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A cognition-centered personalization framework for cultural-heritage content

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Abstract

The heterogeneity of the audience of cultural heritage institutions introduces numerous challenges to the delivery of the content. Considering that people differ in the way they perceive, process, and recall information and that their individual cognitive differences influence their experience, performance, and knowledge acquisition when performing cultural-heritage activities, the human-cognition factor should be considered as an important personalization factor within cultural-heritage contexts. To this end, we propose a cognition-centered personalization framework for delivering cultural-heritage activities, tailored to the users’ cognitive characteristics. The framework implements rule-based personalization algorithms that are based on cognition-centered user models that are created implicitly, transparently, and in run-time based on classifiers that correlate end-user cognitive characteristics with interaction and visual behavior patterns. For evaluating the proposed framework and improving the external validity of the experimental results, we conducted two eye-tracking between-subjects user-studies (\(N=226\)) covering two different cognitive styles (field dependence–independence and visualizer–verbalizer) and two different types of cultural activity (visual goal-oriented and visual exploratory). The results provide evidence about the applicability, effectiveness, and efficiency of the proposed framework and underpin the added value of adopting cognition-centered personalization frameworks within digitized cultural-heritage interaction contexts.

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Notes

  1. In the literature, various terms have been used to describe these two types of behavior, such as exploration vs. inspection (Helmert et al. 2005) or skimming vs. scrutinizing (Lohmeyer and Meboldt 2015); however, they all describe the same two types.

References

  • Alexandridis, G., Chrysanthi, A., Tsekouras, G.E., Caridakis, G.: Personalized and content adaptive cultural heritage path recommendation: an application to the Gournia and Çatalhöyük archaeological sites. User Model. User-Adapt. Interaction (2019). https://doi.org/10.1007/s11257-019-09227-6

  • Alharthi, S.A., Raptis, G.E., Katsini, C., Dolgov, I., Nacke, L.E., Toups, Z.O.: Toward understanding the effects of cognitive styles on collaboration in multiplayer games. In: Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing, ACM, New York, NY, USA, CSCW ’18, pp. 169–172 (2018). https://doi.org/10.1145/3272973.3274047

  • Altanopoulou, P., Tselios, N., Katsanos, C., Georgoutsou, M., Panagiotaki, M.A.: Wiki-mediated activities in higher education: evidence-based analysis of learning effectiveness across three studies. J. Educ. Technol. Soc. 18(4), 511–522 (2015)

    Google Scholar 

  • Altun, A., Cakan, M.: Undergraduate students’ academic achievement, field dependent/independent cognitive styles and attitude toward computers. Educ. Technol. Soc. 9(1), 289–297 (2006)

    Google Scholar 

  • Alwi, A., Mckay, E.: Investigating online museum exhibits and personal cognitive learning preferences. Proc. Ascilite Auckl. 2009, 25–34 (2009)

    Google Scholar 

  • Angeli, C., Valanides, N., Kirschner, P.: Field dependence independence and instructional-design effects on learners’ performance with a computer-modeling tool. Comput. Hum. Behav. 25(6), 1355–1366 (2009). https://doi.org/10.1016/j.chb.2009.05.010

    Article  Google Scholar 

  • Angeli, C., Valanides, N., Polemitou, E., Fraggoulidou, E.: An interaction effect between young children’s field dependence-independence and order of learning with glass-box and black-box simulations: evidence for the malleability of cognitive style in computer-supported learning. Comput. Hum. Behav. 61, 569–583 (2016). https://doi.org/10.1016/j.chb.2016.03.060

    Article  Google Scholar 

  • Antoniou, A., Lepouras, G.: Modeling visitors’ profiles: a study to investigate adaptation aspects for museum learning technologies. ACM J. Comput. Cult. Herit. (JOCCH) 3(2), 1–19 (2010). https://doi.org/10.1145/1841317.1841322

    Article  Google Scholar 

  • Antoniou, A., Lepouras, G., Bampatzia, S., Almpanoudi, H.: An approach for serious game development for cultural heritage: case study for an archaeological site and museum. ACM J. Comput. Cult. Herit. (JOCCH) 6(4), 17:1–17:19 (2013). https://doi.org/10.1145/2532630.2532633

    Article  Google Scholar 

  • Antoniou, A., Katifori, A., Roussou, M., Vayanou, M., Karvounis, M., Kyriakidi, M., Pujol-Tost, L.: Capturing the visitor profile for a personalized mobile museum experience: an indirect approach. In: CEUR Workshop Proceedings 1618 (2016) http://ceur-ws.org/Vol-1618/HAAPIE_paper1.pdf

  • Ardissono, L., Kuflik, T., Petrelli, D.: Personalization in cultural heritage: the road travelled and the one ahead. User Model. User Adapt. Interact. 22(1), 73–99 (2012). https://doi.org/10.1007/s11257-011-9104-x

    Article  Google Scholar 

  • Ardito, C., Buono, P., Desolda, G., Matera, M.: From smart objects to smart experiences: an end-user development approach. Int. J. Hum. Comput. Stud. (2017). https://doi.org/10.1016/j.ijhcs.2017.12.002

    Article  Google Scholar 

  • Armstrong, S.J., Cools, E., Sadler-Smith, E.: Role of cognitive styles in business and management: reviewing 40 years of research. Int. J. Manag. Rev. 14(3), 238–262 (2012). https://doi.org/10.1111/j.1468-2370.2011.00315.x

    Article  Google Scholar 

  • Bahar, M., Hansell, M.H.: The relationship between some psychological factors and their effect on the performance of grid questions and word association tests. Educ. Psychol. 20(3), 349–364 (2000). https://doi.org/10.1080/713663739

    Article  Google Scholar 

  • Baltrunas, L., Ludwig, B., Peer, S., Ricci, F.: Context relevance assessment and exploitation in mobile recommender systems. Pers. Ubiquitous Comput. 16(5), 507–526 (2012). https://doi.org/10.1007/s00779-011-0417-x

    Article  Google Scholar 

  • Barz, M., Daiber, F., Bulling, A.: Prediction of gaze estimation error for error-aware gaze-based interfaces. In: Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications (ETRA ’16), pp. 275–278 (2016). https://doi.org/10.1145/2857491.2857493

  • Basile, P., de Gemmis, M., Iaquinta, L., Lops, P., Musto, C., Narducci, F., Semeraro, G.: SpIteR: A module for recommending dynamic personalized museum tours. In: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, Volume 01. IEEE Computer Society, Washington, DC, USA, WI-IAT ’09, pp. 584–587 (2009). https://doi.org/10.1109/WI-IAT.2009.99,

  • Belk, M., Fidas, C., Germanakos, P., Samaras, G.: Do human cognitive differences in information processing affect preference and performance of CAPTCHA? Int. J. Hum. Comput. Stud. 84, 1–18 (2015). https://doi.org/10.1016/j.ijhcs.2015.07.002

    Article  Google Scholar 

  • Belk, M., Fidas, C., Katsini, C., Avouris, N., Samaras, G.: Effects of human cognitive differences on interaction and visual behavior in graphical user authentication. In: Bernhaupt, R., Dalvi, G., Joshi, A, K Balkrishan, D., O’Neill, J., Winckler, M. (eds.) Human–Computer Interaction—INTERACT 2017. Springer International Publishing, Cham, pp. 287–296 (2017). https://doi.org/10.1007/978-3-319-67687-6_19

  • Bixler, R., D’Mello, S.: Automatic gaze-based user-independent detection of mind wandering during computerized reading. User Model. User-Adapt. Interact. 26(1), 33–68 (2016). https://doi.org/10.1007/s11257-015-9167-1

    Article  Google Scholar 

  • Bohnert, F., Zukerman, I.: Personalised viewing-time prediction in museums. User Model. User-Adapt. Interact. 24(4), 263–314 (2014). https://doi.org/10.1007/s11257-013-9141-8

    Article  Google Scholar 

  • Brusilovsky, P., Millán, E.: User models for adaptive hypermedia and adaptive educational systems. In: The Adaptive Web. Springer, Berlin, pp. 3–53 (2007). https://doi.org/10.1007/978-3-540-72079-9_1

  • Cameron, B., Dwyer, F.: The effect of online gaming, cognition and feedback type in facilitating delayed achievement of different learning objectives. J. Interactive Learn. Res. 16(3), 243–258 (2005)

    Google Scholar 

  • Carmagnola, F., Cena, F., Console, L., Cortassa, O., Gena, C., Goy, A., Torre, I., Toso, A., Vernero, F.: Tag-based user modeling for social multi-device adaptive guides. User Model. User-Adapt. Interact. 18(5), 497–538 (2008). https://doi.org/10.1007/s11257-008-9052-2

    Article  Google Scholar 

  • Chang, K.E., Chang, C.T., Hou, H.T., Sung, Y.T., Chao, H.L., Lee, C.M.: Development and behavioral pattern analysis of a mobile guide system with augmented reality for painting appreciation instruction in an art museum. Comput. Educ. 71, 185–197 (2014). https://doi.org/10.1016/j.compedu.2013.09.022

    Article  Google Scholar 

  • Chang, B., Chen, S.Y., Jhan, S.N.: The influences of an interactive group-based videogame: cognitive styles vs. prior ability. Comput. Educ. 88, 399–407 (2015). https://doi.org/10.1016/j.compedu.2015.08.006

    Article  Google Scholar 

  • Chen, C.F., Chen, F.S.: Experience quality, perceived value, satisfaction and behavioral intentions for heritage tourists. Tourism Manag. 31(1), 29–35 (2010). https://doi.org/10.1016/j.tourman.2009.02.008

    Article  Google Scholar 

  • Chen, C.M., Huang, S.H.: Web-based reading annotation system with an attention-based self-regulated learning mechanism for promoting reading performance. Br. J. Educ. Technol. 45(5), 959–980 (2014). https://doi.org/10.1111/bjet.12119

    Article  Google Scholar 

  • Chen, S.Y., Liu, X.: An integrated approach for modeling learning patterns of students in web-based instruction. ACM Trans. Comput. Hum. Interact. 15(1), 1–28 (2008). https://doi.org/10.1145/1352782.1352783

    Article  Google Scholar 

  • Chen, X., Starke, S.D., Baber. C., Howes, A.: A cognitive model of how people make decisions through interaction with visual displays. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI ’17). ACM Press, New York, New York, USA, pp. 1205–1216 (2017). https://doi.org/10.1145/3025453.3025596

  • Cheverst, K., Davies, N., Mitchell, K., Smith, P.: Providing tailored (context-aware) information to city visitors. In: Brusilovsky, P., Stock, O., Strapparava, C. (eds.) Adaptive Hypermedia and Adaptive Web-Based Systems. Springer, Berlin, pp. 73–85 (2000). https://doi.org/10.1007/3-540-44595-1_8

  • Clewley, N., Chen, SY., Liu, X.: Cognitive styles and web-based instruction: field dependent/independent vs. holist/serialist. In: 2009 IEEE International Conference on Systems, Man and Cybernetics. IEEE, pp. 2074–2079 (2009). https://doi.org/10.1109/ICSMC.2009.5346314

  • Clewley, N., Chen, S.Y., Liu, X.: Cognitive styles and search engine preferences. J. Doc. 66(4), 585–603 (2010). https://doi.org/10.1108/00220411011052966

    Article  Google Scholar 

  • Codish, D., Ravid, G.: Personality based gamification—educational gamification for extroverts and introverts. In: Conference for the Study of Innovation and Learning Technologies: Learning in the Technological Era (CHAIS ’14), pp. 36–44 (2012). https://doi.org/10.1108/VJIKMS-06-2014-0043

  • Coenen, T., Mostmans, L., Naessens, K.: MuseUs: case study of a pervasive cultural heritage serious game. ACM J. Comput. Cult. Herit. (JOCCH) 6(2), 1–19 (2013). https://doi.org/10.1145/2460376.2460379

    Article  Google Scholar 

  • Costantini, S., Mostarda, L., Tocchio, A., Tsintza, P.: DALICA: agent-based ambient intelligence for cultural-heritage scenarios. IEEE Intell. Syst. 23(2), 34–41 (2008). https://doi.org/10.1109/MIS.2008.24

    Article  Google Scholar 

  • Cramer, H., Evers, V., Ramlal, S., van Someren, M., Rutledge, L., Stash, N., Aroyo, L., Wielinga, B.: The effects of transparency on trust in and acceptance of a content-based art recommender. User Model. User-Adapt. Interact. 18(5), 455–496 (2008). https://doi.org/10.1007/s11257-008-9051-3

    Article  Google Scholar 

  • Crosby, M.E., Ikehara, C.S.: Using real-time physiological monitoring for assessing cognitive states. In: Digital Multimedia Perception and Design. IGI Global, pp. 170–186 (2006). https://doi.org/10.4018/978-1-59140-860-4.ch008

  • Cureton, E.E.: The upper and lower twenty-seven per cent rule. Psychometrika 22(3), 293–296 (1957). https://doi.org/10.1007/BF02289130

    Article  MATH  Google Scholar 

  • Davis, J.K.: Educational implications of field dependence-independence. Field dependence independence: cognitive style across the life span, pp 149–176

  • Denisova, A., Nordin, A.I., Cairns, P.: The convergence of player experience questionnaires. In: Proceedings of the 2016 Annual Symposium on Computer-Human Interaction in Play. ACM, New York, NY, USA, CHI PLAY ’16, pp. 33–37 (2016). https://doi.org/10.1145/2967934.2968095

  • Dim, E., Kuflik, T.: Automatic detection of social behavior of museum visitor pairs. ACM Trans. Interact. Intell. Syst. 4(4), 17:1–17:30 (2014). https://doi.org/10.1145/2662869

    Article  Google Scholar 

  • Dunnewold, A.L.: Thought field therapy efficacy following large scale traumatic events. Curr. Res. Psychol. 5(1), 34–39 (2014). https://doi.org/10.3844/crpsp.2014.34.39

    Article  Google Scholar 

  • Dwyer, F.M., Moore, D.M.: Effect of color coding on visually and verbally oriented tests with students of different field dependence levels. J. Educ. Technol. Syst. 20(4), 311–320 (1992). https://doi.org/10.2190/T0EY-KF0H-0RTV-X5DG

    Article  Google Scholar 

  • Elley, W.B.: Vocabulary acquisition from listening to stories. Read. Res. Q. 24(2), 174 (1989). https://doi.org/10.2307/747863

    Article  Google Scholar 

  • Eraslan, S., Yesilada, Y., Harper, S.: Scanpath trend analysis on web pages: clustering eye tracking scanpaths. ACM Trans. Web (TWEB) 10(4), 20:1–20:35 (2016). https://doi.org/10.1145/2970818

    Article  Google Scholar 

  • Evans, C., Richardson, J.T.E., Waring, M.: Field independence: reviewing the evidence. Br. J. Educ. Psychol. 83(2), 210–224 (2013). https://doi.org/10.1111/bjep.12015

    Article  Google Scholar 

  • Falk, J.H., Dierking, L.D.: The museum experience revisited. Routledge, London (2016)

    Book  Google Scholar 

  • Fidas, C.A., Avouris, N.M.: Personalization of mobile applications in cultural heritage environments. In: Proceedings of the 6th International Conference on Information, Intelligence, Systems and Applications (IISA 2015), pp. 1–6 (2015). https://doi.org/10.1109/IISA.2015.7388114

  • Fosh, L., Benford, S., Koleva, B.: Supporting group coherence in a museum visit. In: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing. ACM, New York, NY, USA, CSCW ’16, pp. 1–12 (2016). https://doi.org/10.1145/2818048.2819970

  • Frias-Martinez, E., Chen, S.Y., Liu, X.: Automatic cognitive style identification of digital library users for personalization. J. Am. Soc. Inf. Sci. Technol. 58(2), 237–251 (2007). https://doi.org/10.1002/asi.20477

    Article  Google Scholar 

  • Frias-Martinez, E., Chen, S.Y., Liu, X.: Evaluation of a personalized digital library based on cognitive styles: adaptivity vs. adaptability. Int. J. Inf. Manag. 29(1), 48–56 (2009). https://doi.org/10.1016/j.ijinfomgt.2008.01.012

    Article  Google Scholar 

  • Froschauer, J., Merkl, D., Arends, M., Goldfarb, D.: Art history concepts at play with ThIATRO. ACM J. Comput. Cult. Herit. (JOCCH) 6(2), 7:1–7:15 (2013). https://doi.org/10.1145/2460376.2460378

    Article  Google Scholar 

  • Germanakos, P., Belk, M.: A generic human-centred personalization framework: the case of mapU. In: Human-Centred Web Adaptation and Personalization: From Theory to Practice. Springer International Publishing, Cham, pp. 137–182 (2016). https://doi.org/10.1007/978-3-319-28050-9_5

  • Ghiani, G., Leporini, B., Paternò, F.: Supporting orientation for blind people using museum guides. In: CHI ’08 Extended Abstracts on Human Factors in Computing Systems. ACM, New York, NY, USA, CHI EA ’08, pp. 3417–3422 (2008). https://doi.org/10.1145/1358628.1358867

  • Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009). https://doi.org/10.1145/1656274.1656278

    Article  Google Scholar 

  • Han, S., Yang, S., Kim, J., Gerla, M.: EyeGuardian: A framework of eye tracking and blink detection for mobile device users. In: Proceedings of the Twelfth Workshop on Mobile Computing Systems & Applications. ACM, New York, NY, USA, HotMobile ’12, pp. 6:1–6:6 (2012). https://doi.org/10.1145/2162081.2162090

  • Helmert, J.R., Joos, M., Pannasch, S., Velichkovsky, B.M.: Two visual systems and their eye movements: evidence from static and dynamic scene perception. Proc. Cognit. Sci. Soc. 27(27), 2283–2288 (2005). http://escholarship.org/uc/item/9nv0c661#page-1

  • Hochberg, L., Ovesdotter Alm, C., Rantanen, E.M., Yu, Q., Delong, C.M., Haake, A., Alm, C.O.: Towards automatic annotation of clinical decision-making style. In: Proceedings of LAW VIII—The 8th Linguistic Annotation Workshop, pp. 129–138 (2014)

  • Hong, J.C., Hwang, M.Y., Tam, K.P., Lai, Y.H., Liu, L.C.: Effects of cognitive style on digital jigsaw puzzle performance: a GridWare analysis. Comput. Hum. Behav. 28(3), 920–928 (2012). https://doi.org/10.1016/j.chb.2011.12.012

    Article  Google Scholar 

  • Hong, M., Jung, J.J., Piccialli, F., Chianese, A.: Social recommendation service for cultural heritage. Pers. Ubiquitous Comput. 21(2), 191–201 (2017). https://doi.org/10.1007/s00779-016-0985-x

    Article  Google Scholar 

  • Hwang, G.J., Sung, H.Y., Hung, C.M., Huang, I., Tsai, C.C.: Development of a personalized educational computer game based on students’ learning styles. Educ. Technol. Res. Dev. 60(4), 623–638 (2012). https://doi.org/10.1007/s11423-012-9241-x

    Article  Google Scholar 

  • Janiszewski, C.: The influence of display characteristics on visual exploratory search behavior. J. Consum. Res. 25(3), 290–301 (1998). https://doi.org/10.1086/209540

    Article  Google Scholar 

  • Jennett, C., Cox, A.L., Cairns, P., Dhoparee, S., Epps, A., Tijs, T., Walton, A.: Measuring and defining the experience of immersion in games. Int. J. Hum. Comput. Stud. 66(9), 641–661 (2008). https://doi.org/10.1016/j.ijhcs.2008.04.004

    Article  Google Scholar 

  • Kaptein, M., Parvinen, P.: Advancing E-commerce personalization: process framework and case study. Int. J. Electron. Commer. 19(3), 7–33 (2015). https://doi.org/10.1080/10864415.2015.1000216

    Article  Google Scholar 

  • Karaman, S., Bagdanov, A.D., D’Amico, G., Landucci, L., Ferracani, A., Pezzatini, D., Del Bimbo, A.: Passive profiling and natural interaction metaphors for personalized multimedia museum experiences. In: Petrosino, A., Maddalena, L., Pala, P. (eds.) New Trends in Image Analysis and Processing—ICIAP 2013. Springer, Berlin, pp. 247–256 (2013). https://doi.org/10.1007/978-3-642-41190-8_27,

  • Katifori, A., Karvounis, M., Kourtis, V., Kyriakidi, M., Roussou, M., Tsangaris, M., Vayanou, M., Ioannidis, Y., Balet, O., Prados, T., Keil, J., Engelke, T., Pujol, L.: CHESS: personalized storytelling experiences in museums. In: Mitchell, A., Fernández-Vara, C., Thue, D. (eds.) Interactive Storytelling. Springer International Publishing, Cham, pp. 232–235 (2014). https://doi.org/10.1007/978-3-319-12337-0_28,

  • Katsini, C., Fidas, C., Belk, M., Avouris, N., Samaras, G.: Influences of users’ cognitive strategies on graphical password composition. In: Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems. ACM, New York, NY, USA, CHI EA ’17, pp. 2698–2705 (2017). https://doi.org/10.1145/3027063.3053217

  • Katsini, C., Fidas, C., Raptis, G.E., Belk, M., Samaras, G., Avouris, N.: Eye gaze-driven prediction of cognitive differences during graphical password composition. In: 23rd International Conference on Intelligent User Interfaces. ACM, New York, NY, USA, IUI ’18, pp. 147–152 (2018a). https://doi.org/10.1145/3172944.3172996

  • Katsini, C., Fidas, C., Raptis, G.E., Belk, M., Samaras, G., Avouris, N.: Influences of human cognition and visual behavior on password strength during picture password composition. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, New York, NY, USA, CHI ’18, pp. 87:1–87:14 (2018b). https://doi.org/10.1145/3173574.3173661

  • Katsini, C., Fidas, C., Belk, M., Samaras, G., Avouris, N.: A human cognitive perspective of users’ password choices in recognition-based graphical authentication. Int. J. Hum. Comput. Interact. (2019). https://doi.org/10.1080/10447318.2019.1574057

  • Kempiak, J., Hollywood, L., Bolan, P., McMahon-Beattie, U.: The heritage tourist: an understanding of the visitor experience at heritage attractions. Int. J. Herit. Stud. 23(4), 375–392 (2017). https://doi.org/10.1080/13527258.2016.1277776

    Article  Google Scholar 

  • Khatib, M., Hosseinpur, R.M.: On the validity of the group embedded figure test (GEFT). J. Lang. Teach. Res. 2(3), 640–648 (2011). https://doi.org/10.4304/jltr.2.3.640-648

    Article  Google Scholar 

  • Kim, K.S.: Implications of user characteristics in information seeking on the world wide web. Int. J. Hum. Comput. Interact. 13(3), 323–340 (2001). https://doi.org/10.1207/S15327590IJHC1303_3

    Article  Google Scholar 

  • Koć-Januchta, M., Höffler, T., Thoma, G.B., Prechtl, H., Leutner, D.: Visualizers versus verbalizers: effects of cognitive style on learning with texts and pictures—an eye-tracking study. Comput. Hum. Behav. 68, 170–179 (2017). https://doi.org/10.1016/j.chb.2016.11.028

    Article  Google Scholar 

  • Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97(1–2), 273–324 (1997). https://doi.org/10.1016/S0004-3702(97)00043-X

    Article  MATH  Google Scholar 

  • Kontiza, K., Bikakis, A., Miller, R.: Cognitive-based visualization of semantically structured cultural heritage data. In: Proceedings of the International Workshop on Visualizations and User Interfaces for Ontologies and Linked Data (VOILA 2015), CEUR Workshop Proceedings, vol. 1456, pp. 61–68 (2015). http://ceur-ws.org/Vol-1456/paper6.pdf

  • Kozhevnikov, M.: Cognitive styles in the context of modern psychology: toward an integrated framework of cognitive style. Psychol. Bull. 133(3), 464–481 (2007). https://doi.org/10.1037/0033-2909.133.3.464

    Article  Google Scholar 

  • Krejtz, K., Duchowski, A., Szmidt, T., Krejtz, I., González Perilli, F., Pires, A., Vilaro, A., Villalobos, N.: Gaze transition entropy. ACM Trans. Appl. Percept. (TAP) 13(1), 4:1–4:20 (2015). https://doi.org/10.1145/2834121

    Article  Google Scholar 

  • Ku, O., Hou, C.C., Chen, S.Y.: Incorporating customization and personalization into game-based learning: a cognitive style perspective. Comput. Hum. Behav. 65, 359–368 (2016). https://doi.org/10.1016/j.chb.2016.08.040

    Article  Google Scholar 

  • Kuflik, T., Wecker, A.J., Lanir, J., Stock, O.: An integrative framework for extending the boundaries of the museum visit experience: linking the pre, during and post visit phases. Inf. Technol. Tour. 15(1), 17–47 (2015). https://doi.org/10.1007/s40558-014-0018-4

    Article  Google Scholar 

  • Lanir, J., Kuflik, T., Sheidin, J., Yavin, N., Leiderman, K., Segal, M.: Visualizing museum visitors’ behavior: where do they go and what do they do there? Pers. Ubiquitous Comput. 21(2), 313–326 (2017). https://doi.org/10.1007/s00779-016-0994-9

    Article  Google Scholar 

  • Liew, T.W., Tan, S.M., Seydali, R.: Learners’ field dependence and the effects of personalized narration on learners’ computer perceptions and task-related attitudes in multimedia learning. J. Educ. Technol. Syst. 42(3), 255–272 (2014). https://doi.org/10.2190/ET.42.3.e

    Article  Google Scholar 

  • Light, D.: Progress in dark tourism and thanatourism research: an uneasy relationship with heritage tourism. Tour. Manag. 61, 275–301 (2017). https://doi.org/10.1016/j.tourman.2017.01.011

    Article  Google Scholar 

  • Lightner, N.J.: Model testing of users’ comprehension in graphical animation: the effect of speed and focus areas. Int. J. Hum. Comput. Interact. 13(1), 53–73 (2001). https://doi.org/10.1207/S15327590IJHC1301_4

    Article  Google Scholar 

  • Lipton, Z.C., Elkan, C., Naryanaswamy, B.: Optimal thresholding of classifiers to maximize F1 measure. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds.) Machine Learning and Knowledge Discovery in Databases. Springer, Berlin, pp. 225–239 (2014). https://doi.org/10.1007/978-3-662-44851-9_15

  • Liu, M., Reed, W.M.: The relationship between the learning strategies and learning styles in a hypermedia environment. Comput. Hum. Behav. 10(4), 419–434 (1995). https://doi.org/10.1016/0747-5632(94)90038-8

    Article  Google Scholar 

  • Lo, J.J., Wang, Y.J.: Development of an adaptive EC website with online identified cognitive styles of anonymous customers. Int. J. Hum. Comput. Interact. 28(9), 560–575 (2012). https://doi.org/10.1080/10447318.2011.629952

    Article  Google Scholar 

  • Loftus, G.R.: Eye fixations and recognition memory for pictures. Cognit. Psychol. 3(4), 525–551 (1972). https://doi.org/10.1016/0010-0285(72)90021-7

    Article  Google Scholar 

  • Lohmeyer, Q., Meboldt, M.: How we understand engineering drawings: an eye tracking study investigating skimming and scrutinizing sequences. In: Proceedings of the 20th International Conference on Engineering Design (ICED15), Milan, Italy (2015)

  • Lu, Ch., Hong, J.C., Huang, Ph.: The effects of individual characteristics on children’s problem solving performances in the context of game-based learning. In: Redesigning Pedagogy: Culture, Knowledge and Understanding. National Institute of Education, Singapore (2007)

  • Mawad, F., Tras, M., Gimnez, A., Maiche, A., Ares, G.: Influence of cognitive style on information processing and selection of yogurt labels: insights from an eye-tracking study. Food Res. Int. 74, 1–9 (2015). https://doi.org/10.1016/j.foodres.2015.04.023

    Article  Google Scholar 

  • Mayer, R.E.: Aids to text comprehension. Educ. Psychol. 19(1), 30–42 (1984). https://doi.org/10.1080/00461528409529279

    Article  Google Scholar 

  • Mayer, R.E., Massa, L.J.: Three facets of visual and verbal learners: cognitive ability, cognitive style, and learning preference. J. Educ. Psychol. 95(4), 833–846 (2003). https://doi.org/10.1037/0022-0663.95.4.833

    Article  Google Scholar 

  • Mehigan, T.J., Barry, M., Kehoe, A., Pitt. I.: Using eye tracking technology to identify visual and verbal learners. In: Proceedings of 2011 IEEE International Conference on Multimedia and Expo (2011). https://doi.org/10.1109/ICME.2011.6012036

  • Mokatren, M., Kuflik, T., Shimshoni, I.: Exploring the potential of a mobile eye tracker as an intuitive indoor pointing device: a case study in cultural heritage. In: Future Generation Computer Systems, pp. 528–541 (2017). https://doi.org/10.1016/j.future.2017.07.007

  • MRF: Eye Tracking Market Research Report - Global Forecast 2023. Technical report, Half-Cooked Research Reports (2017)

  • Naudet, Y., Antoniou, A., Lykourentzou, I., Tobias, E., Rompa, J., Lepouras, G.: Museum personalization based on gaming and cognitive styles. Int. J. Virtual Communities Soc. Netw. 7(2), 1–30 (2015). https://doi.org/10.4018/IJVCSN.2015040101

    Article  Google Scholar 

  • Nelson, L., Held, C., Pirolli, P., Hong, L., Schiano, D., Chi, EH.: With a little help from my friends: examining the impact of social annotations in sensemaking tasks. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, New York, NY, USA, CHI ’09, pp. 1795–1798 (2009). https://doi.org/10.1145/1518701.1518977

  • Nisiforou, E., Laghos, A.: Field dependence independence and eye movement patterns: investigating users’ differences through an eye tracking study. Interact. Comput. 28(4), 407–420 (2016). https://doi.org/10.1093/iwc/iwv015

    Article  Google Scholar 

  • Not, E., Petrelli, D.: Blending customisation, context-awareness and adaptivity for personalised tangible interaction in cultural heritage. Int. J. Hum. Comput. Stud. (2018). https://doi.org/10.1016/j.ijhcs.2018.01.001

  • Not, E., Petrelli, D.: Empowering cultural heritage professionals with tools for authoring and deploying personalised visitor experiences. User Model. User-Adapt. Interact. (2019). https://doi.org/10.1007/s11257-019-09224-9

  • Oltman, P.K., Raskin, E., Witkin, H.A.: Group Embedded Figures Test. Consulting Psychologists Press, Palo Alto (1971)

    Google Scholar 

  • Over, E., Hooge, I., Vlaskamp, B., Erkelens, C.: Coarse-to-fine eye movement strategy in visual search. Vis. Res. 47(17), 2272–2280 (2007). https://doi.org/10.1016/j.visres.2007.05.002

    Article  Google Scholar 

  • Packer, J., Ballantyne, R.: Conceptualizing the visitor experience: a review of literature and development of a multifaceted model. Visit. Stud. 19(2), 128–143 (2016). https://doi.org/10.1080/10645578.2016.1144023

    Article  Google Scholar 

  • Paivio, A.: Mental Representations: A Dual-Coding Approach. Oxford University Press, Oxford (1986)

    Google Scholar 

  • Papathanassiou-Zuhrt, D.: Cognitive load management of cultural heritage information: an application multi-mix for recreational learners. Procedia Soc. Behav. Sci. 188, 57–73 (2015). https://doi.org/10.1016/j.sbspro.2015.03.339

    Article  Google Scholar 

  • Papoutsaki, A., Sangkloy, P., Laskey, J., Daskalova, N., Huang, J., Hays, J.: WebGazer: scalable webcam eye tracking using user interactions. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI 2016), pp. 3839–3845 (2016). http://par.nsf.gov/biblio/10024076

  • Pechenizkiy, M., Calders, T.: A framework for guiding the museum tours personalization. In: International ACM Workshop on Personalized Access to Cultural Heritage (PATCH), pp. 1–12 (2007)

  • Pedersen, I., Gale, N., Mirza-Babaei, P., Reid, S.: More than meets the eye: the benefits of augmented reality and holographic displays for digital cultural heritage. ACM J. Comput. Cult. Herit. (JOCCH) 10(2), 11:1–11:15 (2017). https://doi.org/10.1145/3051480

    Article  Google Scholar 

  • Perazzi, F., Krahenbuhl, P., Pritch, Y., Hornung, A.: Saliency filters: contrast based filtering for salient region detection. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp. 733–740 (2012). https://doi.org/10.1109/CVPR.2012.6247743

  • Perry, S., Roussou, M., Economou, M., Young, H., Pujol, L.: Moving beyond the virtual museum: engaging visitors emotionally. In: Proceedings of the 23rd International Conference on Virtual System Multimedia (VSMM 2017). IEEE, pp. 1–8 (2017). https://doi.org/10.1109/VSMM.2017.8346276,

  • Petersen, S.E., Posner, M.I.: The attention system of the human brain. Annu. Rev. Neurosci. 13, 25–42 (1990). https://doi.org/10.1146/annurev.ne.13.030190.000325

    Article  Google Scholar 

  • Petrelli, D., Not, E.: User-centred design of flexible hypermedia for a mobile guide: reflections on the HyperAudio experience. User Model. User Adapt. Interact. 15(3–4), 303–338 (2005). https://doi.org/10.1007/s11257-005-8816-1

    Article  Google Scholar 

  • Petrelli, D., Marshall, M.T., O’Brien, S., McEntaggart, P., Gwilt, I.: Tangible data souvenirs as a bridge between a physical museum visit and online digital experience. Pers. Ubiquitous Comput. 21(2), 281–295 (2017). https://doi.org/10.1007/s00779-016-0993-x

    Article  Google Scholar 

  • Petridis, P., Dunwell, I., Liarokapis, F., Constantinou, G., Arnab, S., de Freitas, S., Hendrix, M.: The herbert virtual museum. J. Electr. Comput. Eng. 2013, 1–8 (2013). https://doi.org/10.1155/2013/487970

    Article  Google Scholar 

  • Plass, J., Chun, D., Mayer, R., Leutner, D.: Supporting visual and verbal learning preferences in a second-language multimedia learning environment. J. Educ. Psychol. 90(1), 25–36 (1998). https://doi.org/10.1037/0022-0663.90.1.25

    Article  Google Scholar 

  • Pollalis, C., Fahnbulleh, W., Tynes, J., Shaer, O.: HoloMuse: enhancing engagement with archaeological artifacts through gesture-based interaction with holograms. In: Proceedings of the Eleventh International Conference on Tangible, Embedded, and Embodied Interaction. ACM, New York, NY, USA, TEI ’17, pp. 565–570 (2017). https://doi.org/10.1145/3024969.3025094

  • Pollatsek, A., Juhasz, B.J., Reichle, E.D., Machacek, D., Rayner, K.: Immediate and delayed effects of word frequency and word length on eye movements in reading: a reversed delayed effect of word length. J. Exp. Psychol. Hum. Percept. Perform. 34(3), 726–750 (2008). https://doi.org/10.1037/0096-1523.34.3.726

    Article  Google Scholar 

  • Pujol, L., Katifori, A., Vayanou, M., Roussou, M., Karvounis, M., Kyriakidi, M., Eleftheratou, S., Ioannidis, Y.: From Personalization to adaptivity: creating immersive visits through interactive digital storytelling at the acropolis museum. In: Workshop Proceedings of the 9th International Conference on Intelligent Environments, pp. 541–554 (2013). https://doi.org/10.3233/978-1-61499-286-8-541,

  • Rajaonarivo, L., Maisel, E., De Loor, P.: An evolving museum metaphor applied to cultural heritage for personalized content delivery. User Model. User-Adapt. Interact. (2019). https://doi.org/10.1007/s11257-019-09222-x

  • Raptis, G.E., Fidas, C.A., Avouris, N.M.: A Qualitative Analysis of the effect of wholistic-analytic cognitive style dimension on the cultural heritage game playing. In: Proceedings of the 7th International Conference on Information, Intelligence, Systems Applications. IEEE, IISA 2016, pp. 1–6 (2016a). https://doi.org/10.1109/IISA.2016.7785364

  • Raptis, G.E., Fidas, C.A., Avouris, N.M.: Differences of field dependent/independent gamers on cultural heritage playing: preliminary findings of an eye-tracking study. In: Ioannides, M., Fink, E., Moropoulou, A., Hagedorn-Saupe, M., Fresa, A., Liestøl, G., Rajcic, V., Grussenmeyer, P. (eds.) Digital Heritage. Progress in Cultural Heritage: Documentation, Preservation, and Protection. Springer International Publishing, Cham, pp. 199–206 (2016b). https://doi.org/10.1007/978-3-319-48974-2_22

  • Raptis, G.E., Fidas, C.A., Avouris, N.M.: Do Field dependence-independence differences of game players affect performance and behaviour in cultural heritage games? In: Proceedings of the 2016 Annual Symposium on Computer-Human Interaction in Play. ACM, New York, NY, USA, CHI PLAY ’16, pp. 38–43 (2016c). https://doi.org/10.1145/2967934.2968107

  • Raptis, G.E., Fidas, C.A., Avouris, N.M.: Using eye tracking to identify cognitive differences: a brief literature review. In: Proceedings of the 20th Pan-Hellenic Conference on Informatics. ACM, New York, NY, USA, PCI ’16, pp. 21:1–21:6 (2016d). https://doi.org/10.1145/3003733.3003762

  • Raptis, G.E., Fidas, C.A., Avouris, N.M.: Cultural heritage gaming: effects of human cognitive styles on players’ performance and visual behavior. In: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization. ACM, New York, NY, USA, UMAP ’17, pp. 343–346 (2017a). https://doi.org/10.1145/3099023.3099090

  • Raptis, G.E., Fidas, C.A., Avouris, N.M.: On Implicit elicitation of cognitive strategies using gaze transition entropies in pattern recognition tasks. In: Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems, ACM, New York, NY, USA, CHI EA ’17, pp. 1993–2000 (2017b). https://doi.org/10.1145/3027063.3053106

  • Raptis, G.E., Katsini, C., Belk, M., Fidas, C., Samaras, G., Avouris, N.: Using Eye gaze data and visual activities to infer human cognitive styles: method and feasibility studies. In: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, ACM, New York, NY, USA, UMAP ’17, pp. 164–173 (2017c). https://doi.org/10.1145/3079628.3079690. http://doi.acm.org/10.1145/3079628.3079690

  • Raptis, G.E., Fidas, C.A., Avouris, N.M.: Effects of mixed-reality on players’ behaviour and immersion in a cultural tourism game: a cognitive processing perspective. Int. J. Hum. Comput. Stud. 114, 69–79 (2018a). https://doi.org/10.1016/j.ijhcs.2018.02.003

    Article  Google Scholar 

  • Raptis, G.E., Fidas, C.A., Katsini, C., Avouris, NM.: Towards a cognition-centered personalization framework for cultural-heritage content. In: Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, New York, NY, USA, CHI EA ’18, pp. LBW011:1–LBW011:6 (2018b). https://doi.org/10.1145/3170427.3190613

  • Raptis, G.E., Katsini, C., Fidas, C.A., Avouris, N.M.: Visualization of cultural-heritage content based on individual cognitive differences. In: Proceedings of the AVI-CH 2018 Workshop on Advanced Visual Interfaces for Cultural Heritage (AVI-CH 2018), CEUR Workshop Proceedings, vol. 2091, pp. 74–81 (2018c). http://ceur-ws.org/Vol-2091/paper9.pdf

  • Raptis, G.E., Fidas, C.A., Avouris, N.M.: Do game designers’ decisions related to visual activities affect knowledge acquisition in cultural heritage games?. An evaluation from a human cognitive processing perspective. ACM J. Comput. Cult. Herit. (JOCCH) 12, 4.1–4.25 (2019)

  • Rayner, K.: Visual attention in reading: eye movements reflect cognitive processes. Mem. Cognit. 5(4), 443–448 (1977). https://doi.org/10.3758/BF03197383

    Article  Google Scholar 

  • Richardson, A.: Verbalizer-visualizer: a cognitive style dimension. J. Ment. Imag. 1(1), 109–125 (1977)

    Google Scholar 

  • Rittschof, K.A.: Field dependence-independence as visuospatial and executive functioning in working memory: implications for instructional systems design and research. Educ. Technol. Res. Dev. 58(1), 99–114 (2010). https://doi.org/10.1007/s11423-008-9093-6

    Article  Google Scholar 

  • Rodríguez-Hernández, MdC, Ilarri, S., Hermoso, R., Trillo-Lado, R.: Towards trajectory-based recommendations in museums: evaluation of strategies using mixed synthetic and real data. Procedia Comput. Sci. 113, 234–239 (2017). https://doi.org/10.1016/j.procs.2017.08.355

    Article  Google Scholar 

  • Roes, I., Stash, N., Wang, Y., Aroyo, L.: A Personalized walk through the museum: the CHIP interactive tour guide. In: CHI ’09 Extended Abstracts on Human Factors in Computing Systems. ACM, New York, NY, USA, CHI EA ’09, pp. 3317–3322 (2009). https://doi.org/10.1145/1520340.1520479

  • Rubino, I., Barberis, C., Xhembulla, J., Malnati, G.: Integrating a location-based mobile game in the museum visit: evaluating visitors’ behaviour and learning. ACM J. Comput. Cult. Herit. (JOCCH) 8(3), 15:1–15:18 (2015). https://doi.org/10.1145/2724723

    Article  Google Scholar 

  • Sansonetti, G., Gasparetti, F., Micarelli, A., Cena, F., Gena, C.: Enhancing cultural recommendations through social and linked open data. User Model. User-Adapt. Interact. (2019). https://doi.org/10.1007/s11257-019-09225-8

  • Schmiedek, F., Oberauer, K., Wilhelm, O., Süß, H.M., Wittmann, W.W.: Individual differences in components of reaction time distributions and their relations to working memory and intelligence. J. Exp. Psychol. Gen. 136(3), 414–429 (2007). https://doi.org/10.1037/0096-3445.136.3.414

    Article  Google Scholar 

  • Solé Puig, M., Puigcerver, L., Aznar-Casanova, J.A., Supèr, H.: Difference in visual processing assessed by eye vergence movements. PLoS ONE 8(9), e72041 (2013). https://doi.org/10.1371/journal.pone.0072041

    Article  Google Scholar 

  • Sosnovsky, S., Brusilovsky, P.: Evaluation of topic-based adaptation and student modeling in QuizGuide. User Model. User Adapt. Interact. 25(4), 371–424 (2015). https://doi.org/10.1007/s11257-015-9164-4

    Article  Google Scholar 

  • Steichen, B., Carenini, G., Conati, C.: User-adaptive information visualization: using eye gaze data to infer visualization tasks and user cognitive abilities. In: Proceedings of the 2013 International Conference on Intelligent User Interfaces. ACM, New York, NY, USA, IUI ’13, pp. 317–328 (2013). https://doi.org/10.1145/2449396.2449439

  • Steichen, B., Wu, M.M.A., Toker, D., Conati, C., Carenini, G.: Te, Te, Hi, Hi: Eye gaze sequence analysis for informing user-adaptive information visualizations. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, G.J. (eds.) User Modeling, Adaptation, and Personalization, pp. 183–194. Springer International Publishing, Cham (2014)

    Chapter  Google Scholar 

  • Sumi, Y., Mase, K.: Supporting awareness of shared interests and experiences in community. ACM SIGGROUP Bull. 21(3), 35–42 (2000). https://doi.org/10.1145/605647.605653

    Article  Google Scholar 

  • Sylaiou, S., Liarokapis, F., Kotsakis, K., Patias, P.: Virtual museums: a survey and some issues for consideration. J. Cult. Herit. 10(4), 520–528 (2009). https://doi.org/10.1016/j.culher.2009.03.003

    Article  Google Scholar 

  • Tanenbaum, J., Tomizu, A.: Narrative meaning creation in interactive storytelling. Int. J. Comput. Sci. 2(1), 3–20 (2008)

    Google Scholar 

  • Tanenbaum, K., Hatala, M., Tanenbaum, J., Wakkary, R., Antle, A.: A case study of intended versus actual experience of adaptivity in a tangible storytelling system. User Model. User Adapt. Interact. 24(3), 175–217 (2014). https://doi.org/10.1007/s11257-013-9140-9

    Article  Google Scholar 

  • Templeton, G.F.: A two-step approach for transforming continuous variables to normal: implications and recommendations for IS research. Commun. Assoc. Inf. Syst. 28(1), 41–58 (2011)

    Google Scholar 

  • Toker, D., Conati, C., Steichen, B., Carenini, G.: Individual user characteristics and information visualization: connecting the dots through eye tracking. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, New York, NY, USA, CHI ’13, pp. 295–304 (2013). https://doi.org/10.1145/2470654.2470696

  • Toker, D., Lallé, S., Conati, C.: Pupillometry and head distance to the screen to predict skill acquisition during information visualization tasks. In: Proceedings of the 22nd International Conference on Intelligent User Interfaces. ACM, New York, NY, USA, IUI ’17, pp. 221–231 (2017). https://doi.org/10.1145/3025171.3025187

  • Tolmie, P., Benford, S., Greenhalgh, C., Rodden, T., Reeves, S.: Supporting group interactions in museum visiting. In: Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing. ACM, New York, NY, USA, CSCW ’14, pp. 1049–1059 (2014). https://doi.org/10.1145/2531602.2531619

  • Tseng, J.C., Chu, H.C., Hwang, G.J., Tsai, C.C.: Development of an adaptive learning system with two sources of personalization information. Comput. Educ. 51(2), 776–786 (2008). https://doi.org/10.1016/j.compedu.2007.08.002

    Article  Google Scholar 

  • Tsianos, N., Germanakos, P., Lekkas, Z., Mourlas, C., Samaras, G.: Eye-tracking users’ behavior in relation to cognitive style within an E-learning environment. In: Proceedings of the Ninth IEEE International Conference on Advanced Learning Technologies. IEEE, ICALT vol. 2009, pp. 329–333 (2009). https://doi.org/10.1109/ICALT.2009.110

  • Vanderheyden, K., De Baets, S.: Does cognitive style diversity affect performance in dyadic student teams? Learn. Individ. Differ. 38, 143–150 (2015). https://doi.org/10.1016/j.lindif.2015.01.006

    Article  Google Scholar 

  • Vassilakis, C., Poulopoulos, V., Antoniou, A., Wallace, M., Lepouras, G., Nores, M.L.: exhiSTORY: Smart exhibits that tell their own stories. Future Gen. Comput. Syst. 81, 542–556 (2018). https://doi.org/10.1016/j.future.2017.10.038

    Article  Google Scholar 

  • Wang, Y., Aroyo, L.M., Stash, N., Rutledge, L.: Interactive user modeling for personalized access to museum collections: the Rijksmuseum case study. In: Conati, C., McCoy, K., Paliouras, G. (eds.) User Modeling 2007. Springer, Berlin, pp. 385–389 (2007). https://doi.org/10.1007/978-3-540-73078-1_50

  • Wang, Y., Mahmud, J., Liu, T.: Understanding cognitive styles from user-generated social media content. In: Proceedings of the Tenth International AAAI Conference on Web and Social Media (ICWSM 2016). AAAI, Cologne, Germany (2016)

  • Wedel, M., Pieters, R.: Eye fixations on advertisements and memory for brands: a model and findings. Mark. Sci. 19(4), 297–312 (2000). https://doi.org/10.1287/mksc.19.4.297.11794

    Article  Google Scholar 

  • Wilde, M., Urhahne, D.: Museum learning: a study of motivation and learning achievement. J. Biol. Educ. 42(2), 78–83 (2008). https://doi.org/10.1080/00219266.2008.9656115

    Article  Google Scholar 

  • Windhager, F., Federico, P., Mayr, E., Schreder, G., Smuc, M.: A review of information visualization approaches and interfaces to digital cultural heritage collections. In: Proceedings of the 9th Forum Media Technology 2016 and 2nd All Around Audio Symposium 2016 (FMT 2016), CEUR Workshop Proceedings, vol. 1734, pp. 74–81 (2016). http://ceur-ws.org/Vol-1734/fmt-proceedings-2016-paper9.pdf

  • Witkin, H.A., Moore, C.A., Goodenough, D.R., Cox, P.W.: Field-dependent and field-independent cognitive styles and their educational implications. ETS Res. Bull. Ser. 2, 1–64 (1975). https://doi.org/10.1002/j.2333-8504.1975.tb01065.x

    Article  Google Scholar 

  • Wojciechowski, R., Walczak, K., White, M., Cellary, W.: Building virtual and augmented reality museum exhibitions. In: Proceedings of the Ninth International Conference on 3D Web Technology. ACM, New York, NY, USA, Web3D ’04, pp. 135–144 (2004). https://doi.org/10.1145/985040.985060

  • Yelizarov, A,, Gamayunov, D.: Adaptive visualization interface that manages user’s cognitive load based on interaction characteristics. In: Proceedings of the 7th International Symposium on Visual Information Communication and Interaction. ACM, New York, NY, USA, VINCI ’14, pp. 1:1–1:8 (2014). https://doi.org/10.1145/2636240.2636844

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  1. Human-Computer Interaction (HCI) Group, Laboratory of Interactive Systems, Department of Electrical and Computer Engineering, University of Patras, Patras, Greece

    George E. Raptis, Christina Katsini & Nikolaos Avouris

  2. Department of Cultural Heritage Management and New Technologies, University of Patras, Patras, Greece

    Christos Fidas

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Raptis, G.E., Fidas, C., Katsini, C. et al. A cognition-centered personalization framework for cultural-heritage content. User Model User-Adap Inter 29, 9–65 (2019). https://doi.org/10.1007/s11257-019-09226-7

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