Ahmed Sabbir Arif
Computer Science & Engineering, University of California, Merced, Merced, California, USA
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Academic Career Length
18Years (2009-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 September 2009
Conference Paper
Ahmed Sabbir ArifWSWolfgang Stuerzlinger
Wolfgang Stuerzlinger
Researchers have proposed many text entry systems to enable users to perform this frequent task as quickly and precise as possible. Unfortunately the reported data varies widely and it is difficult to extract meaningful average entry speeds and error rates from this body of work. In this article we collect data from well-designed and well-reported experiments for the most important text entry methods, including those for handheld devices. Our survey results show that thumb keyboard is the fastest text entry method after the standard QWERTY keyboard, and that Twiddler is fastest amongst non-QWERTY methods. Moreover, we survey how text entry errors were handled in these studies. Finally, we conducted a user study to detect which effect different error-handling methodologies have on text entry performance metrics. Our study results show that the way human errors are handled has indeed a significant effect on all frequently used error metrics.
27 June 2019
Book Chapter
TDTafadzwa Joseph Dube
Tafadzwa Joseph DubeAhmed Sabbir Arif10 April 2010
Conference Paper
Ahmed Sabbir ArifWSWolfgang Stuerzlinger
Wolfgang Stuerzlinger
Researchers have developed many models to predict and understand human performance in text entry. Most of the models are specific to a technology or fail to account for human factors and variations in system parameters, and the relationship between them. Moreover, the process of fixing errors and its effects on text entry performance has not been studied. Here, we first analyze real-life text entry error correction behaviors. We then use our findings to develop a new model to predict the cost of error correction for character-based text entry technologies. We validate our model against quantities derived from the literature, as well as with a user study. Our study shows that the predicted and observed cost of error correction correspond well. At the end, we discuss potential applications of our new model.
7 May 2016
Conference Paper
Ahmed Sabbir ArifSKSunjun Kim
Sunjun Kim
WSWolfgang Stuerzlinger
Wolfgang Stuerzlinger
GLGeehyuk Lee
Geehyuk Lee
AMAli Mazalek
Ali MazalekWe present a new smart-restorable backspace technique to facilitate correction of "overlooked" errors on touchscreen-based tablets. We conducted an empirical study to compare the new backspace technique with the conventional one. Results of the study revealed that the new technique improves the overall text entry performance, both in terms of speed and operations per character, by significantly reducing error correction efforts. In addition, results showed that most users preferred the new technique to the one they use on their tablets, and found it easy to learn and use. Most of them also felt that it improved their overall text entry performance, thus wanted to keep using it.
25 November 2013
Conference Paper
Ahmed Sabbir ArifWSWolfgang Stuerzlinger
Wolfgang Stuerzlinger24 June 2013
Conference Paper
Ahmed Sabbir ArifCSCristina Sylla
Cristina SyllaIn this paper, we present results of two user studies that compared the performance of touch-based and pen-based gesture input on capacitive touchscreens for both adult and 8-11 years old child users. Results showed that inputting gestures with pen was significantly faster and more accurate than touch for adult users. However, no significant effect of input method was observed on performance for child users. Similarly, user experience evaluation showed that a large number of adult users favoured one technique over the other and/or found a technique more comfortable to use than the other, while child users were mostly neutral. This trend, however, was not statistically significant.
About Me
Dr. Ahmed Sabbir Arif is an Associate Professor of Computer Science & Engineering at the University of California, Merced, where he leads the Inclusive Interaction Lab. His interdisciplinary research spans computer science, cognitive science, human factors, virtual reality, accessibility, and applied artificial intelligence, with a focus on advancing accessibility through innovative interaction techniques. He has been recognized with a Hellman Fellowship (2020), an NSF CAREER Award (2023), and multiple best paper and honorable mention awards in human-computer interaction (HCI). Dr. Arif earned his PhD in Computer Science & Engineering from York University, an MSc in Computer & Mathematical Sciences from Lakehead University, and a BSc in Computer Science & Studies from Trent University. Before joining UC Merced, he was a Postdoctoral Research Fellow at Toronto Metropolitan University and York University, and a Research Intern at Microsoft Research, Redmond.
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