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Impact of Screen Size on Performance, Awareness, and User Satisfaction With Adaptive Graphical User Interfaces
"... Adaptive personalization, where the system adapts the interface to a user’s needs, has the potential for significant performance benefits on small screen devices. However, research on adaptive interfaces has almost exclusively focused on desktop displays. To explore how well previous findings genera ..."
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Cited by 35 (6 self)
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Adaptive personalization, where the system adapts the interface to a user’s needs, has the potential for significant performance benefits on small screen devices. However, research on adaptive interfaces has almost exclusively focused on desktop displays. To explore how well previous findings generalize to small screen devices, we conducted a study with 36 subjects to compare adaptive interfaces for small and desktop-sized screens. Results show that high accuracy adaptive menus have an even larger positive impact on performance and satisfaction when screen real estate is constrained. The drawback of the high accuracy menus, however, is that they reduce the user’s awareness of the full set of items in the interface, potentially making it more difficult for users to learn about new features. Author Keywords Adaptive interfaces, personalization, small screen devices, menu design, user study, interaction techniques. ACM Classification Keywords H.5.2 [User Interfaces]: Evaluation/methodology, interaction styles.
Distract-R: Rapid prototyping and evaluation of in-vehicle interfaces
- In Human Factors in Computing Systems: CHI 2005 Conference Proceedings
, 2005
"... As driver distraction from in-vehicle devices becomes an increasingly critical issue, researchers have aimed to establish better scientific understanding of distraction along with better engineering tools to build less distracting devices. This paper presents a new system, Distract-R, that allows de ..."
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Cited by 22 (10 self)
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As driver distraction from in-vehicle devices becomes an increasingly critical issue, researchers have aimed to establish better scientific understanding of distraction along with better engineering tools to build less distracting devices. This paper presents a new system, Distract-R, that allows designers to rapidly prototype and evaluate new in-vehicle interfaces. The core engine of the system relies on a rigorous cognitive model of driver behavior which, when integrated with models of task behavior on the prototyped interfaces, generate predictions of driver performance and distraction. Distract-R allows a designer to prototype basic interfaces, demonstrate possible tasks on these interfaces, specify relevant driver characteristics and driving scenarios, and finally simulate, visualize, and analyze the resulting behavior as generated by the cognitive model. The paper includes three modeling studies that demonstrate the system’s ability to account for various aspects of driver performance for several types of in-vehicle interfaces. More generally, Distract-R illustrates how cognitive models can be used as internal simulation engines for design tools intended for non-modelers, with the ultimate goal of helping to understand and predict user behavior in multitasking environments. Categories and Subject Descriptors: H.1.2 [Models and Principles] User/Machine Systems – Human factors;
Why it's quick to be square: modeling new and existing hierarchical menu designs
- In Proceedings of the Conference on Human Factors in Computing Systems (CHI
, 2010
"... We consider different hierarchical menu and toolbar-like interface designs from a theoretical perspective and show how a model based on visual search time, pointing time, decision time and expertise development can assist in understanding and predicting interaction performance. Three hierarchical me ..."
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Cited by 7 (0 self)
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We consider different hierarchical menu and toolbar-like interface designs from a theoretical perspective and show how a model based on visual search time, pointing time, decision time and expertise development can assist in understanding and predicting interaction performance. Three hierarchical menus designs are modelled – a traditional pull-down menu, a pie menu and a novel Square Menu with its items arranged in a grid – and the predictions are validated in an empirical study. The model correctly predicts the relative performance of the designs – both the eventual dominance of Square Menus compared to traditional and pie designs and a performance crossover as users gain experience. Our work shows the value of modelling in HCI design, provides new insights about performance with different hierarchical menu designs, and demonstrates a new high-performance menu type. Author Keywords Menus, hierarchical menus, performance models.
A design, tests, and considerations for improving keystroke and mouse loggers.
- Interacting with Computers,
, 2013
"... Abstract We start by reviewing several logging tools. We then report improvements to a keystroke logger we have developed for the Mac and PC, Recording User Input (RUI). These improvements include changes to its interface, increased accuracy, and extensions to its logging ability. RUI runs in the b ..."
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Abstract We start by reviewing several logging tools. We then report improvements to a keystroke logger we have developed for the Mac and PC, Recording User Input (RUI). These improvements include changes to its interface, increased accuracy, and extensions to its logging ability. RUI runs in the background recording user behavior with timestamps and mouse location data across all applications-thus avoiding problems associated with video logs and instrumenting individual applications. We provide a summary and comparison of tests for loggers and and present procedures for validating logger timing that quantifies timing accuracy using an external clock. We demonstrate these tests on RUI and three other applications (Morae, Camtasia, and AppMonitor). We conclude by providing some general specifications and considerations for creating, testing, evaluating, and using keystroke and mouse loggers with respect to different experimental questions and tasks. 2
Building large learning models with Herbal
- In Proceedings of ICCM-Tenth International Conference on Cognitive Modeling
, 2010
"... In this paper, we describe a high-level behavior representation language (Herbal) and report new work regarding Herbal’s ACT-R compiler. This work suggests that Herbal reduces model development time by a factor of 10 when compared to working directly in Soar, ACT-R, or Jess. We then introduce a larg ..."
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In this paper, we describe a high-level behavior representation language (Herbal) and report new work regarding Herbal’s ACT-R compiler. This work suggests that Herbal reduces model development time by a factor of 10 when compared to working directly in Soar, ACT-R, or Jess. We then introduce a large ACT-R model (541 rules) that we generated in approximately 8 hours. We fit the model to learning data. The comparison indicates that humans performing spreadsheet tasks appeared to start with some expertise. The comparison also suggests that ACT-R, when processing tasks consisting of hundreds of unique memory elements over times spans of twenty to forty minutes, may have problems accurately representing the learning rates of humans. In addition, our study indicates that the spacing between learning sessions has significant effects that may impact the modeling of memory decay in ACT-R.
Evaluation of a Contextual Assistant Interface Using Cognitive Models
"... Abstract—Cognitive models allow predicting some aspects of utility and usability of human machine interfaces, and also simulating the interaction with these interfaces. The action of predicting is based on a task analysis which analyses what a user is required to do in terms of actions and cognitive ..."
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Abstract—Cognitive models allow predicting some aspects of utility and usability of human machine interfaces, and also simulating the interaction with these interfaces. The action of predicting is based on a task analysis which analyses what a user is required to do in terms of actions and cognitive processes to achieve a task. Task analysis facilitates the understanding of the functionalities of the system to be modeled. Cognitive models are part of the analytical approaches that do not make necessarily appeal to the user during the interface development process. This paper presents a study about the evaluation of a human machine interaction (HMI) with an interface of a contextual assistant, using ACT-R and GOMS cognitive models. It shows how these techniques may be applied in HMI evaluation, design and research, emphasizing on the task analysis in one side, and on the time execution of tasks in the other side. In order to validate and support our results, an experimental study of user performance, during the interaction with the contextual assistant interface is conducted at the DOMUS laboratory. The results of our models show that both models GOMS and ACT-R give good to very good predictions of user performance at the task level as well as the object level, our results are very close to those obtained in the experimental study. Keywords—HMI, interface evaluation, cognitive modeling, user modeling, user performance. I.
Analytical model based evaluation of human machine interfaces using cognitive modeling
- International Journal of Information Technology
, 2008
"... Abstract—Cognitive models allow predicting some aspects of utility and usability of human machine interfaces (HMI), and simulating the interaction with these interfaces. The action of predicting is based on a task analysis, which investigates what a user is required to do in terms of actions and cog ..."
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Abstract—Cognitive models allow predicting some aspects of utility and usability of human machine interfaces (HMI), and simulating the interaction with these interfaces. The action of predicting is based on a task analysis, which investigates what a user is required to do in terms of actions and cognitive processes to achieve a task. Task analysis facilitates the understanding of the system’s functionalities. Cognitive models are part of the analytical approaches, that do not associate the users during the development process of the interface. This article presents a study about the evaluation of a human machine interaction with a contextual assistant’s interface using ACT-R and GOMS cognitive models. The present work shows how these techniques may be applied in the evaluation of HMI, design and research by emphasizing firstly the task analysis and secondly the time execution of the task. In order to validate and support our results, an experimental study of user performance is conducted at the DOMUS laboratory, during the interaction with the contextual assistant’s interface. The results of our models show that the GOMS and ACT-R models give good and excellent predictions respectively of users performance at the task level, as well as the object level. Therefore, the simulated results are very close to the results obtained in the experimental study. Keywords—HMI, interface evaluation, Analytical evaluation, cognitive modeling, user modeling, user performance. I.
Exploring Potential Usability Gaps when Switching Mobile Phones: An Empirical Study
"... The present study explores potential usability gaps when users switch from a familiar to an unfamiliar mobile phone interface. A within-subject experiment was performed in which nine users familiar with Sony-Ericsson T630 and nine familiar with Nokia 7250 performed tasks on both phones. On average, ..."
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Cited by 1 (0 self)
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The present study explores potential usability gaps when users switch from a familiar to an unfamiliar mobile phone interface. A within-subject experiment was performed in which nine users familiar with Sony-Ericsson T630 and nine familiar with Nokia 7250 performed tasks on both phones. On average, test subjects spent more time on finishing tasks with an unfamiliar phone than with a familiar one. For two of the four tasks, there was a significant difference in completion time between the first-time Nokia users and the first-time Sony-Ericsson users. The tasks of adding a contact to the address book and sending an SMS to a contact in the address book were performed more quickly by new Nokia users than by new Sony-Ericsson users. The subjective difficulty ranking also showed that first-time Nokia users found the new phone easier to use than first-time Sony-Ericsson users did. Hierarchical Task Analysis is used as a potential explanation, and three other theories that relate to these findings are presented: mental models, habit errors, and emotional attachment. Categories and Subject Descriptors H.5.2 [Information Interfaces and Presentation (e.g., HCI)]: User Interfaces- evaluation / methodology, interaction styles (e.g., commands, menus, forms, direct manipulation), usercentred design.
Why it’s Quick to be Square: Modelling New and Existing Hierarchical Menu Designs
, 2016
"... We consider different hierarchical menu and toolbar-like interface designs from a theoretical perspective and show how a model based on visual search time, pointing time, decision time and expertise development can assist in understanding and predicting interaction performance. Three hierarchical me ..."
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We consider different hierarchical menu and toolbar-like interface designs from a theoretical perspective and show how a model based on visual search time, pointing time, decision time and expertise development can assist in understanding and predicting interaction performance. Three hierarchical menus designs are modelled – a traditional pull-down menu, a pie menu and a novel Square Menu with its items arranged in a grid – and the predictions are validated in an empirical study. The model correctly predicts the relative performance of the designs – both the eventual dominance of Square Menus compared to traditional and pie designs and a performance crossover as users gain experience. Our work shows the value of modelling in HCI design, provides new insights about performance with different hierarchical menu designs, and demonstrates a new high-performance menu type.
Prediction of Human Performance Time to Find Objects on Multi-display Monitors using ACT-R Cognitive Architecture
"... Objective: The aim of this study was to predict human performance time in finding objects on multi-display monitors using ACT-R cognitive architecture. Background: Display monitors are one of the representative interfaces for interaction between people and the system. Nowadays, the use of multi-disp ..."
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Objective: The aim of this study was to predict human performance time in finding objects on multi-display monitors using ACT-R cognitive architecture. Background: Display monitors are one of the representative interfaces for interaction between people and the system. Nowadays, the use of multi-display monitors is increasing so that it is necessary to research about the interaction between users and the system on multi-display monitors. Method: A cognitive model using ACT-R cognitive architecture was developed for the model-based evaluation on multi-display monitors. To develop the cognitive model, first, an experiment was performed to extract the latency about the where system of ACT-R. Then, a menu selection experiment was performed to develop a human performance model to find objects on multi-display monitors. The validation of the cognitive model was also carried out between the developed ACT-R model and empirical data. Results: As a result, no significant difference on performance time was found between the model and empirical data. Conclusion: The ACT-R cognitive architecture could be extended to model human behavior in the search of objects on multi-display monitors.. Application: This model can help predicting performance time for the model-based usability evaluation in the area of
