrfac actions. For instanc,

2 Grawmyr (2006) and Mouin and Lapalm (2012) rcordd usr slctions among altrnativ visualizations to rcommnd visualizations in subsqunt tasks. Gotz and Wn (2009) track suboptimal usr intraction pattrns to rcommnd altrnativ visualizations for th currnt task. (Ahn and Brusilovsky, 2013) track a history of usr sarch trms to customiz th display of xploratory sarch rsults. Gaz data has bn shown to b a valuabl sourc of information for usr modling in various domains. Eivazi and Bdnarik (2011) usd gaz data to prdict usr stratgis whn solving a puzzl gam. Kardan and Conati (2013) and Bondarva t al. (2013) us gaz to prdict studnt larning with ducational softwar, whil Jaqus t al. (2014) lvrags it for affct prdiction. Liu t al. (2009) prdict skill lvl diffrncs btwn usrs in collaborativ tasks. Usr Studis Th first study (Tokr t al. 2012) lookd at th thr objctivs abov in th contxt of using two Infovis tchniqus: bar graphs and radar graphs (Bar/Radar study from now on). Th scond study (Carnini t al 2014) xtndd th first study by valuating a varity of visual prompts dsignd to hlp usrs procss bar graphs, with th long trm goal of undrstanding which, if any, of ths visual prompts could b suitabl as adaptiv intrvntions undr spcific circumstancs (Intrvntion study from now on). Whil th first two studis involvd visualizations that could only b procssd visually, th third study (Conati t al 2014) xtndd our invstigation to intractiv visualizations, i.. visualizations that provid usrs with a varity of functionalitis to xplor th visualizd data intractivly. W rfr to this study as VC study, bcaus it targtd an intractiv visualization calld ValuChart, which is dsignd to support usrs in dcision making tasks involving prfrntial choic (i.., th procss of slcting th bst option out of a possibly larg st of altrnativs basd on multipl attributs). In all thr studis w collctd information on a varity of usr charactristics that could affct a usr s visualization xprinc. Ths includ visualization xprtis, as wll as thr cognitiv masurs: prcptual spd (PS), visual working mmory (viswm) and vrbal working mmory (vrbwm). Th Intrvntion and VC study also includd locus of control, a prsonality trait that othr studis hav shown to impact visualization ffctivnss (Zimkiwicz t al 2011). Th VC study also includs masurs of task xprtis, i.. xprtis in making prfrntial choics in gnral and by using visualization aids. All thr studis considrd diffrnt task typs and varid task complxity, to invstigat if and how ths task charactristics influnc th impact of usr diffrncs on visualization xprinc. Th Bar/Radar study (Tokr t al. 2012) includd 5 diffrnt task typs, chosn from a st of low-lvl analysis tasks idntifid as largly capturing popl s activitis whil mploying information visualization (.g., rtriv th valu of a spcific datapoint, find th datapoint with an xtrm valu in th datast tc.) (Amar t al. 2005). Th study also varid task complxity, by visualizing two datasts of diffrnt siz (i., consisting of ithr two or thr data sris rspctivly). Th Intrvntion (Carnini t al 2014) and VC study (Conati t al 2014) also varid task typ and complxity, but in ordr to limit th numbr of xprimntal conditions, complxity was varid by slcting task typs that wr, rspctivly, among th simplst and th most complx in Amar t al (2005) rathr than including multipl datasts of diffrnt complxity. In addition to low-lvl visualization tasks drivd from Amar t al (2005), th VC study also includd th highlvl task of using ValuChart to xplor at will a st of altrnativs (.g., movis to watch), and slct th prfrrd itm. Dpndnt masurs wr collctd in trms of both prformanc (.g., loggd task compltion tim), as wll as subjctiv masurs of usr satisfaction. Also, in ach study, usrs gaz was trackd via a Tobii T120 ytrackr. W analyzd study data in thr diffrnt ways. To invstigat th impact of usr and task charactristics on visualization xprinc (Objctiv 1), w ran linar mixd-ffcts modl (Mixd Modl) analyss with usr and task charactristics as factors/covariats, and prformanc and satisfaction masurs as dpndnt variabls (Tokr t al 2012, Carnini t al 2014, Conati t al 2014). To invstigat th impact of usr/task charactristics on gaz bhavior (Objctiv 2), w ran similar mixd modls whr th dpndnt variabls wr a varity of summativ statistics on gaz masurs,.g., rat of gaz fixations, avrag fixation lngth, prcntag of gaz transitions btwn salint aras of th visualization (known as Aras of Intrst, or AOI) (Tokr t al 2013, Tokr t al 2014). Finally, to invstigat whthr gaz data can hlp build usr modls that prdict rlvant usr/task charactristics during visualization procssing (Objctiv 3), w ran machin larning xprimnts that lvragd diffrnt fatur sts basd on gaz data to prdict usr prformanc and cognitiv traits, as wll as task typ and difficulty (Stichn t al Stichn t al 2014, Gingric and Conati, submittd). W hav compltd all typs of analysis for both th Radar/Bar (Tokr t al, 2012, Tokr t al 2013, Stichn t al 2014) and Intrvntion study (Carnini t al 2014, Tokr t al

3 2014, Gingrich and Conati submittd) whras for th Valu Chart study w has so far prformd only th analysis rlatd to Objctiv 1 (Conati t al 2014) In th nxt sction w summariz a slction of rsults from th analyss. All rsults rportd ar statistically significant at th (adjustd).05 lvl, unlss othrwis qualifid Ovrviw of Rsults Impact of usr charactristics on prformanc Bar/Radar study (Tokr t al 2012). For simpl tasks (i.., tasks prformd with th simplr datast) w found, not surprisingly, that highr PS corrspondd to fastr compltion tim with both visualizations. Howvr, w also found that th diffrnc in tim prformanc btwn bar and radar graphs dcrass as usr's PS incrass. This rsult is important bcaus it confirms th finding in (Conati and Maclatr 2008) that PS is a cognitiv masur that can impact th compard ffctivnss of two diffrnt visualizations, at last whn on of thm is a radar graph. For mor complx tasks, th main ffct of PS bcoms marginally significant, but still has a mdium-larg ffct siz. Individual diffrncs also impactd usr subjctiv prfrncs. Usrs with a high viswm gav highr prfrnc ratings to radar graphs than usrs with a low viswm, and usrs with a low vrbwm found bar graphs asir to us than usrs with high vrbalwm. Intrvntion study (Carnini t al 2014).Whil thr was no impact of cognitiv abilitis on task prformanc with simpl tasks (i., tasks asking usrs to rtriv a spcific valu from a bar chart), for mor complx tasks (rquiring usrs to comput aggrgat masurs ovr a subst of th data) all thr cognitiv abilitis (PS, viswm and vrbwm) had an impact: usrs with highr valus of ths abilitis prformd significantly bttr in trms of a prformanc masur combining accuracy and compltion tim. Ths rsults indicat that task complxity can significantly impact usr prformanc dpnding on cognitiv abilitis, and suggsts that usrs with lowr cognitiv abilitis would bnfit from adaptiv intrvntions whn tasks gt hard. It should b notd that whil th rsult for PS aligns with rsults in prvious work (Tokr t al 2012) for viswm and vrbwm, this study is th first to connct ths two cognitiv traits to task prformanc (as opposd to usr prfrncs) with a visualization, possibly bcaus prvious studis rlid on tasks that wr not complx nough to dtct ths ffcts. In trms of possibl influncs of individual diffrncs on th ffctivnss of th diffrnt intrvntions tstd in th study, non wr found for task prformanc: all intrvntions had a similar positiv impact on usrs prformanc, and wr bttr than rciving no intrvntion (xcpt for Avrag Rfrnc Lins with complx tasks). Individual diffrncs, howvr, affctd usrs subjctiv masurs of intrvntions usfulnss, spcifically diffrncs in viswm affctd th usfulnss ratings for Bolding and Avg. Rf Lins intrvntion. This finding confirms th influnc of viswm on subjctiv ratings found in (Tokr t al 2012). ValuChart study (Conati l al 2014). W invstigatd th ffct of usrs charactristics on prformanc on low and high lvl tasks, mdiatd by two visualization layout (vrtical & horizontal). Ths layouts can b considrd as a possibl form of prsonalization, sinc prvious studis with ValuCharts suggst that thy ar not quivalnt in trms of usr prformanc. For low-lvl tasks, intraction ffcts wr found btwn task typ and ach of: visualization xprtis, PS, viswm, and vrbwm. Usrs with lowr visualization xprtis ar significantly slowr in mor complx low-lvl tasks, suggsting that prsonalizd support should b availabl to non-xprts for such tasks. In gnral, as w found for th Intrvntion Study, lowr lvls of usr prcptual spd and vrbal working mmory ngativly impactd prformanc on spcific task typs. Howvr, for all low-lvl tasks, usrs with lowr viswm wr significantly fastr that usrs with highr viswm whn thy workd with th horizontal layout, contrary to prvious findings showing that lowr viswm usrs ar at a disadvantag. This rsult is important bcaus it indicats that giving usrs th appropriat visual artifacts for thir cognitiv abilitis (.g. a horizontal layout for usrs with low viswm), can compnsat for limitations in ths abilitis. Bar /radar Tabl 1. Faturs on which ach usr charactristic has a significant impact pr xprimnt. Intrvntions Valu charts Prcptual spd prformanc prformanc prformanc Vrbal working mmory Eas-ofus ratings prformanc prformanc Visual working mmory -Bar vs. radar prfrnc ratings prformanc Eas-of-us ratings for intrvntions prformanc Usr xprtis prformanc For high-lvl tasks, usrs with low slf-ratd frquncy of using visualizations to mak prfrntial choics spnt significantly lss tim making dcisions with th vrtical layout, with similar lvls of dcision confidnc or dcision satisfaction. This suggsts that prsonalization basd on layouts could incras fficacy on high-lvl

4 dcision making tasks. Tabl 2 summarizs all th main significant ffcts of usr charactristics found in our analyss Analysis of y-tracking data W invstigatd (i) if usrs charactristics impact gaz bhavior during visualization procssing tasks, (ii) which gaz faturs ar th most influncd by usrs charactristics (Objctiv 2). W summariz hr som of th rsults that can b lvragd to undrstand how to provid usr-adaptiv intrvntions. In th Bar/Radar study (Tokr t al 2013) w found that PS significantly impactd gaz bhavior, influncing fixation rat and gaz masurs rlating to th lgnd, labls and graph rgions (Aras of Intrsts, or AOI, from now on). For instanc, usrs with low PS spnt mor tim and transitiond mor oftn to th labls and lgnd rgion of th visualization. This ffct was mor pronouncd whn prforming difficult tasks. This finding suggsts supporting usrs with low PS (.g., oldr adults and popl with autism) in trms of lgnd/labl procssing, spcially for mor difficult tasks, givn that ths usrs hav lowr prformanc, as discussd in th prvious sction (Tokr t al 2012). Similar rsults and conclusions for labl procssing wr found in th Intrvntion study, i.. usrs with low PS had lowr prformanc and spnt mor tim procssing th labl rgion of th visualization with complx tasks (Tokr t al 2014). Th intrvntion study, in addition, uncovrd analogous finding for visvw and vrwm. Usrs with lowr lvls of ths masurs tndd to hav wors task prformanc, as discussd abov, which can b xplaind by th impact that ths masurs wr found to hav on diffrnt lmnts of th Intrvntion study visualizations. For instanc, usrs with low viswm spnt mor tim and transitiond mor oftn to th "answr input" rgion of th visualization on complx tasks, suggsting that ths usrs likly hav difficulty conncting th answr options in th input ara with th information in th graph, which causs thm to go back and forth btwn th input and th othr graph aras mor oftn than high visualwm usrs do. This bhavior can xplain why in this study low visualwm usrs wr found to b slowr at solving th tasks than thir high visualvw countrparts. This combination of findings suggst that w may want to xprimnt with dsigning adaptiv support for low visualwm usrs that focuss on facilitating procssing of th input options in rlation to th task (.g., xprimnt with diffrnt input mthods or visual rprsntations of radio buttons). Similarly, usrs with low vrbwm spnd mor of thir tim rading th txtual lmnts of th visualization (lgnd, labls and qustions). This ffct can xplain th incras in task compltion tim that w found for low VisualWM usrs, indicating that it is worthwhil to invstigat adaptiv intrvntions that aid th procssing of a visualization s txtual componnt for ths usrs. All th abov rsults provid vidnc that usrs with lowr cognitiv abilitis could bnfit from adaptiv intrvntions that can hlp thm procss visualizations componnts that may affct thir task prformanc, and that y gaz analysis can hlp idntify ths componnts Classification xprimnts basd on gaz data W invstigatd if usrs charactristics, tasks complxity and usrs prformanc can b prdictd solly basd on y tracking data (Objctiv 3), for both th Bar/Radar and Intrvntion studis (Stichn t al. 2103, 2014; Gingrich and Conati submittd) For both datasts, w wr abl to build classifirs that prdict ach of th abov masurs with accuracy significantly bttr than majority baslin classifirs, rlativly arly on from th start of a targt visualization task. Classification accuracy for task typ rachs th % aftr obsrving only 10% of th gaz data for that task. This rsult has dirct implications for a systm ability to provid usful adaptiv support to usrs, givn th strong influnc that task typ/complxity has on usr prformanc, as discussd abov. For instanc, using gaz data a usr-adaptiv modul build for ths two visualizations would b abl to distinguish whthr a usr is ngagd in an asir or mor complx task, and considr adaptations accordingly. Usr prformanc classification complmnts task typ classification by idntifying occasions in which usrs ar most in nd of support, and in our xprimnts it rachs accuracis in th % aftr sing 10% of th data. Classification of usr cognitiv abilitis, which can b usful for th fin-graind tailoring of visualization support, has lowr accuracis in th rang, indicating that task typ and prformanc hav strongr impact on usr gaz bhaviors than ths cognitiv masurs. Still, it should b notd that ths accuracis ar achivd aftr sing usr gaz for only on task, and tasks in ths two usr studis ar basic short ons (on avrag shortr than a minut) than usrs would prform multipl tims whn procssing a visualization. Highr classification accuracis could likly by achivabl by allowing th systm to track th usr gaz ovr mor than on task. Discussion and Conclusions Th long-trm goal of th rsarch discussd in this papr is to dvis intllignt usr-adaptiv visualizations that can adapt in ral tim to th spcific nds and abilitis of ach individual usr. Basd on th rsults found in th thr

5 usr studis ovrviwd in this papr, w can idntify two broad catgoris of adaptiv support. Th first catgory consists of dlivring adaptiv intrvntions that can hlp th usr procss a givn visualization. For instanc, findings in th Intrvntion study suggst that, in th prsnc of complx tasks, usrs with low prcptual spd may bnfit from adaptiv intrvntions that hlp thm procss th labl rgion of th visualization, whras usrs with low visual working mmory should b givn support with th answr input rgion (.g., radio buttons vs. drop-down mnus), and usrs with low vrbal working mmory can b hlpd by adaptiv intrvntions that mphasiz all txtual lmnts of th visualization. Th scond catgory of adaptiv support consists of slcting th bst visualization or visualization layout among a st of altrnativs. For instanc, findings in th Bar Graph study suggst that usrs with low prcptual spd should b givn bar graphs instad of radar graphs whn working with information sking tasks, bcaus thy ar fastr with thm. On th othr hand, usrs with high visual working mmory and xprtis with radar graphs should b givn this typ of visualization bcaus thy ar likly to prfr it to bar graphs. Similarly, th VC study findings suggst that, for prforming low-lvl visualization tasks with Valu Charts, usrs with low visual working mmory should b givn a horizontal layout, which allows ths usrs to compnsat for thir visual working mmory limitations. Whn prforming high-lvl dcision-making tasks with valu charts, usrs with limitd xprtis in using visualizations for dcision making should b givn a vrtical layout, as thy spnt lss tim making thir dcision with this layout at no cost of dcision quality. In ordr to provid adaptiv support as dscribd abov, an adaptiv visualization nds to b abl to assss in ral tim whn intrvntions ar ndd and why. W hav prsntd promising rsults with two diffrnt datasts (from th Bar/Radar study and from th Intrvntion study) showing that classifirs basd solly on gaz data can prdict arly on during a task whthr th usr may nd hlp bcaus thy ar showing low prformanc with th currnt visualization st up. W also showd arly accurat prdiction of typ/complxity of th visualization task a usr is working on, which affcts th typ of support th usr may nd. Accurat information on th usr s cognitiv abilitis that can furthr qualify th typ of adaptiv support to b providd is hardr to obtain using solly gaz data on th currnt usr task, although our classifirs still prform significantly bttr than majority class baslin classifirs. On of our thrads for futur work involvs invstigating ways to incras prdiction accuracy for a usr s cognitiv abilitis, for instanc classifying ovr mor than on task, as wll as looking at additional faturs for classification such as pupil-basd masurs and action-basd faturs whn availabl (.g. in th valu Chart datast). A scond thrad involvs dsigning som of th diffrnt adaptation stratgis idntifid in this papr, and valuat thir ffctivnss first via Wizard of Oz studis and thn by implmnting th actual adaptiv cycl. Finally, w ar looking at practical applications of our approachs. For instanc, w ar lvraging xisting corpora of multimodal documnts (.g. articls from th Economist) with graphs, txt that dscrib diffrnt aspcts of th graphs, and xplicit links btwn rlatd sntncs and graph lmnts, to track whn a usr is rading sntncs that rquir attntion to th graph and solicit attntion to th rlvant lmnts in an adaptiv mannr.. Rfrncs Ja-wook Ahn and Ptr Brusilovsky Adaptiv visualization for xploratory information rtrival. Information Procssing & Managmnt, 49(5): , Sptmbr. Robrt Amar, Jams Eagan, and John Stasko Low-Lvl Componnts of Analytic Activity in Information Visualization. In Procdings of th Procdings of th 2005 IEEE Symposium on Information Visualization, pags 15 21, Washington, DC, USA. IEEE Computr Socity. Daria Bondarva, Cristina Conati, Rza Fyzi-Bhnagh, Jason M. Harly, Rogr Azvdo, and François Boucht Infrring Larning from Gaz Data during Intraction with an Environmnt to Support Slf-Rgulatd Larning. In Artificial Intllignc in Education, volum 7926, pags Springr Brlin Hidlbrg, Brlin, Hidlbrg. Giuspp Carnini, Cristina Conati, Enamul Hoqu, Bn Stichn, Drck Tokr, and Jams T. Enns Highlighting Intrvntions and Usr Diffrncs: Informing Adaptiv Information Visualization Support. Cristina Conati and Hathr Maclarn Exploring th rol of individual diffrncs in information visualization. In Procdings of th working confrnc on Advancd visual intrfacs, pags , Nw York, NY, USA. ACM. C. Conati, G. Carnini, B. Stichn, and D. Tokr, Evaluating th Impact of Usr Charactristics and Diffrnt Layouts on an Intractiv Visualization for Dcision Making, in Procdings of th Eurographics Confrnc on Visualization, 2014 (to appar) Shahram Eivazi and Roman Bdnarik Prdicting Problm- Solving Bhavior and Prformanc Lvls from Visual Attntion Data. In Procdings of 2nd Workshop on Ey Gaz in Intllignt Human Machin Intraction at IUI 2011, pags David Gotz and Zhn Wn Bhavior-drivn visualization rcommndation. In Procdings of th 14th intrnational

6 confrnc on Intllignt usr intrfacs, pags , Nw York, NY, USA. ACM. Data for Usr-Adaptiv Information Highlighting Bat Grawmyr Evaluation of ERST An Extrnal Rprsntation Slction Tutor. In Diagrammatic Rprsntation and Infrnc, volum 4045, pags Natasha Jaqus, Cristina Conati, Jason M. Harly, and Rogr Azvdo Prdicting Affct from Gaz Data during Intraction with an Intllignt Tutoring Systm. In Intllignt Tutoring Systms, Lctur Nots in Computr Scinc, pags Springr Intrnational Publishing, January. Samad Kardan and Cristina Conati Comparing and Combining Ey Gaz and Intrfac Actions for Dtrmining Usr Larning with an Intractiv Simulation. In Proc. of UMAP, 21st Int. Conf. on Usr Modling, Adaptation and Prsonalization. Yan Liu, Pi-Yun Hsuh, J. Lai, M. Sangin, M.-A. Nussli, and P. Dillnbourg Who is th xprt? Analyzing gaz data to prdict xprtis lvl in collaborativ applications. In IEEE Intrnational Confrnc on Multimdia and Expo, ICME 2009, pags Jun. M. Mouin and G. Lapalm Using Clustring to Prsonaliz Visualization. In th Intrnational Confrnc on Information Visualisation (IV), pags July. Bn Stichn, Giuspp Carnini, and Cristina Conati Usr-adaptiv information visualization: using y gaz data to infr visualization tasks and usr cognitiv abilitis. In Procdings of th 2013 intrnational confrnc on Intllignt usr intrfacs, pags , Nw York, NY, USA. ACM. Bn Stichn, Cristina Conati, and Giuspp Carnini. 2014a. Infrring Visualization Proprtis, Usr Prformanc, and Usr Cognitiv Abilitis from Ey Gaz Data. ACM Transactions on Intractiv Intllignt Systms. Drck Tokr, Cristina Conati, Giuspp Carnini, and Mona Haraty Towards adaptiv information visualization: on th influnc of usr charactristics. In Proc. of UMAP, 20th Int. Conf. on Usr Modling, Adaptation and Prsonalization. Drck Tokr, Cristina Conati, Bn Stichn, and Giuspp Carnini Individual Usr Charactristics and Information Visualization: Conncting th Dots Through Ey Tracking. In Procdings of th SIGCHI Confrnc on Human Factors in Computing Systms, pags , Nw York, NY, USA. M.C. Vlz, D. Silvr, and M. Trmain Undrstanding visualization through spatial ability diffrncs. In IEEE Visualization, VIS 05, pags C. Zimkiwicz, R.J. Crousr, A.R. Yauilla, S.L. Su, W. Ribarsky, and R. Chang How locus of control influncs compatibility with visualization styl. In 2011 IEEE Confrnc on Visual Analytics Scinc and Tchnology (VAST), pags Octobr. Gingrich M. and Conati C., (Submittd) Constructing Modls of Usr and Charactristics from Ey Gaz. Submittd to AAAI 2015