ABSTRACT
Designing intelligent interactive text entry systems often relies on factors that are difficult to estimate or assess using traditional HCI design and evaluation methods. We introduce a complementary approach by adapting function structure models from engineering design. We extend their use by extracting controllable and uncontrollable parameters from function structure models and visualizing their impact using envelope analysis. Function structure models allow designers to understand a system in terms of its functions and flows between functions and decouple functions from function carriers. Envelope analysis allows the designer to further study how parameters affect variables of interest, for example, accuracy, keystroke savings and other dependent variables. We provide examples of function structure models and illustrate a complete envelope analysis by investigating a parameterized function structure model of predictive text entry. We discuss the implications of this design approach for both text entry system design and for critique of system contributions.
- Ohoud Alharbi, Wolfgang Stuerzlinger, and Felix Putze. 2020. The effects of predictive features of mobile keyboards on text entry speed and errors. Proceedings of the ACM on Human-Computer Interaction 4, ISS, Article 183 (2020), 16 pages. https://doi.org/10.1145/3427311Google ScholarDigital Library
- Kenneth C Arnold, Krzysztof Z. Gajos, and Adam T. Kalai. 2016. On suggesting phrases vs. predicting words for mobile text composition. In Proceedings of the 29th Annual ACM Symposium on User Interface Software and Technology. 603–608. https://doi.org/10.1145/2984511.2984584Google ScholarDigital Library
- Shiri Azenkot and Shumin Zhai. 2012. Touch behavior with different postures on soft smartphone keyboards. In Proceedings of ACM MobileHCI. 251–260. https://doi.org/10.1145/2371574.2371612Google ScholarDigital Library
- Xiaojun Bi, Yang Li, and Shumin Zhai. 2013. FFitts law: modeling finger touch with Fitts’ law. In Proceedings of the 31st ACM Conference on Human Factors in Computing Systems. 1363–1372. https://doi.org/10.1145/2470654.2466180Google ScholarDigital Library
- Andy Cockburn, Carl Gutwin, and Saul Greenberg. 2007. A predictive model of menu performance. In Proceedings of the 25th ACM Conference on Human Factors in Computing Systems. 627–636. https://doi.org/10.1145/1240624.1240723Google ScholarDigital Library
- Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. 2009. Introduction to Algorithms. MIT press.Google Scholar
- Andrew Fowler, Kurt Partridge, Ciprian Chelba, Xiaojun Bi, Tom Ouyang, and Shumin Zhai. 2015. Effects of language modeling and its personalization on touchscreen typing performance. In Proceedings of the 33rd ACM Conference on Human Factors in Computing Systems. 649–658. https://doi.org/10.1145/2702123.2702503Google ScholarDigital Library
- Nestor Garay-Vitoria and Julio Abascal. 2006. Text prediction systems: a survey. Universal Access in the Information Society 4, 3 (2006), 188–203. https://doi.org/10.1007/s10209-005-0005-9Google ScholarDigital Library
- Nestor Garay-Vitoria and Julio Abascal. 2010. Modelling text prediction systems in low- and high-inflected languages. Computer Speech and Language 24, 2 (2010), 117–135. https://doi.org/10.1016/j.csl.2009.03.008Google ScholarDigital Library
- Shaona Ghosh and Per Ola Kristensson. 2017. Neural networks for text correction and completion in keyboard decoding. arXiv:1709.06429 (2017).Google Scholar
- Saul Greenberg and Bill Buxton. 2008. Usability evaluation considered harmful (some of the time). In Proceedings of the 26th ACM Conference on Human Factors in Computing Systems. 111–120. https://doi.org/10.1145/1357054.1357074Google ScholarDigital Library
- Heidi Horstmann Koester and Simon P. Levine. 1994. Modeling the speed of text entry with a word prediction interface. IEEE Transactions on Rehabilitation Engineering 2, 3(1994), 177–187. https://doi.org/10.1109/86.331567Google ScholarCross Ref
- Heidi Horstmann Koester and Simon P Levine. 1995. Validating quantitative models of user performance with word prediction systems. In Proceedings of the 18th Annual Conference on Rehabilitation Technology. 127–129.Google Scholar
- Heidi Horstmann Koester and Simon P. Levine. 1996. Effect of a word prediction feature on user performance. Augmentative and Alternative Communication 12, 3 (1996), 155–168. https://doi.org/10.1080/07434619612331277608Google ScholarCross Ref
- Heidi Horstmann Koester and Simon P. Levine. 1998. Model simulations of user performance with word prediction. Augmentative and Alternative Communication 14, 1 (1998), 25–35. https://doi.org/10.1080/07434619812331278176Google ScholarCross Ref
- Ray Hyman. 1953. Stimulus information as a determinant of reaction time.Journal of Experimental Psychology 45, 3 (1953), 188–196. https://doi.org/10.1037/h0056940Google ScholarCross Ref
- Per Ola Kristensson. 2009. Five challenges for intelligent text entry methods. AI Magazine 30, 4 (2009), 85–94. https://doi.org/10.1609/aimag.v30i4.2269Google ScholarDigital Library
- Per Ola Kristensson, James Lilley, Rolf Black, and Annalu Waller. 2020. A design engineering approach for quantitatively exploring context-aware sentence retrieval for nonspeaking individuals with motor disabilities. In Proceedings of the 38th ACM Conference on Human Factors in Computing Systems. Paper 398. https://doi.org/10.1145/3313831.3376525Google ScholarDigital Library
- Henry Lieberman. 2003. The tyranny of evaluation. CHI Fringe (2003).Google Scholar
- Ying Liu and Kari-Jouko Räihä. 2010. Predicting Chinese text entry speeds on mobile phones. In Proceedings of the 28th ACM Conference on Human Factors in Computing Systems. 2183–2192. https://doi.org/10.1145/1753326.1753657Google ScholarDigital Library
- Dan R. Olsen Jr. 2007. Evaluating user interface systems research. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology. 251–258. https://doi.org/10.1145/1294211.1294256Google ScholarDigital Library
- Gerhard Pahl and Wolfgang Beitz. 2013. Engineering Design. Springer.Google Scholar
- Adam Pauls and Dan Klein. 2011. Faster and smaller n-gram language models. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics. 258–267.Google Scholar
- Philip Quinn and Shumin Zhai. 2016. A cost-benefit study of text entry suggestion interaction. In Proceedings of the 34th ACM Conference on Human Factors in Computing Systems. 83–88. https://doi.org/10.1145/2858036.2858305Google ScholarDigital Library
- Miika Silfverberg, I. Scott MacKenzie, and Panu Korhonen. 2000. Predicting text entry speed on mobile phones. In Proceedings of the 18th ACM Conference on Human Factors in Computing Systems. 9–16. https://doi.org/10.1145/332040.332044Google ScholarDigital Library
- William R. Soukoreff and I. Scott Mackenzie. 1995. Theoretical upper and lower bounds on typing speed using a stylus and a soft keyboard. Behaviour & Information Technology 14, 6 (1995), 370–379. https://doi.org/10.1080/01449299508914656Google ScholarCross Ref
- Keith Vertanen, Dylan Gaines, Crystal Fletcher, Alex M. Stanage, Robbie Watling, and Per Ola Kristensson. 2019. VelociWatch: designing and evaluating a virtual keyboard for the input of challenging text. In Proceedings of the 37th ACM Conference on Human Factors in Computing Systems. Paper 591. https://doi.org/10.1145/3290605.3300821Google ScholarDigital Library
- Keith Vertanen and Per Ola Kristensson. 2011. The imagination of crowds: conversational AAC language modeling using crowdsourcing and large data sources. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. 700–711.Google Scholar
- Keith Vertanen and Per Ola Kristensson. 2011. A versatile dataset for text entry evaluations based on genuine mobile emails. In Proceedings of ACM MobileHCI. 295–298. https://doi.org/10.1145/2037373.2037418Google ScholarDigital Library
- Keith Vertanen, Haythem Memmi, Justin Emge, Shyam Reyal, and Per Ola Kristensson. 2015. VelociTap: investigating fast mobile text entry using sentence-based decoding of touchscreen keyboard input. In Proceedings of the 33rd ACM Conference on Human Factors in Computing Systems. 659–668. https://doi.org/10.1145/2702123.2702135Google ScholarDigital Library
- Daryl Weir, Henning Pohl, Simon Rogers, Keith Vertanen, and Per Ola Kristensson. 2014. Uncertain text entry on mobile devices. In Proceedings of the 32nd ACM Conference on Human Factors in Computing Systems. 2307–2316. https://doi.org/10.1145/2556288.2557412Google ScholarDigital Library
Index Terms
- Design and Analysis of Intelligent Text Entry Systems with Function Structure Models and Envelope Analysis
Recommendations
Designing, Developing, and Evaluating AI-driven Text Entry Systems for Augmentative and Alternative Communication Users and Researchers
MobileHCI '23 Companion: Proceedings of the 25th International Conference on Mobile Human-Computer InteractionNon-speaking individuals with motor disabilities heavily rely on augmentative and alternative communication (AAC) text entry systems to communicate. However, designing, developing, and evaluating AAC text entry systems for users and researchers gives ...
Imperfect Surrogate Users: Understanding Performance Implications of Augmentative and Alternative Communication Systems through Bounded Rationality, Human Error, and Interruption Modeling
MHCINonspeaking individuals with motor disabilities frequently rely on augmentative and alternative communication (AAC) systems that allow users to communicate through a text entry interface coupled with a speech synthesizer. Such systems are notoriously ...
Design dimensions of intelligent text entry tutors
AIED'11: Proceedings of the 15th international conference on Artificial intelligence in educationIntelligent text entry methods use techniques from artificial intelligence to improve entry rates. While these text entry methods are useful in situations when a full-sized keyboard is impractical or unavailable, they also require substantial training ...
Comments