CONSOLIDATED MODEL OF VISUAL AESTHETICS ATTRIBUTES FOR SENSE-BASED USER EXPERIENCE
DOI:
https://doi.org/10.11113/jt.v77.6808Keywords:
Visual aesthetics, user experience, interactive products, creative contentAbstract
The purpose of this study is to determine visual aesthetic attributes for user experience. As interactive digital media and their associated content have diversified, there are difficulties in finding universal visual aesthetic guidelines. While previous studies look into each unique user experience, there is little focusing on meta-analysis of visual aesthetics in providing user experience. Thus, by means of content analysis, this study attempts to determine visual aesthetics attributes for sense-based user experience. As a result, a consolidated model which comprises of visual aesthetics attributes and its inter-connections with regard to human senses is developed. This model offers guidance for creative industry practitioners in designing and developing aesthetic interactive digital media and creative content.Â
References
Tractinsky, N. 2013. Visual Aesthetics. [Online]. From: https://www.interaction-design.org/printerfriendly/encyclopedia/visual_aesthetics.html. [Accessed on 14 Jan 2015].
Fallman, D. 2008. The Interaction Design Research Triangle of Design Practice, Design Studies, and Design Exploration. Design Issues. 24(3): 4-18.
Lidwell, W., Holden, K., and Butler, J. 2010. Garbage in-Garbage out. In Universal Principles Of Design: 125 Ways To Enhance Usability, Influence Perception, Increase Appeal, Make Beter Design Decisions, And Teach Through Design. Rockport Publishers Inc. 112-113.
Dhar, S., Ordonez, V., and Berg, T. L. 2011. High Level Describable Attributes For Predicting Aesthetics And Interestingness. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1657-1664.
Gentle, J. E., Hardle, W., and Mori, Y. 2004. Handbook of Computational Statistics : Concepts And Methods. Edisi ke-2. Berlin: Springer.
Li, C. and Chen, T. 2009. Aesthetic Visual Quality Assessment Of Paintings. IEEE Journal on Selected Topics in Signal Processing. 3: 236-252.
Lindstrom, M. 2005. Brand Sense: How To Build Powerful Brands Through Touch, Taste, Smell, Sight & Sound. Kogan Page Publishers.
Mark Michael Smith. 2007. Sensing The Past: Seeing, Hearing, Smelling, Tasting, And Touching In History. University of California Press.
Pajusalu, M. 2012. The Evaluation of User Interface Aesthetics. Tallinn University.
Hassenzahl, M., Lindgaard, G., Platz, A., and Tractinsky, N. 2008. 08292 Abstracts Collection: The Study of Visual Aesthetics in Human-Computer Interaction. Dagstuhl Seminar. 1-12.
Schnotz, W. 2002. Commentary: Towards an Integrated View of Learning from Text and Visual Displays. Educational Psychology Review. 14(1): 101-120.
Herbert, Z. 2009. Sight, Sound, Motion: Applied Media Aesthetics. Edisi ke-6. Wadsworth Publishing Co Inc.
Cook, M. P. 2006. Visual Representations In Science Education: The Influence Of Prior Knowledge And Cognitive Load Theory On Instructional Design Principles. Science Education. 90(6): 1073-1091.
Hedegaard, S. and Simonsen, J. G. 2013. Extracting Usability And User Experience Information From Online User Reviews. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Paris. 27 April to 2 May 2013. 2089-2098.
Hsu, W. and Sosnick, M. 2009. Evaluating Interactive Music Systems : An HCI Approach. Proceedings of the International Conference on New Interfaces for Musical Expression. Pittsburgh, PA, USA. 4-6 June. 25-28.
Wanderley, M. M. and Orio, N. 2002. Evaluation of Input Devices for Musical Expression: Borrowing Tools from HCI. Computer Music Journal. 26(3): 62-76.
Rusnida, R., Abdul Razak, Y., Juliana A. AbuBakar, and Abdul Syafiq, B. 2014. The Theoretical Framework of Designing DesktopVR in Learning Environment. In The 3rd International Conference on Computer Engineering and Mathematical Sciences. Langkawi, Malaysia. 4-5 December 2014. 805-809.
Nass, C., and Lee, K. M. 2001. Does Computer-Synthesized Speech Manifest Personality? Experimental Tests Of Recognition, Similarity-Attraction, And Consistency-Attraction. Journal of Experimental Psychology: Applied. 7(3): 171-181.
Hassenzahl, M. 2004. The Interplay of Beauty, Goodness, and Usability in Interactive Products. Human-Computer Interaction. 19(4): 319-349.
Supli, A. A., and Aziz, A. A. 2014. Tag Cloud Algorithm with the Inclusion of Personality Traits. International Journal of Computer Applications. 101(3): 15-22.
Kozma, R. 2003. The Material Features Of Multiple Representations And Their Cognitive And Social Affordances For Science Understanding. Learning and Instruction. 13(2): 205-226.
Ngo, D. C. L., Samsudin, A., and Abdullah, R. 2000. Aesthetic Measures For Assessing Graphic Screens. Journal of Information Science and Engineering. 16(1): 97-116.
Katzman, N. and Nyenhuis, J. 1972. Color vs. Black- And- White Effects On Learning, Opinion, And Attention. Educational Technology Research and Development. 20(1): 16-28.
Attribute. Def 1e. Def 2e. 2015. In Dictionary.com. [Online]. From: http://dictionary.reference.com/ [Accessed on 1 Jan 2015].
Texture. 2015. In Oxford Dictionaries online. [Online]. From: http://www.oxforddictionaries.com. [Accessed on 1 Jan 2015].
Juliana, A., AbuBakar, Nur, S., Salam, A., Zulkifli, A. N., Khairie, M., and Ruslan, M. Z. 2014. The Effect of 3D Realism and Meaning Making : A Conceptual Model. Knowledge Management International Conference (KMICe). 12-15 August 2014.
Newsam, S. D. and Kamath, C. 2004. Retrieval Using Texture Features in High Resolution Multi-spectral Satellite Imagery. International Society for Optics and Photonics: Defense and Security. Orlando, Florida. 12-16 April 2004. 21-32.
Mayer, R. E. 2005. Cognitive Theory of Multimedia Learning. In The Cambridge Handbook of Multimedia Learning. Cambridge University Press. 31-48
Alm, C. O., Roth, D., and Sproat, R. 2005. Emotions from Text: Machine Learning for Text-based Emotion prediction. In Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing. Vancouver, Canada. 6-8 October 2005. 579-586.
Sáenz, L. M. and Fuchs, L. S. 2002. Examining the Reading Difficulty of Secondary Students with Learning Disabilities. In A Journal of the Hammill Institute on Disabilities: Remedial and Special Education. 23(1): 31-41.
Abadiano, H. R. 2002. Reading Expoitory Text: The Challenges of Students With Learning Disabilities. In New England Reading Association Journal. 38(2): 49-55.
Ariza, C. 2009. The Interrogator As Critic: The Questionable Relevance Of Turing Tests And Aesthetic Tests In The Evaluation Of Generative Music Systems. Computer Music Journal. 33(1): 1-23.
Graphic. 2015. In Oxford Dictionaries online. [Online]. From: http://www.oxforddictionaries.com. [Accessed on 1 Jan 2015].
Salimun, C., Purchase, H. C., Simmons, D. R., and Brewster, S. 2010. The Effect Of Aesthetically Pleasing Composition On Visual Search Performance. Proceedings of the 6th Nordic Conference on Human-Computer Interaction Extending Boundaries. Reykjavik, Iceland. 16-20 October 2010. 422-431.
Ward, T. B., Becker, A. H., Duffin Hass, S., and Vela, E. 1991. Attribute Availability And The Shape Bias In Children’s Category Generalization. Cognitive Development. 6(2): 143-167.
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