Skip to main content

Advertisement

SpringerLink
Log in

Gaze analysis of user characteristics in magazine style narrative visualizations

  • Published:
User Modeling and User-Adapted Interaction Aims and scope Submit manuscript

Abstract

Previous research has shown that various user characteristics (e.g., cognitive abilities, personality traits, and learning abilities) can influence user experience during information visualization tasks. These findings have prompted researchers to investigate user-adaptive information visualizations that can help users by providing personalized support based on their specific needs. Whereas existing work has been mostly limited to tasks involving just visualizations, the aim of our research is to broaden this work to include scenarios where users process textual documents with embedded visualizations, i.e., Magazine Style Narrative Visualizations, or MSNVs for short. In this paper, we analyze eye tracking data collected from a user study with MSNVs to uncover processing behaviors that are negatively impacting user experience (i.e., time on task) for users with low abilities in these user characteristics. Our analysis leverages Linear Mixed-Effects Models to evaluate the relationships among user characteristics, gaze processing behaviors, and task performance. Our results identify several MSNV processing behaviors within the visualization that contribute to poor task performance for users with low reading proficiency. For instance, we identify that users with low reading proficiency transition significantly more often compared to their counterparts between relevant and non-relevant bars, and transition more often from bars to the labels. We present our findings as a step toward designing user-adaptive support mechanisms to alleviate these difficulties with MSNVs, and provide suggestions on how our results can be leveraged for creating a set of meaningful interventions for future evaluation (e.g., dynamically highlighting relevant bars and labels in the visualization to help users with low reading proficiency locate them more effectively).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Source: The Economist—Dec 22, 2012

Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. Definitions of the user characteristics discussed in this sub-section are provided in Table 3 (Sect. 3.4).

  2. Using the guidelines from Field (2003) that r = 0.10 is a small correlation, r = 0.30 is medium, and r = 0.50 is large, we computed the Kendall’s τ equivalent according to Walker (2003), yielding: τ = 0.06 small association, τ = 0.19 medium, and τ = 0.33 large.

  3. The current analysis overrides the analysis described in Toker et al. (2018) that contained a statistical flaw.

References

  • Akaike, H.: A new look at the statistical model identification. IEEE Trans. Autom. Control 19(6), 716–723 (1974)

    MathSciNet  MATH  Google Scholar 

  • Allen, B.: Individual differences and the conundrums of user-centered design: two experiments. J. Am. Soc. Inf. Sci. 51(6), 508–520 (2000)

    Google Scholar 

  • Baddeley, A.: Oxford Psychology Series, No. 11. Working Memory. Clarendon Press/Oxford University Press, New York (1986)

    Google Scholar 

  • Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate—a practical and powerful approach to multiple testing. J. R. Stat. Soc. 57(1), 289–300 (1995)

    MathSciNet  MATH  Google Scholar 

  • Blair, J.R., Spreen, O.: Predicting premorbid IQ: a revision of the national adult reading test. Clin. Neuropsychol. 3(2), 129–136 (1989)

    Google Scholar 

  • Boy, J., Rensink, R.A., Bertini, E., et al.: A principled way of assessing visualization literacy. IEEE Trans. Vis. Comput. Graph. 20(12), 1963–1972 (2014)

    Google Scholar 

  • Cacioppo, J.T., Petty, R.E., Kao, C.F.: The efficient assessment of need for cognition. J. Pers. Assess. 48(3), 306–307 (1984)

    Google Scholar 

  • Carenini, G., Conati, C., Hoque, E., et al.: User task adaptation in multimedia presentations. In: Proceedings of the 1st International Workshop on User-Adaptive Information Visualization (WUAV 2013), in Conjunction with the 21st Conference on User Modeling, Adaptation and Personalization (UMAP 2013) (2013)

  • Carenini, G., Conati, C., Hoque, E., et al.: Highlighting interventions and user differences: informing adaptive information visualization support. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, pp. 1835–1844 (2014)

  • Çöltekin, A., Fabrikant, S.I., Lacayo, M.: Exploring the efficiency of users’ visual analytics strategies based on sequence analysis of eye movement recordings. Int. J. Geogr. Inf. Sci. 24(10), 1559–1575 (2010)

    Google Scholar 

  • Conati, C., Maclaren, H.: Exploring the role of individual differences in information visualization. In: Proceedings of the Working Conference on Advanced visual Interfaces, New York, NY, USA. ACM, pp. 199–206 (2008)

  • Conati, C., Carenini, G., Hoque, E., et al.: Evaluating the impact of user characteristics and different layouts on an interactive visualization for decision making. In: Computer Graphics Forum, Wiley Online Library, pp. 371–380 (2014)

  • Conati, C., Lallé, S., Rahman, M.A., et al.: Further results on predicting cognitive abilities for adaptive visualizations. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, Melbourne, Australia. AAAI Press (2017)

  • D’Mello, S., Olney, A., Williams, C., et al.: Gaze tutor: a gaze-reactive intelligent tutoring system. Int. J. Hum. Comput. Stud. 70(5), 377–398 (2012)

    Google Scholar 

  • D’Mello, S., Mills, C., Bixler, R., et al.: Zone out no more: mitigating mind wandering during computerized reading. In: Proceedings of the 10th International Conference on Educational Data Mining, Wuhan, China, pp. 8–15 (2017)

  • Dyson, M.C., Haselgrove, M.: The influence of reading speed and line length on the effectiveness of reading from screen. Int. J. Hum. Comput. Stud. 54(4), 585–612 (2001)

    Google Scholar 

  • Ekstrom, R.B., French, J.W., Harman, H.H., et al.: Manual for Kit of Factor Referenced Cognitive Tests. Educational Testing Service, Princeton (1976)

    Google Scholar 

  • Field, A.P.: How to Design and Report Experiments. Sage, London (2003)

    Google Scholar 

  • Folker, S., Ritter, H., Sichelschmidt, L.: Processing and integrating multimodal material—the influence of color-coding. In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 27(27), pp. 690–695 (2005)

  • Gingerich, M.J., Conati, C.: Constructing models of user and task characteristics from eye gaze data for user-adaptive information highlighting. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)

  • Gotz, D., Wen, Z.: Behavior-driven visualization recommendation. In: Proceedings of the 14th International Conference on Intelligent User Interfaces, IUI’09, New York, NY, USA. ACM, pp. 315–324 (2009)

  • Grabe, W., Jiang, X.: Assessing reading. In: Kunnan, A.J. (ed.) The Companion to Language Assessment, pp. 185–200. Wiley, Hoboken (2013)

    Google Scholar 

  • Grawemeyer, B.: Evaluation of ERST: an external representation selection tutor. In: Proceedings of the 4th International Conference on Diagrammatic Representation and Inference, Diagrams’06. Springer, Berlin, Heidelberg, pp. 154–167 (2006)

    Google Scholar 

  • Green, T.M., Fisher, B.: Towards the personal equation of interaction: the impact of personality factors on visual analytics interface interaction. In: 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST), pp. 203–210 (2010)

  • Green, N.L., Carenini, G., Kerpedjiev, S., et al.: AutoBrief: an experimental system for the automatic generation of briefings in integrated text and information graphics. Int. J. Hum. Comput. Stud. 61(1), 32–70 (2004)

    Google Scholar 

  • Hegarty, M., Just, M.A.: Constructing mental models of machines from text and diagrams. J. Mem. Lang. 32(6), 717–742 (1993)

    Google Scholar 

  • Huang, D., Tory, M., Aseniero, B.A., et al.: Personal visualization and personal visual analytics. IEEE Trans. Vis. Comput. Graph. 21(3), 420–433 (2015)

    Google Scholar 

  • Human Factors (HF): Guidelines on the Multimodality of Icons, Symbols and Pictograms. European Telecommunications Standards Institute, Sophia Antipolis (2008)

    Google Scholar 

  • Just, M.A., Carpenter, P.A.: Eye fixations and cognitive processes. Cogn. Psychol. 8(4), 441–480 (1976)

    Google Scholar 

  • Kalyuga, S.: Managing Cognitive Load in Adaptive Multimedia Learning. Information Science Reference, Hershey (2009)

    Google Scholar 

  • Kalyuga, S., Chandler, P., Sweller, J.: Levels of expertise and instructional design. Hum. Factors 40(1), 1–17 (1998)

    Google Scholar 

  • Kalyuga, S., Law, Y.K., Lee, C.H.: Expertise reversal effect in reading Chinese texts with added causal words. Instr. Sci. 41(3), 481–497 (2013)

    Google Scholar 

  • Kong, N., Hearst, M.A., Agrawala, M.: Extracting references between text and charts via crowdsourcing. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Toronto, Canada. ACM, pp. 31–40 (2014)

  • Kules, B., Capra, R.: Influence of training and stage of search on gaze behavior in a library catalog faceted search interface. J. Am. Soc. Inform. Sci. Technol. 63(1), 114–138 (2012)

    Google Scholar 

  • Kuznetsova, A., Brockhoff, P.B., Christensen, R.H.B.: lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82(13) (2017). http://www.jstatsoft.org/v82/i13/. Accessed 12 October 2018

  • Lallé, S., Toker, D., Conati, C., et al.: Prediction of users’ learning curves for adaptation while using an information visualization. In: Proceedings of the 20th International Conference on Intelligent User Interfaces. ACM, pp. 357–368 (2015)

  • Lallé, S., Conati, C., Carenini, G.: Predicting confusion in information visualization from eye tracking and interaction data. In: Proceedings on the 25th International Joint Conference on Artificial Intelligence, pp. 2529–2535 (2016)

  • Lallé, S., Conati, C., Carenini, G.: Impact of individual differences on user experience with a visualization interface for public engagement. In: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization, UMAP’17, New York, NY, USA. ACM, pp. 247–252 (2017)

  • Lankow, J., Ritchie, J., Crooks, R.: Infographics: The Power of Visual Storytelling. Wiley, Hoboken (2012)

    Google Scholar 

  • Loboda, T.D., Brusilovsky, P., Brunstein, J.: Inferring word relevance from eye-movements of readers. In: ACM Press, pp. 175 (2011)

  • Logie, R.H.: Visuo-spatial Working Memory. Nachdr. Psychology Press, Hove (2009)

    Google Scholar 

  • Martínez-Gómez, P., Aizawa, A.: Recognition of understanding level and language skill using measurements of reading behavior. In: ACM Press, pp. 95–104 (2014)

  • Mayer, R.E.: Multimedia Learning, 2nd edn. Cambridge University Press, Cambridge (2009)

    Google Scholar 

  • Meara, P.: EFL Vocabulary Tests, 2nd edn. Lognostics, Swansea (2010)

    Google Scholar 

  • Meara, P., Jones, G.: Eurocentres Vocabulary Size Test 10KA. Eurocentres Learning Service, Zurich (1990)

    Google Scholar 

  • Metoyer, R., Zhi, Q., Janczuk, B., et al.: Coupling story to visualization: using textual analysis as a bridge between data and interpretation. In: ACM Press, pp. 503–507 (2018)

  • Munzner, T.: Visualization Analysis and Design. CRC Press, Taylor & Francis Group (2014)

  • Nazemi, K., Retz, R., Bernard, J., et al.: Adaptive semantic visualization for bibliographic entries. In: Advances in Visual Computing, Lecture Notes in Computer Science. Springer, Berlin, Heidelberg, pp. 13–24 (2013)

    Google Scholar 

  • Olsen, A.: The tobii i-vt fixation filter. Tobii Technology (2012). http://www.tobii.com/global/analysis/training/whitepapers/tobii_whitepaper_tobiiivtfixationfilter.pdf. Accessed 13 September 2015

  • Ooms, K., De Maeyer, P., Fack, V., et al.: Interpreting maps through the eyes of expert and novice users. Int. J. Geogr. Inf. Sci. 26(10), 1773–1788 (2012)

    Google Scholar 

  • Ooms, K., De Maeyer, P., Fack, V.: Study of the attentive behavior of novice and expert map users using eye tracking. Cartogr. Geogr. Inf. Sci. 41(1), 37–54 (2014)

    Google Scholar 

  • Ottley, A., Yang, H., Chang, R.: Personality as a predictor of user strategy: how locus of control affects search strategies on tree visualizations. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, Seoul, Korea. ACM, pp. 3251–3254 (2015)

  • Ozcelik, E., Arslan-Ari, I., Cagiltay, K.: Why does signaling enhance multimedia learning? Evidence from eye movements. Comput. Hum. Behav. 26(1), 110–117 (2010)

    Google Scholar 

  • Scheiter, K., Wiebe, E., Holsanova, J.: Theoretical and Instructional Aspects of Learning with Visualizations. Instructional Design: Concepts, Methodologies, Tools and Applications. IGI Global, Hershey (2011)

    Google Scholar 

  • Segel, E., Heer, J.: Narrative visualization: telling stories with data. IEEE Trans. Vis. Comput. Graph. 16(6), 1139–1148 (2010)

    Google Scholar 

  • Steichen, B., Conati, C., Carenini, G.: Inferring visualization task properties, user performance, and user cognitive abilities from eye gaze data. ACM Trans. Interact. Intell. Syst. 4, 11 (2014)

    Google Scholar 

  • Strauss, E., Sherman, E.M.S., Spreen, O., et al.: A Compendium of Neuropsychological Tests: Administration, Norms, and Commentary, 3rd edn. Oxford University Press, Oxford (2006)

    Google Scholar 

  • Tai, R.H., Loehr, J.F., Brigham, F.J.: An exploration of the use of eye-gaze tracking to study problem-solving on standardized science assessments. Int. J. Res. Method Educ. 29(2), 185–208 (2006)

    Google Scholar 

  • Tang, H., Topczewski, J.J., Topczewski, A.M., et al.: Permutation test for groups of scanpaths using normalized Levenshtein distances and application in NMR questions. In: ACM Press, pp. 169 (2012)

  • Toker, D., Conati, C.: Eye tracking to understand user differences in visualization processing with highlighting interventions. In: Proceedings of the 22nd International Conference on User Modeling, Adaptation, and Personalization, UMAP’14. Springer, Aalborg, Denmark (2014)

    Google Scholar 

  • Toker, D., Conati, C., Carenini, G., et al.: Towards adaptive information visualization: on the influence of user characteristics. In: Proceedings of the 20th International Conference on User Modeling, Adaptation, and Personalization, UMAP’12. Springer, Berlin, Heidelberg, pp. 274–285 (2012)

    Google Scholar 

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

  • 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 Press, pp. 221–231 (2017)

  • Toker, D., Conati, C., Carenini, G.: User-adaptive support for processing magazine style narrative visualizations: identifying user characteristics that matter. In: ACM Press, pp. 199–204 (2018)

  • Tufte, E.R.: Visual Explanations: Images and Quantities, Evidence and Narrative. Graphics Press, Cheshire (1997)

    MATH  Google Scholar 

  • Turner, M.L., Engle, R.W.: Is working memory capacity task dependent? J. Mem. Lang. 28(2), 127–154 (1989)

    Google Scholar 

  • van Gog, T.: The signaling (or cueing) principle in multimedia learning. In: Mayer, Richard (ed.) The Cambridge Handbook of Multimedia Learning, pp. 263–278. Cambridge University Press, Cambridge (2014)

    Google Scholar 

  • Velez, M.C., Silver, D., Tremaine, M.: Understanding visualization through spatial ability differences. In: Proceedings of the IEEE Conference on Visualization, Minneapolis, MN, USA. IEEE, pp. 511–518 (2005)

  • Vogel, E.K., Woodman, G.F., Luck, S.J.: Storage of features, conjunctions, and objects in visual working memory. J. Exp. Psychol. Hum. Percept. Perform. 27(1), 92–114 (2001)

    Google Scholar 

  • Waddell, T.F., Auriemma, J.R., Sundar, S.S.: Make it simple, or force users to read? Paraphrased design improves comprehension of end user license agreements. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI’16. ACM Press, pp. 5252–5256 (2016)

  • Walker, D.A.: Converting Kendall’s Tau for correlational or meta-analytic analyses. J. Mod. Appl. Stat. Methods 2(2), 525–530 (2003)

    Google Scholar 

  • Wiley, J., Sanchez, C.A., Jaeger, A.J.: The individual differences in working memory capacity principle in multimedia learning. The Cambridge Handbook of Multimedia Learning, pp. 598–620. Cambridge University Press, Cambridge (2014)

    Google Scholar 

  • Wobbrock, J.O., Findlater, L., Gergle, D., et al.: The aligned rank transform for nonparametric factorial analyses using only anova procedures. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, pp. 143–146 (2011)

  • Ziemkiewicz, C., Crouser, R.J., Yauilla, A.R., et al.: How locus of control influences compatibility with visualization style. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology, Providence, RI, USA. IEEE, pp. 81–90 (2011)

Download references

Author information

Authors and Affiliations

  1. University of British Columbia, Vancouver, Canada

    Dereck Toker, Cristina Conati & Giuseppe Carenini

Authors
  1. Dereck Toker
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cristina Conati
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Giuseppe Carenini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dereck Toker.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Toker, D., Conati, C. & Carenini, G. Gaze analysis of user characteristics in magazine style narrative visualizations. User Model User-Adap Inter 29, 977–1011 (2019). https://doi.org/10.1007/s11257-019-09244-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11257-019-09244-5

Keywords

Search

Navigation