Novel text entry and mobile interaction techniques for Arabic language users

  • Karim Mohsen Mahmoud El Batran

Student thesis: Doctoral Thesis


Inspired by an observational study of Egyptian Agricultural Census counters, this research aims to improve mobile data entry though better form navigation and improved Arabic text entry. Four improvements were taken into consideration in sequence: (1) minimizing large forms to fit small mobile device screens and easing form navigation process, (2) optimizing Arabic keyboard layout to suit Arabic Language users, (3) introducing Gesture-based Arabic Writing Pads (GBAWPs) that fit small mobile device screens and smart watch surfaces, and (4) enhancing a quantitative prediction model to overcome the defect in modeling interactions on mobile devices. This research shows an improvement of form navigation on mobile devices. The approach is based on computerizing forms and using Panning and Zooming as a navigation technique. In order to do so, an observational study was conducted on the Egyptian Agricultural Census (EAC). However, there were considerable challenges in reducing the size of the paper forms to fit mobile devices and introducing fast navigation technique. It was concluded after computerizing the forms that using the Panning and Zooming technique scored less completion task time and workload in comparison to the tabbed navigation technique. Moreover, this research presents a new design of an Arabic keyboard layout for effective text entry on touch screen mobile phones. The approach is based on Pareto front optimization using three metrics: minimizing finger travel distance in order to maximize speed, minimizing neighboring key error ambiguities in order to maximize the quality of spell correction, and maximizing familiarity for Arabic Language users through approximate alphabetic sorting. In user studies, the new layout showed an observed improvement in typing speed in comparison to a common Arabic layout. Currently, there is an opportunity to research new optimized keyboard designs with less usage experience than QWERTY as in mainstream Western European languages. Pareto optimization can produce high quality keyboards for alphabet based languages that could be beneficial when there is less reluctance to change from QWERTY. Furthermore, this research also illustrates Gesture-based text entry as a method used for mobile devices. Its success and acceptance is critically dependent on the reliability of gesture recognition. The gesture recognition of the GBAWP is accomplished through a sequence of touched points or swipes on the screen. In order to maximize the text area field and minimize the number of keys displayed on the screen, a 12-key GBAWP interface was introduced appearing like a 12-key physical keypad phone. Considering the Arabic letters characteristics, structure, and maximizing speed, a 6-key GBAWP layout based on dot recognition was introduced. After conducting usability tests on both the 12-key and 6-key GBAWP, it was found that users could perform text entry on mobile devices using the 12-key GBAWP with an estimate of 2.9 words-per-minute on average. They also executed text entry tasks on a Sony SmartWatch 2 with an average of 3.2 words-per-minute. This could increase to an estimate of 4.5 words-per-minute on average, on the long term. While entry speeds were slow, users found it easy to use and it supports largely eyes free interaction. Gesture-based technique enables users to perform Arabic text entry on small display mobile devices and watches using both the 12-key and 6-key GBAWP. Finally, this research introduces an enhancement to KLM (Keystroke-Level Model), a quantitative prediction model predicting the user's behaviour in low-level tasks. This was acomplished by extending it with three new operators describing interactions on mobile touchscreen devices and tablets. The approach is based on Fitts' Law to identify a performance measure es
Date of Award25 Sep 2015
Original languageEnglish
Awarding Institution
  • University Of Strathclyde
SupervisorMark Dunlop (Supervisor) & John Soraghan (Supervisor)

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