Thiago Donizetti dos Santos
Vagner Figueredo de Santana
ABSTRACT
The wide variety of services and data available on the internet may make people's lives easier, increasing the access to information and turning services that were complicated into more practical ones. However, the use of computers can be difficult for some people due to issues related to usability, accessibility, or for feeling afraid or anxious while using computers. When this anxiety reaches high levels, they manifest what is known as Computer Anxiety (CA). People with Computer Anxiety (PwCA) may face problems when using computers at home, at work or for study purposes, resulting in multiple forms of barriers even before the actual interaction with a computer. In this context, an eye tracking field study was performed with 39 elderly participants interacting with a website aiming to identify user interface elements impacting negatively task performance and user experience for people with CA. Moreover, an initial exploratory study was performed on the feasibility of creating a classifier for identifying sessions related to people with CA. Results show that certain user interface elements (e.g., carousel and maps) might impact negatively task performance and user experience for PwCA, due to information overload and salient objects calling users' attention. Moreover, classification model using Random Forest reached accuracy of 84.8%. From the presented results, one expects that personalized systems could use classification algorithms to identify sessions from PwCA and then simplify user interfaces based on different levels of CA.
- Fazil Abdullah, Rupert Ward, and Ejaz Ahmed. 2016. Investigating the influence of the most commonly used external variables of TAM on students' Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Computers in Human Behavior 63 (2016), 75--90.Google Scholar
Digital Library
- Osvaldo P. Almeida and Shirley A. Almeida. 1999. Confiabilidade da versão brasileira da Escala de Depressão em Geriatria (GDS) versão reduzida. Arquivos de Neuro-Psiquiatria 57 (06 1999), 421 - 426. http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0004-282X1999000300013&nrm=isoGoogle Scholar
- Mustafa Baloğlu and Vildan Çevik. 2008. Multivariate effects of gender, ownership, and the frequency of use on computer anxiety among high school students. Computers in Human Behavior 24, 6 (2008), 2639--2648.Google ScholarDigital Library
- Jennifer R. Bergstrom and Andrew Schall. 2014. Eye tracking in user experience design. Elsevier.Google ScholarDigital Library
- Ângela M. B. Biaggio. 1980. Desenvolvimento da forma infantil em português do inventário de ansiedade traço-estado de Spielberger. Arquivos Brasileiros de Psicologia 32, 3 (1980), 106--118.Google Scholar
- Tanja Blascheck, Markus John, Steffen Koch, Leonard Bruder, and Thomas Ertl. 2016. Triangulating user behavior using eye movement, interaction, and think aloud data. In Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications. 175--182.Google ScholarDigital Library
- Nikos Bozionelos. 2001. Computer anxiety: relationship with computer experience and prevalence. Computers in human behavior 17, 2 (2001), 213--224.Google Scholar
- Leo Breiman. 2001. Random forests. Machine learning 45, 1 (2001), 5--32.Google ScholarDigital Library
- John Brooke et al. 1996. SUS-A quick and dirty usability scale. Usability evaluation in industry 189, 194 (1996), 4--7.Google Scholar
- Pew Research Center. 2017. Tech Adoption Climbs Among Older Adults. http://www.pewinternet.org/2017/05/17/tech-adoption-climbs-among-older-adults/Google Scholar
- Jyh-Rong Chou and Hung-Cheng Tsai. 2009. On-line learning performance and computer anxiety measure for unemployed adult novices using a grey relation entropy method. Information Processing & Management 45, 2 (2009), 200--215.Google ScholarDigital Library
- Miha Cimperman, Maja M. Brenčič, and Peter Trkman. 2016. Analyzing older users' home telehealth services acceptance behavior-applying an Extended UTAUT model. International journal of medical informatics 90 (2016), 22--31.Google ScholarCross Ref
- Deborah R. Compeau and Christopher A. Higgins. 1995. Computer self-efficacy: Development of a measure and initial test. MIS quarterly (1995), 189--211.Google Scholar
- World Wide Web Consortium et al. 2008. Web content accessibility guidelines (WCAG) 2.0. (2008).Google Scholar
- Elizabeth D. Cooper-Gaiter. 2015. Computer anxiety and computer self-efficacy of older adults. (2015).Google Scholar
- Soussan Djamasbi, Marisa Siegel, and Tom Tullis. 2010. Generation Y, web design, and eye tracking. International journal of human-computer studies 68, 5 (2010), 307--323.Google Scholar
- Thiago Donizetti dos Santos and Vagner Figueredo de Santana. 2019. A Computer Anxiety Model for Elderly Users Interacting with the Web. In Proceedings of the 16th Web For All 2019 Personalization - Personalizing the Web (W4A '19). Association for Computing Machinery, New York, NY, USA, Article 32, 10 pages. https://doi.org/10.1145/3315002.3317565Google ScholarDigital Library
- Eileen Doyle, Ioanna Stamouli, and Meriel Huggard. 2005. Computer anxiety, self-efficacy, computer experience: An investigation throughout a computer science degree. In Frontiers in Education, 2005. FIE'05. Proceedings 35th Annual Conference. IEEE, S2H-3.Google ScholarCross Ref
- Mireia Fernández-Ardèvol and Loredana Ivan. 2015. Why age is not that important? An ageing perspective on computer anxiety. In International Conference on Human Aspects of IT for the Aged Population. Springer, 189--200.Google ScholarDigital Library
- Daniela Giordano, Isaak Kavasidis, Carmelo Pino, and Concetto Spampinato. 2012. Content based recommender system by using eye gaze data. In Proceedings of the Symposium on Eye Tracking Research and Applications. 369--372.Google ScholarDigital Library
- Laura Granka, Helene Hembrooke, and Geri Gay. 2006. Location Location Location: Viewing Patterns on WWW Pages. In Proceedings of the 2006 Symposium on Eye Tracking Research & Applications (ETRA '06). Association for Computing Machinery, New York, NY, USA, 43. https://doi.org/10.1145/1117309.1117328Google ScholarDigital Library
- Saurabh Gupta. 2017. Reducing computer anxiety in self-paced technology training. Advances in Teaching and Learning Technologies (2017).Google Scholar
- Yoshiko Habuchi, Haruhiko Takeuchi, and Muneo Kitajima. 2006. The Influence of Web Browsing Experience on Web-Viewing Behavior. In Proceedings of the 2006 Symposium on Eye Tracking Research & Applications (ETRA '06). Association for Computing Machinery, New York, NY, USA, 47. https://doi.org/10.1145/1117309.1117332Google ScholarDigital Library
- Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian Witten. 2009. The WEKA data mining software: an update. ACM SIGKDD explorations newsletter 11, 1 (2009), 10--18.Google ScholarDigital Library
- Bassam Hasan and Mesbah Ahmed. 2012. A path analysis of the impact of application-specific perceptions of computer self-efficacy and anxiety on technology acceptance. In End-User Computing, Development, and Software Engineering: New Challenges. IGI Global, 354--368.Google Scholar
- Rugayah Hashim, Hashim Ahmad, and Che Abdullah. 2010. Paradox of e-learning for distance education students at UiTM Shah Alam. In Distance Learning and Education (ICDLE), 2010 4th International Conference on. IEEE, 105--107.Google ScholarCross Ref
- Robert K. Heinssen, Carol R. Glass, and Luanne A. Knight. 1987. Assessing computer anxiety: Development and validation of the computer anxiety rating scale. Computers in human behavior 3, 1 (1987), 49--59.Google Scholar
- Thomas T. Hewett, Ronald Baecker, Stuart Card, Tom Carey, Jean Gasen, Marilyn Mantei, Gary Perlman, Gary Strong, and William Verplank. 1992. ACM SIGCHI Curricula for Human-Computer Interaction. http://old.sigchi.org/cdg/index.htmlGoogle Scholar
- Maxwell K. Hsu, Stephen W. Wang, and Kevin K. Chiu. 2009. Computer attitude, statistics anxiety and self-efficacy on statistical software adoption behavior: An empirical study of online MBA learners. Computers in Human Behavior 25, 2 (2009), 412--420.Google ScholarDigital Library
- Loredana Ivan and Ioana Schiau. 2016. Experiencing Computer Anxiety Later in Life: The Role of Stereotype Threat. In International Conference on Human Aspects of IT for the Aged Population. Springer, 339--349.Google Scholar
- Robert JK Jacob and Keith S Karn. 2003. Eye tracking in human-computer interaction and usability research: Ready to deliver the promises. In The mind's eye. Elsevier, 573--605.Google ScholarCross Ref
- Ashok Jashapara and Wei-Chun Tai. 2006. Understanding the complexity of human characteristics on e-learning systems: an integrated study of dynamic individual differences on user perceptions of ease of use. Knowledge Management Research & Practice 4, 3 (2006), 227--239.Google ScholarCross Ref
- Eszter Józsa. 2010. A potential application of pupillometry in web-usability research. Periodica Polytechnica Social and Management Sciences 18, 2 (2010), 109--115.Google ScholarCross Ref
- Krzysztof Krejtz, Katarzyna Wisiecka, Izabela Krejtz, Paweł Holas, Michał Olszanowski, and Andrew T Duchowski. 2018. Dynamics of emotional facial expression recognition in individuals with social anxiety. In Proceedings of the 2018 ACM symposium on eye tracking research & applications. 1--9.Google ScholarDigital Library
- Kerrie D. Laguna and Renee L. Babcock. 2000. Computer Testing of Memory Across the Adult Life Span. Experimental Aging Research 26, 3 (2000), 229--243. https://doi.org/10.1080/036107300404877 arXiv:https://doi.org/10.1080/036107300404877 PMID: 10919068.Google ScholarCross Ref
- Clayton Lewis. 1982. Using the "thinking-aloud" method in cognitive interface design. IBM TJ Watson Research Center Yorktown Heights, NY.Google Scholar
- Brenda H. Loyd and Clarice Gressard. 1984. Reliability and factorial validity of computer attitude scales. Educational and psychological measurement 44, 2 (1984), 501--505.Google Scholar
- Ruth H. Maki, William S. Maki, Michele Patterson, and P. David Whittaker. 2000. Evaluation of a Web-based introductory psychology course: I. Learning and satisfaction in on-line versus lecture courses. Behavior Research Methods, Instruments, & Computers 32, 2 (01 Jun 2000), 230--239. https://doi.org/10.3758/BF03207788Google Scholar
- Matthew M. Maurer and Michael R. Simonson. 1984. Development and validation of a measure of computer anxiety. (1984).Google Scholar
- Ram Mehar and Avneet Kaur. 2017. Effect of Web Based Instructional Strategy on Achievement in Computer Science in Relation to Computer Anxiety. Asian Journal of Research in Social Sciences and Humanities 7, 6 (2017), 202--216.Google ScholarCross Ref
- Denise M. de Melo and Altemir J. G. Barbosa. 2015. O uso do Mini-Exame do Estado Mental em pesquisas com idosos no Brasil: uma revisão sistemática. Ciência e Saúde Coletiva 20 (12 2015), 3865 - 3876. http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1413-81232015001203865&nrm=isoGoogle Scholar
- Bamshad Mobasher. 2007. Data mining for web personalization. In The adaptive web. Springer, 90--135.Google Scholar
- Fabiano M. de Moraes, Vagner F. de Santana, and Juliana C. Braga. 2016. Supporting the selection of web content modality based on user interactions logs. In Proceedings of the 13th Web for All Conference. ACM, 40.Google Scholar
- Jakob Nielsen and Kara Pernice. 2010. Eyetracking web usability. New Riders.Google Scholar
- Zacchaeus O Omogbadegun. 2019. Technical Support: Towards Mitigating Effects of Computer Anxiety on Acceptance of E-Assessment Amongst University Students in Sub Saharan African Countries. In ICT Unbounded, Social Impact of Bright ICT Adoption: IFIP WG 8.6 International Conference on Transfer and Diffusion of IT, TDIT 2019, Accra, Ghana, June 21-22, 2019, Proceedings, Vol. 558. Springer, 48.Google Scholar
- Bing Pan, Helene A Hembrooke, Geri K Gay, Laura A Granka, Matthew K Feusner, and Jill K Newman. 2004. The determinants of web page viewing behavior: an eye-tracking study. In Proceedings of the 2004 symposium on Eye tracking research & applications. 147--154.Google ScholarDigital Library
- Saroj Parasuraman and Magid Igbaria. 1990. An examination of gender differences in the determinants of computer anxiety and attitudes toward microcomputers among managers. International Journal of Man-Machine Studies 32, 3 (1990), 327--340.Google ScholarDigital Library
- Chee W. Phang, Juliana Sutanto, Atreyi Kankanhalli, Yan Li, Bernard C. Tan, and Hock-Hai Teo. 2006. Senior citizens' acceptance of information systems: A study in the context of e-government services. IEEE Transactions on Engineering Management 53, 4 (2006), 555--569.Google ScholarCross Ref
- William G. Powers et al. 1973. The Effects of Prior Computer Exposure On Man-Machine Computer Anxiety. (1973).Google Scholar
- Annalyse C. Raub. 1981. Correlates of computer anxiety in college students. (1981).Google Scholar
- Larry D. Rosen, Deborah C. Sears, and Michelle M. Weil. 1987. Computerphobia. Behavior Research Methods 19, 2 (1987), 167--179.Google Scholar
- Vagner Figueredo de Santana, Rogerio Abreu de Paula, Juliana Jansen Ferreira, Renato Cerqueira, Dario Sergio Cersósimo, Marco Ferraz, and Joana Almeida. 2019. Different Specialties, Different Gaze Strategies: Eye Tracking Opportunities in Seismic Interpretation Context. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (CHI EA '19). Association for Computing Machinery, New York, NY, USA, 1--6. https://doi.org/10.1145/3290607.3313083Google ScholarDigital Library
- Vagner Figueredo de Santana, Juliana Jansen Ferreira, Rogério Abreu de Paula, and Renato Fontoura de Gusmão Cerqueira. 2018. An Eye Gaze Model for Seismic Interpretation Support. In Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications (ETRA '18). Association for Computing Machinery, New York, NY, USA, Article 44, 10 pages. https://doi.org/10.1145/3204493.3204554Google ScholarDigital Library
- Thiago D. dos Santos and Vagner F. de Santana. 2018. Computer Anxiety and Interaction: A Systematic Review. In Proceedings of the Internet of Accessible Things. ACM, 18.Google Scholar
- Maimunah M. Shah, Roshidi Hassan, and Roslani Embi. 2011. Computer anxiety: Research results. In Humanities, Science and Engineering (CHUSER), 2011 IEEE Colloquium on. IEEE, 386--391.Google Scholar
- Maimunah M. Shah, Roshidi Hassan, and Roslani Embi. 2012. Technology acceptance and computer anxiety. In 2012 International Conference on Innovation Management and Technology Research. 306--309. https://doi.org/10.1109/ICIMTR.2012.6236408Google ScholarCross Ref
- Nelson Silva, Tobias Schreck, Eduardo Veas, Vedran Sabol, Eva Eggeling, and Dieter W Fellner. 2018. Leveraging eye-gaze and time-series features to predict user interests and build a recommendation model for visual analysis. In Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications. 1--9.Google ScholarDigital Library
- Charles D. Spielberger, Richard L. Gorsuch, Robert E. Lushene, Peter R. Vagg, and Gerard A. Jacobs. 1983. Manual for the state-trait anxiety inventory (Palo Alto, CA, Consulting Psychologists Press). Inc (1983).Google Scholar
- Ben Steichen, Bo Fu, and Tho Nguyen. 2020. Inferring Cognitive Style from Eye Gaze Behavior During Information Visualization Usage. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization. 348--352.Google ScholarDigital Library
- Jason B. Thatcher, J. Christopher Zimmer, Michael J. Gundlach, and D. Harrison McKnight. 2008. Internal and External Dimensions of Computer Self-Efficacy: An Empirical Examination. IEEE Transactions on Engineering Management 55, 4 (Nov 2008), 628--644. https://doi.org/10.1109/TEM.2008.927825Google ScholarCross Ref
- Sandeep Vidyapu, V Vijaya Saradhi, and Samit Bhattacharya. 2018. Fixation-Indices Based Correlation between Text and Image Visual Features of Webpages. In Proceedings of the 2018 ACM Symposium on Eye Tracking Research and; Applications (ETRA '18). Association for Computing Machinery, New York, NY, USA, Article 53, 5 pages. https://doi.org/10.1145/3204493.3204566Google ScholarDigital Library
- Jeffery D. Wilfong. 2006. Computer anxiety and anger: The impact of computer use, computer experience, and self-efficacy beliefs. Computers in Human Behavior 22, 6 (2006), 1001--1011.Google ScholarCross Ref
- Jerome A. Yesavage, Terence L. Brink, Terence L. Rose, Owen Lum, Virginia Huang, Michael Adey, and Von O. Leirer. 1982. Development and validation of a geriatric depression screening scale: a preliminary report. Journal of psychiatric research 17, 1 (1982), 37--49.Google ScholarCross Ref
- Daniel Hadrian Yohandy, Djoko Budiyanto Setyohadi, and Albertus Joko Santoso. 2020. Considered Factors of Online News Based on Respondents' Eye Activity Using Eye-Tracker Analysis. Future Internet 12, 3 (2020), 57.Google ScholarCross Ref
Index Terms
- Gaze Interaction and People with Computer Anxiety: Paving the Way to User Interface Simplification
Recommendations
Computer Anxiety and Interaction: A Systematic Review
W4A '18: Proceedings of the 15th International Web for All ConferenceWith the increasing use of technology in everyday life, one would expect that the use of computers would be comfortable and straightforward for everyone. However, some people still feel intimidated when using computers. Those people experience multiple ...
Does computer anxiety reach levels which conform to DSM IV criteria for specific phobia?
Fear of technology in general and of computers in particular has been shown to be prevalent in the population. This study sets out to explore the experience of computer anxiety and is in two parts. The first part examines computer anxiety in the context ...
Computer Anxiety in Nursing: An Investigation from Turkish Nurses
The purpose of this study was to determine and analyze levels of computer anxiety in nurses at a public university hospital in Turkey. This study investigated the dimensions of computer anxiety in terms of computer literacy, self-efficacy, physical ...
Comments