Abstract Websites for university units provide lists of faculty (teaching staff) to support a variety of users’ tasks including creating collaborations and student choice for projects and courses. However, these lists often only provide shallow features about the faculty such as pictures and names and not the semantic attributes of expertise, interest, or accomplishments. Prospective students, faculty, parents, donors, and those in the community often cannot directly access these semantic attributes and sometimes not without extensive search. Not having scholarship-focused individual entries leaves the selection process more open for implicit and explicit biases to be applied when searching for areas of expertise—if the website is face-focused (only pictures and names are provided), users can only choose (or choose who to explore further) based solely on name and physical appearance, thus including race, clothing and attractiveness. This paper argues for ease of access to the right information and self-authorship of the public-facing information. We document that this problem is pervasive at universities across the world (n = 275). We suggest good practices for decreasing the prominence of less relevant information to summarize faculty. This is accomplished by increasing the prominence and accessibility of more relevant information, including self-reported research interests and accomplishments. We provide example templates to support more semantic choices that would be applicable to similar organizational lists. This approach could be applied to other sets of professionals, such as doctors and lawyers.

There have been discussions about the value of fidelity since simulation-based training systems have been created. A primary question, which has yet to be fully answered, is what is the effect of level of simulation fidelity on learning on a target task? We present a new analysis method and use it for several analyses of a training simulation for an electronic maintenance task with two levels of fidelity: a high-fidelity simulation that basically takes as much time as the real-world task and a low-fidelity simulation with minimal delays and many actions removed or reduced in fidelity and time. The analyses are based on the Keystroke-Level Model (KLM) and the power law of learning. The analyses predict that the performance on the low-fidelity simulation initially takes between one quarter and one eighth of the time of the high, and thus starts out providing between four and eight times as many practice trials in a given time period. The low-fidelity curve has a lower intercept and a steeper slope. Learners that move from low to high appear to not be adversely affected. For a small number of practice trials, this makes a significant difference. We also explore the effect of missing subtasks in the low-fidelity simulation. This effect varies with the tasks included: If the low-fidelity simulation does not train an important task, learners can be slower when they transfer. We also analyze a simulation that we have built and are studying. These analyses demonstrate that using lower fidelity training situations helps most where there is less time to practice, and that if there is extensive time to practice full fidelity has nearly the same outcome (but perhaps not the same costs or risks). We discuss how this analysis approach can help choose the level of fidelity of future training simulations.

We face delays in a variety of situations. They are either inevitable, e.g., due to system limits, or are intentionally added, e.g., advertisements. In many situations, a visual feedback is provided during the delay to manage expectations. This feedback is usually provided through progress bars, percentages, or countdowns, depending on design limitations such as screen size. In this article, we use 15-second delays and examine (a) how delays affect users’ decision-making and task satisfaction, and (b) how to manipulate time perception to reduce the negative consequences of delays. Experiment 1 ( N =421) shows that faster countdowns increase task satisfaction and lead to more rational decisions in the subsequent task. In Experiment 2, we investigate the effect of countdown speed on delay perception and recall ( N =531). We show that faster countdowns lead to shorter perceived delays, while the delay will be recalled as longer after the task. The opposite is obtained for slower countdowns. We also increased the countdown rate and found a limit for the effect of increased speed. Thus, designers have to trade-off between how delays are perceived at the moment of experience and how they are recalled. We discuss the implications of these findings for user interface design.

We investigate performance change arising through learning, forgetting, and relearning. Participants learned a spreadsheet task with either keystroke-driven (keyboard, n = 30) or mouse-based menu-driven (mouse, n = 30) commands. Their performance confirmed the power law of practice. The keyboard users learned to complete the task faster than the mouse users on the last learning session (Day 4). At a 6-day retention interval, the mouse users were observed to forget more—they took more time to complete the task than the keyboard users. Of interest, the participants in the two modality groups showed no reliable differences in their forgetting under the retention of 12 and 18 days. With additional practice, the mouse group users with the 6-day retention relearned more—they reliably reduced the time to complete the task in comparison to the paired keyboard group. These results help understand why people may choose to use a mouse-driven graphical user interface rather than a keystroke-driven interface: People choosing to use a mouse-based menu-driven interface may not need to use a knowledge-in-the-head strategy but knowledge-in-the-world, and may be doing so because this strategy provides better relearning, rather than because it is faster or easier initially or because it is better for learning or forgetting. These results provide a richer explanation of why menu-driven interfaces (knowledge-in-the-world) are more ubiquitous and suggests when they can be replaced, for example, where use is infrequent but often enough that forgetting does not substantially occur. Our results provide preliminary suggestions for choosing optimal training strategies and supporting these strategies in terms of the three stages of learning and forgetting.

We report a way to build a series of GOMS-like cognitive user models representing a range of performance at different stages of learning. We use a spreadsheet task across multiple sessions as an example task; it takes about 20--30 min. to perform. The models were created in ACT-R using a compiler. The novice model has 29 rules and 1,152 declarative memory task elements (chunks)—it learns to create procedural knowledge to perform the task. The expert model has 617 rules and 614 task chunks (that it does not use) and 538 command string chunks—it gets slightly faster through limited declarative learning of the command strings and some further production compilation; there are a range of intermediate models. These models were tested against aggregate and individual human learning data, confirming the models’ predictions. This work suggests that user models can be created that learn like users while doing the task.

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 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.

Abstract We present a new method and tool for activity modelling through qualitative sequential data analysis. In particular, we address the question of constructing a symbolic abstract representation of an activity from an activity trace. We use knowledge engineering techniques to help the analyst build an ontology of the activity, that is, a set of symbols and hierarchical semantics that supports the construction of activity models. The ontology construction is pragmatic, evolutionist and driven by the analyst in accordance with their modelling goals and their research questions. Our tool helps the analyst define transformation rules to process the raw trace into abstract traces based on the ontology. The analyst visualizes the abstract traces and iteratively tests the ontology, the transformation rules and the visualization format to confirm the models of activity. With this tool and this method, we found innovative ways to represent a car‐driving activity at different levels of abstraction from activity traces collected from an instrumented vehicle. As examples, we report two new strategies of lane changing on motorways that we have found and modelled with this approach.

We propose a new diagrammatic notation system for Goals, Operators, Methods, Selection rules (GOMS), called Codein, with extensions to support the evaluation of Reality Based Interaction Styles. The proposed notation gives added power to GOMS to model and evaluate the task completion time of parallel actions during the performance of a task, something that was previously only possible using CPM-GOMS, which is far more complicated to use. Codein's evaluative power is verified through an experiment. The first condition of the experiment compares the completion time predicted by a GOMSL model, a Codein model, and the actual completion time of participants in a direct manipulation task. The second compares the completion time of participants in a Tangible User Interface task with predictions by a GOMSL model and a Codein model. Predicted task times by Codein in both conditions come close to the actual experimental results.

Producing useful and usable software often requires continuous and iterative evaluation. This paper introduces a novel usability evaluation method based on the Cognitive Dimensions of Notations framework. The target of our evaluation is Herbal a suite of tools designed to simplify agent development by providing a high-level language and maintenance-oriented development environment. The method introduced here uncovers dimensions of concern, which are used to measure the usability of Herbal and to identify areas for improvement in the design. In this article, we demonstrate how we used dimensions of concern to effectively evaluate and improve usability, and we discuss ways in which our method can be adapted, extended, and applied to improving the usability of other interactive systems.

We describe concepts to support the analysis of cell phone menu hierarchies, based on cognitive models of users and easy-to-use optimization techniques. We present an empirical study of user performance on five simple tasks of menu traversal on an example cell phone. Two of the models applied to these tasks, based on GOMS and ACT-R, give good predictions of behavior. We use the empirically supported models to create an effective evaluation and improvement process for menu hierarchies. Our work makes three main contributions: a novel and timely study of a new, very common HCI task; new versions of existing models for accurately predicting performance; and a search procedure to generate menu hierarchies that reduce traversal time, in simulation studies, by about a third.

Models of users are a way to understand and improve the usability of computer interfaces. We present here a model in ACT-R cognitive-modeling language that interacts with a publicly available driving simulation as a simple analog for robot interfaces. The model interacts with the unmodified Java interface by incorporating a novel use of bitmap parsing. The model's structure starts to describe the knowledge a human operator of a robot must have. The model also indicates some of the aspects of the task will be difficult for the operator. For example, the model's performance makes quantitative predictions about how robot speed will influence navigation quality, correlating well to human performance. While the model does not cover all aspects of human-robot interaction, it illustrates how providing user models access to an interface through its bitmap can lead to more accurate and more widely applicable model users.

Cell phone interfaces are now ubiquitous. In this paper, we describe concepts to support the analysis of cell phone menu hierarchies. We present an empirical study of user performance on five simple tasks of menu traversal on a cell phone. Two models we tested, based on GOMS and ACT-R, give very good predictions of behavior. We use the study results to motivate an effective evaluation process for menu hierarchies. Our work makes several contributions: a novel and timely study of a new, very common HCI task; new models for accurately predicting performance; novel development tools to support such modeling; and a search procedure to generate menu hierarchies that reduce traversal time, in simulation studies, by about a third.

Cognitive models are computer programs that simulate human performance of cognitive skills. They have been useful to HCI by predicting task times, by assisting users, and by acting as surrogate users. If cognitive models could interact with the same interfaces that users do, the models would be easier to develop and would be easier to apply as interface testers. This approach can be encapsulated as a cognitive model interface management system (CMIMS), which is analogous to and based on a user interface management system (UIMS). We present five case studies using three different UIMSes. These show how models can interact with interfaces using an interaction mechanism that is designed to apply to all interfaces generated within a UIMS. These interaction mechanisms start to support and constrain performance in the same ways that human performance is supported and constrained by interaction. Most existing UIMSes can and should be extended to create CMIMSes, and models can and should use CMIMSes to look at larger and more complex tasks. CMIMSes will help to further exploit the synergy between the disciplines of cognitive modeling and HCI by supporting cognitive models as users.

A useful approach towards improving interface design is to incorporate known HCI theory in design tools. As a step toward this, we have created a tool incorporating several known psychological results (e.g., alias generation rules and the keystroke model). The tool, simple additions to a spreadsheet developed for psychology, helps create theoretically motivated aliases for command line interfaces, and could be further extended to other interface types. It was used to semi-automatically generate a set of aliases for the interface to a cognitive modelling system. These aliases reduce typing time by approximately 50%. Command frequency data, necessary for computing time savings and useful for arbitrating alias clashes, can be difficult to obtain. We found that expert users can quickly provide useful and reasonably consistent estimates, and that the time savings predictions were robust across their predictions and when compared with a uniform command frequency distribution.