Nonparametric data from multi-factor experiments arise often in human-computer interaction (HCI). Examples may include error counts, Likert responses, and preference tallies. But because multiple factors are involved, common nonparametric tests (e.g., Friedman) are inadequate, as they are unable to examine interaction effects. While some statistical techniques exist to handle such data, these techniques are not widely available and are complex. To address these concerns, we present the Aligned Rank Transform (ART) for nonparametric factorial data analysis in HCI. The ART relies on a preprocessing step that "aligns" data before applying averaged ranks, after which point common ANOVA procedures can be used, making the ART accessible to anyone familiar with the F-test. Unlike most articles on the ART, which only address two factors, we generalize the ART to N factors. We also provide ARTool and ARTweb, desktop and Web-based programs for aligning and ranking data. Our re-examination of some published HCI results exhibits advantages of the ART.

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The aligned rank transform for nonparametric factorial analyses using only anova procedures

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

Toolglass™ widgets are new user interface tools that can appear, as though on a transparent sheet of glass, between an application and a traditional cursor. They can be positioned with one hand while the other positions the cursor. The widgets provide a rich and concise vocabulary for operating on application objects. These widgets may incorporate visual filters, called Magic Lens™ filters, that modify the presentation of application objects to reveal hidden information, to enhance data of interest, or to suppress distracting information. Together, these tools form a see-through interface that offers many advantages over traditional controls. They provide a new style of interaction that better exploits the user’ s everyday skills. They can reduce steps, cursor motion, and errors. Many widgets can be provided in a user inter face, by designers and by users, without requiring dedicated screen space. In addition, lenses provide rich context -dependent feedback and the ability to view details and context simultaneous ly. Our widgets and lenses can be combined to form operation and viewing macros, and can be used over multiple applications. CR Categories and Subject Descriptors: I.3.6 [Computer Graphics]: Methodology and Techniques- interaction techniques; H.5.2 [Information Interfaces and Presentation]: User Interfaces- interaction styles; I.3.3 [ Computer Graphics]: Picture/Image Generation- viewing algorithms; I.3.4 [Computer Graphics]: Graphics Utilities - graphics editors

http://pages.cpsc.ucalgary.ca/~saul/581/exer.eps/1toolglassp445-bier.pdf

Toolglass and magic lenses: the see-through interface

According to Fitts' law, human movement can be modeled by analogy to the transmission of information. Fitts' popular model has been widely adopted in numerous research areas, including kinematics, human factors, and (recently) human-computer interaction (HCI). The present study provides a historical and theoretical context for the model, including an analysis of problems that have emerged through the systematic deviation of observations from predictions. Refinements to the model are described, including a formulation for the index of task difficulty that is claimed to be more theoretically sound than Fitts' original formulation. The model's utility in predicting the time to position a cursor and select a target is explored through a review of six Fitts' law studies employing devices such as the mouse, trackball, joystick, touchpad, helmet-mounted sight, and eye tracker. An analysis of the performance measures reveals tremendous inconsistencies, making across-study comparisons difficult. Sources of experimental variation are identified to reconcile these differences.

Fitts' law as a research and design tool in human-computer interaction

How to measure usability is an important question in HCI research and user interface evaluation. We review current practice in measuring usability by categorizing and discussing usability measures from 180 studies published in core HCI journals and proceedings. The discussion distinguish several problems with the measures, including whether they actually measure usability, if they cover usability broadly, how they are reasoned about, and if they meet recommendations on how to measure usability. In many studies, the choice of and reasoning about usability measures fall short of a valid and reliable account of usability as quality-in-use of the user interface being studied. Based on the review, we discuss challenges for studies of usability and for research into how to measure usability. The challenges are to distinguish and empirically compare subjective and objective measures of usability; to focus on developing and employing measures of learning and retention; to study long-term use and usability; to extend measures of satisfaction beyond post-use questionnaires; to validate and standardize the host of subjective satisfaction questionnaires used; to study correlations between usability measures as a means for validation; and to use both micro and macro tasks and corresponding measures of usability. In conclusion, we argue that increased attention to the problems identified and challenges discussed may strengthen studies of usability and usability research.

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Current practice in measuring usability: Challenges to usability studies and research

This paper makes seven recommendations to HCI researchers wishing to construct Fitts' law models for either movement time prediction, or for the comparison of conditions in an experiment. These seven recommendations support (and in some cases supplement) the methods described in the recent ISO 9241-9 standard on the evaluation of pointing devices. In addition to improving the robustness of Fitts' law models, these recommendations (if widely employed) will improve the comparability and consistency of forthcoming publications. Arguments to support these recommendations are presented, as are concise reviews of 24 published Fitts' law models of the mouse, and 9 studies that used the new ISO standard.

/pdf/towards-a-standard-for-pointing-device-evaluation-pk77zpkpn3.pdf

Towards a standard for pointing device evaluation, perspectives on 27 years of Fitts' law research in HCI

In evaluations of text entry methods, participants enter phrases of text using a technique of interest while performance data are collected. This paper describes and publishes (via the internet) a collection of 500 phrases for such evaluations. Utility programs are also provided to compute statistical properties of the phrase set, or any other phrase set. The merits of using a pre-defined phrase set are described as are methodological considerations, such as attaining results that are generalizable and the possible addition of punctuation and other characters.

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Phrase sets for evaluating text entry techniques

Fitts' law, a one-dimensional model of human movement, is commonly applied to two-dimensional target acquisition tasks on interactive computing systems. For rectangular targets, such as words, it is demonstrated that the model can break down and yield unrealistically low (even negative!) ratings for a task's index of difficulty (ID). The Shannon formulation is shown to partially correct this problem, since ID is always ≥ 0 bits. As well, two alternative interpretations “target width” are introduced that accommodate the two-dimensional nature of tasks. Results of an experiment are presented that show a significant improvement in the model's performance using the suggested changes.

/pdf/extending-fitts-law-to-two-dimensional-tasks-1vxdgc4bt8.pdf

Extending Fitts' law to two-dimensional tasks

Text input for mobile or handheld devices is a flourishing research area. This article begins with a brief history of the emergence and impact of mobile computers and mobile communications devices. Key factors in conducting sound evaluations of new technologies for mobile text entry are presented, including methodology and experiment design. Important factors to consider are identified and elaborated, such as focus of attention, text creation versus text copy tasks, novice versus expert performance, quantitative versus qualitative measures, and the speed-accuracy trade-off. An exciting area within mobile text entry is the combined use of Fitts' law and a language corpus to model, and subsequently optimize, a text entry technique. The model is described, along with examples for a variety of soft keyboards as well as the telephone keypad. A survey of mobile text entry techniques, both in research papers and in commercial products, is presented.

/pdf/text-entry-for-mobile-computing-models-and-methods-theory-1fdqr6n4de.pdf

I. Scott MacKenzie

Papers

Fitts' law as a research and design tool in human-computer interaction

Towards a standard for pointing device evaluation, perspectives on 27 years of Fitts' law research in HCI

Phrase sets for evaluating text entry techniques

Extending Fitts' law to two-dimensional tasks

Text Entry for Mobile Computing: Models and Methods,Theory and Practice