In this preliminary study, we tested a small group of users on their ability to perform common text input tasks using both standard and unfamiliar input devices: the standard QWERTY keyboard, an onscreen QWERTY keyboard on a Pocket PC, a letter recognition system on a Pocket PC, and a T9 text-input system on a cellular phone. We examined user performance, accuracy, and overall preference for the four input methods, and compared these results to the values predicted by Fitts ’ Law. Our findings suggest that the cognitive effort loads for each device had a strong impact on the amount of time required by users to input text, and that Fitts’ Law methods do not accurately account for or predict values including cognitive load or skill transfer
© 2019 Association for Computing Machinery. This paper presents a large-scale dataset on mobile text...
We study the performance and user experience of two popular mainstream mobile text entry methods: Th...
| openaire: EC/H2020/637991/EU//COMPUTED | openaire: EC/H2020/717054/EU//OPTINTThis paper presents a...
Mobile computing devices are increasingly being utilized to support learning activities outside the ...
Mobile computing devices are increasingly being utilized to support learning activities outside the ...
Abstract. The input of text on mobile devices has evolved from physical keyboards to virtually displ...
Text input for mobile or handheld devices is a flourishing research area. This article begins with a...
Text entry on mobile devices (e.g. phones and PDAs) has been a research challenge since devices shra...
This work presents four in-depth empirical investigations on the performance and user experience of...
Purpose: Mobile devices are increasingly used for text entry in contexts where visual attention is f...
There has been a substantial growth in interest in mobile text entry over recent years, among both r...
Mobile phone technology is advancing rapidly. Text entry on mobile devices is essential as more and ...
Mobile phone technology is advancing rapidly. Text entry on mobile devices is essential as more and ...
The input of text on mobile devices has evolved from physical keyboards to virtually displayed touch...
This paper describes the current metrics used in text input research, considering those used for dis...
© 2019 Association for Computing Machinery. This paper presents a large-scale dataset on mobile text...
We study the performance and user experience of two popular mainstream mobile text entry methods: Th...
| openaire: EC/H2020/637991/EU//COMPUTED | openaire: EC/H2020/717054/EU//OPTINTThis paper presents a...
Mobile computing devices are increasingly being utilized to support learning activities outside the ...
Mobile computing devices are increasingly being utilized to support learning activities outside the ...
Abstract. The input of text on mobile devices has evolved from physical keyboards to virtually displ...
Text input for mobile or handheld devices is a flourishing research area. This article begins with a...
Text entry on mobile devices (e.g. phones and PDAs) has been a research challenge since devices shra...
This work presents four in-depth empirical investigations on the performance and user experience of...
Purpose: Mobile devices are increasingly used for text entry in contexts where visual attention is f...
There has been a substantial growth in interest in mobile text entry over recent years, among both r...
Mobile phone technology is advancing rapidly. Text entry on mobile devices is essential as more and ...
Mobile phone technology is advancing rapidly. Text entry on mobile devices is essential as more and ...
The input of text on mobile devices has evolved from physical keyboards to virtually displayed touch...
This paper describes the current metrics used in text input research, considering those used for dis...
© 2019 Association for Computing Machinery. This paper presents a large-scale dataset on mobile text...
We study the performance and user experience of two popular mainstream mobile text entry methods: Th...
| openaire: EC/H2020/637991/EU//COMPUTED | openaire: EC/H2020/717054/EU//OPTINTThis paper presents a...