Home » Proceedings » GI 2021 » Using Action-Level Metrics to Report the Performance of Multi-Step Keyboards

Using Action-Level Metrics to Report the Performance of Multi-Step Keyboards

Gulnar Rakhmetulla (University of California, Merced), Ahmed Sabbir Arif (University of California, Merced), Steven J. Castellucci (Independent Researcher, Toronto), I. Scott MacKenzie (York University), Caitlyn E. Seim (Stanford University)


Proceedings of Graphics Interface 2021:
Virtual Event,
28 – 29 May 2021, pp. 127 – 137

Abstract

Computer users commonly use multi-step text entry methods on handheld devices and as alternative input methods for accessibility. These methods are also commonly used to enter text in languages with large alphabets or complex writing systems. These methods require performing multiple actions simultaneously (chorded) or in a specific sequence (constructive) to produce input. However, evaluating these methods is difficult since traditional performance metrics were designed explicitly for non-ambiguous, uni-step methods (e.g., QWERTY). They fail to capture the actual performance of a multi-step method and do not provide enough detail to aid in design improvements. We present three new action-level performance metrics: UnitER, UA, and UnitCX. They account for the error rate, accuracy, and complexity of multi-step chorded and constructive methods. They describe the multiple inputs that comprise typing a single character-action-level metrics observe actions performed to type one character, while conventional metrics look into the whole character. In a longitudinal study, we used these metrics to identify probable causes of slower text entry and input errors with two existing multi-step methods. Consequently, we suggest design changes to improve learnability and input execution.

Michael A. J. Sweeney Award

Alain Fournier Awards

Bill Buxton Awards

CHCCS Service Awards

CHCCS Achievement Awards

Canadian Digital Media Pioneer Awards

Connect with us

Prix Pionnier des médias numériques

Early Career Researcher Award

primary_navigation_menu