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Predicting and Reducing the Impact of Errors in Character-Based Text Entry

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PREDICTING AND REDUCING THE IMPACT OF ERRORS IN CHARACTER-BASED TEXT ENTRY AHMED SABBIR ARIF A DISSERTATION SUBMITTED TO THE FACULTY OF GRADUATE STUDIES IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY GRADUATE PROGRAM IN COMPUTER SCIENCE AND ENGINEERING YORK UNIVERSITY TORONTO, ONTARIO APRIL 2014 © AHMED SABBIR ARIF, 2014 Abstract This dissertation focuses on the effect of errors in character-based text entry techniques. The effect of errors is targeted from theoretical, behavioral, and practical standpoints. This document starts with a review of the existing literature. It then presents results of a user study that investigated the effect of different error correction conditions on popular text entry performance metrics. Results showed that the way errors are handled has a significant effect on all frequently used error metrics. The outcomes also provided an understanding of how users notice and correct errors. Building on this, the dissertation then presents a new high-level and method-agnostic model for predicting the cost of error correction with a given text entry technique. Unlike the existing models, it accounts for both human and system factors and is general enough to be used with most character-based techniques. A user study verified the model through measuring the effects of a faulty keyboard on text entry performance. Subsequently, the work then explores the potential user adaptation to a gesture recognizer’s misrecognitions in two user studies. Results revealed that users gradually adapt to misrecognition errors by replacing the erroneous gestures with alternative ones, if available. Also, users adapt to a frequently misrecognized gesture faster if it occurs more frequently than the other error-prone gestures. Finally, this work presents a new hybrid approach to simulate pressure detection on standard touchscreens. The new approach combines the existing touch-point- and time-based methods. Results of two user studies showed that it can simulate pressure detection more reliably for at least two pressure levels: regular (~1 N) and extra (~3 N). Then, a new pressure-based text entry technique is presented that does not require tapping outside the virtual keyboard to reject an incorrect or unwanted prediction. Instead, the technique requires users to apply extra pressure for the tap on the next target key. The performance of the new technique was compared with the conventional technique in a user study. Results showed that for inputting short English phrases with 10% non-dictionary words, the new technique increases entry speed by 9% and decreases error rates by 25%. Also, most users (83%) favor the new technique over the conventional one. Together, the research presented in this dissertation gives more insight into on how errors affect text entry and also presents improved text entry methods. ii Acknowledgements I would like to express my gratitude to my supervisor, Dr. Wolfgang Stuerzlinger, for his guidance, support, and funding through his GRAND-NCE and NSERC grants. I would also like to thank the members of my dissertation committee, Dr. Parke Godfrey, Dr. Jimmy Huang, Dr. I. Scott MacKenzie, Dr. Daniel J. Wigdor, and Dr. Melody Wiseheart, for taking time out of their busy schedules to serve on my dissertation examination committee. Their valuable feedback has made this work substantially better. My sincere thanks go to the staff in the Department of Computer Science and Engineering, especially Seela Balkissoon, Susan Cameron, Ouma Jaipaul-Gill, and Ulya Yigit, for guiding me through York University’s administrative process. In addition, I would like to thank the current and former members of our research lab, especially Michelle Brown, Steven J. Castellucci, Euclides José de Mendonça Filho, Andriy Pavlovych, Dmitri Shuralyov, Robert J. Teather, Hussain Tinwala, and my closest friends, Kazi Tanzin Ahmed, Marcel Birkner, Geri Grolinger, M. Arafat Hossain, M. Selim Hossain, Nelson Moniz, Olga Mushtaler, Nassim Nasser, Tomasz R. Nykiel, Shibly Sadiq Shoeb, Humaira Siddiqa, Tareq Ahmed Siraj, and Jaisingh Solanki for being there for me. Special thanks go to NSERC, the Government of Ontario, York University, and CUPE 3093 for financial support through various funds and scholarships. Last but not least, I would like to thank my parents, M. A. Mannan and Shireen Jahan, my younger brother, Ahmed Sazzid Arif, and my loving wife, Nadia Mustafa, for their unconditional love, encouragement, and support. iii Table of Contents Abstract ........................................................................................................................................................... ii Acknowledgements ........................................................................................................................................ iii Table of Contents ............................................................................................................................................ iv List of Tables ................................................................................................................................................. xii List of Figures .............................................................................................................................................. xiii Chapter 1 Introduction .................................................................................................................................. 1 1.1 Motivation .......................................................................................................................................... 4 1.2 Contributions ...................................................................................................................................... 6 1.3 Brief Outline ....................................................................................................................................... 8 Chapter 2 Related Work ............................................................................................................................... 9 2.1 Text Entry vs. Transcription Typing .................................................................................................. 9 2.2 Text Entry Techniques ..................................................................................................................... 10 2.2.1 Standard Qwerty Keyboard ....................................................................................................... 10 2.2.2 Mini-Qwerty Keyboards ............................................................................................................ 11 2.2.3 Projection Qwerty Keyboard ..................................................................................................... 12 2.2.4 Virtual (Qwerty) Keyboard ....................................................................................................... 12 2.2.5 Standard 12-Key Mobile Keypad .............................................................................................. 17 iv 2.2.6 Chorded Keyboards and Keyers ................................................................................................ 20 2.2.7 Handwriting Recognition (HWR) ............................................................................................. 21 2.2.8 Gesture Recognition .................................................................................................................. 24 2.2.9 Pressure in Text Entry ............................................................................................................... 31 2.3 Text Entry Performance Metrics ...................................................................................................... 34 2.3.1 Entry Speed ............................................................................................................................... 35 2.3.2 Error Rate .................................................................................................................................. 36 2.4 Text Entry Performance ................................................................................................................... 38 2.4.1 Error Correction Condition ........................................................................................................ 39 2.4.2 User Expertise ........................................................................................................................... 39 2.4.3 Results ....................................................................................................................................... 40 2.5 Perceptual, Cognitive, and Physical Aspects ................................................................................... 41 2.5.1 Basic Behavioral Phenomena .................................................................................................... 41 2.5.2 Units of Text Entry .................................................................................................................... 43 2.5.3 Error Phenomena ....................................................................................................................... 44 2.5.4 Skill-Learning Phenomena ........................................................................................................ 45 2.5.5 Vision Phenomena ..................................................................................................................... 45 2.6 Error and Error Correction ..............................................................................................................
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