A Model Made of Paper: Clinicians Navigate the Electronic Health Record

The electronic health record (EHR) is actually an aggregation of individual clinical documents. Medical records document not only the knowledge domains of clinical practice, but the work processes and practices that support these domains. Human-computer interaction is an important factor in EHR system success: researchers have argued that clinician readers consciously perceive the context of production, and integrate an understanding of the producer into their understanding of the data. In support, this paper reports findings of an information retrieval study using a simulated EHR containing deidentified clinical documents. Physician subjects verbally demonstrated use of a mental model of the paper medical record during their navigation of the system. Clinicians may actively apply a mental representation of their domain of practice—and actively refer to this paperbased knowledge base—when they access medical data. An understanding of the mental models that clinicians use would greatly inform our understanding of EHR systems

Learning, Performance, and Analysis Support for Complex Software Applications

We propose a three-part framework describing support tools for users of complex software applications such as enterprise resource planning and decision support systems. The model is motivated by the objectives of learning, performance, and analysis and is grounded in the theories of constructivism, pragmatism, and reflection respectively. This mapping is supported both by results of prior research and by a case study formative evaluation of a complex, cognitive support system developed for antiterrorism resource allocation. The work contributes to the field of system usability by providing an integrative framework linking established theoretical positions with empirical research on human-computer interaction

INVARIANT USER INTERFACES

In this article the term invariant user interface is introduced for a generic, stable backbone of all user interfaces, which contain a set of predefined elements and rules to build complex interactive systems. Invariant user interfaces specify fix points in using information systems. We argue that in interfaces of complex software applications such fix points are increasingly necessary. Based upon psychological assumptions and results of human computer interaction (HCI) studies, the necessity and benefits of invariance is shown, among others increased efficiency, enhanced reliability of use and decreased cost of software ownership. In this article invariance properties of state-of-the-art user interfaces are summarized, and a simple interaction model is introduced. Types and limits of invariance is defined using this model, and a set of fundamental criteria is characterized that invariant interfaces must meet

Developing an Understanding of the Nature of Accessibility and Usability Problems Blind Students Face in Web-Enhanced Instruction Environment

Our motivation is the belief that blind students cannot participate effectively in Web-enhanced instruction due to challenges in non-visual Web interactions. We want to understand nature of accessibility and usability problems they face in WEI environments. Literature informs the Web lacks accessibility and usability, but does not explain what aspects of WEI interactions present difficulties for the blind. This is necessary to improve their WEI participation. Our user-centered, task-oriented approach relies on sound understanding of blind students’ WEI experiences, supplemented by perspectives of instructors, developers and accessibility standards. We employ protocol analysis of blind participants’ verbal reports, content analysis of instructor and developer interviews, and objective accessibility evaluation. Results explain where and why difficulties occur; their character; associated interface elements; coping mechanisms and possible solutions. Findings help instructors, developers and accessibility researchers better appreciate blind students’ needs and challenges. It will help develop WEI environments that support non-visual WEI participation

Promoting conceptual change of learning sorting algorithm through the diagnosis of mental models: The effects of gender and learning styles

It has been advocated that pedagogical content knowledge as well as subject matter knowledge are important for improving classroom instructions. To develop pedagogical content knowledge, it is argued that understanding of students' mental representations of concepts is deemed necessary. Yet assessing and comparing mental model of each individual is very tedious and time consuming. This study attempted to use gender and learning styles to associate mental models in learning sorting algorithm. The Gregorc Style Delineator (GSD) was used to measure learning styles of the participants. Mental models were assessed using the Pathfinder Scaling Algorithm (PSA). Results indicated that females showed greater similarity in mental models than males and concrete learners also exhibited closer resemblance to the expert mental model than abstract learners. These suggest that gender and learning styles can be meaningfully used to associate mental models in order to provide a group-based instead of individual-based diagnosis and thus promote conceptual change in learning. © 2009 Elsevier Ltd. All rights reserved.postprin

Conceptualizing institutional repositories work: Using co-discovery to uncover mental models.

This study investigates how people construct mental models of new information systems with which they have limited experience. Six different institutional repositories were used as the experimental systems for this lab-based co-discovery experimental study. Sixty subjects (30 pairs) were asked to complete search tasks based on a simulated work situations using an institutional repository. Subsequently, subjects were instructed to visually depict how they thought the institutional repository worked and then explain this to their partner. Our findings are based on these drawings, descriptors written on drawings, and audio-recordings of explanations and conversations. The results reveal that most of the subjects constructed mental models focusing on system operations and the design of the user interface. Few highlighted the interactivity between the system and the end user or presented a global-view of the system to show how it related to other search engines or databases. We found that the codiscovery method provides a viable research design to elicit people’s mental model construction. The implications of the results for interactive information retrieval community and institutional repository community are discussed in terms of research design, search behavior, and user instruction.Institute of Library and Museum Services (IMLS)Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106418/1/Rieh_IIIX2010.pd

BlocKit:A physical kit for materializing and designing for blockchain infrastructure

Blockchain is a disruptive technology which has significantly challenged assumptions that underpin financial institutions, and has provoked innovation strategies that have the potential to change many aspects of the digital economy. However, because of its novelty and complexity, mental models of blockchain technology are difficult to acquire. Building on embodied cognition theories and material centered-design, we report an innovative approach for the design of BlocKit, a physical three-dimensional kit for materializing blockchain infrastructure and its key entities. Through an engagement with different materials such as clay, paper, or transparent containers we identified important properties of these entities and materialized them through physical artifacts. BlocKit was evaluated by 15 blockchain experts with findings indicating its value for experts’ high level of engagement in communicating about, and designing for blockchain infrastructure. Our study advances an innovative approach for the design of such kits, an initial vocabulary to talk about them, and design implications intended to inspire HCI researchers to engage in designing for infrastructures

Measuring Learnability in Human-Computer Interaction

It is well accepted that learnability is a crucial attribute of usability that should be considered in almost every software system. A good learnability leads within a short time and with minimal effort to a high level of proficiency of the user. Therefore, expensive training time of complex systems is reduced. However, there is only few consensus on how to define and evaluate learnability. In addition, gathering detailed information on learnability is quite difficult. In todays books on usability evaluation, learnability gets only few attention, research publications are spread to several other fields and the term learnability is also used in other context. The objective of this thesis is to give an structured overview of learnability and methods for evaluation and additionally assist in the evaluator’s individual choice of an appropriate method. First of all, several definitions of learnability are discussed. For a deeper understanding psychological background knowledge is provided. Afterwards, methods to asses learnability are presented. This comprises nine methods that seem particularly appropriate to measure learnability. As this methods are very diverse, a framework based on analytical hierarchy process is provided. This framework aims to classify presented methods with respect to certain criteria and assess practitioners in selecting an appropriate method to measure learnability

Supporting User Understanding and Engagement in Designing Intelligent Systems for the Home.

With advances in computing, networking and sensing technology, our everyday objects have become more automated, connected, and intelligent. This dissertation aims to inform the design and implementation of future intelligent systems and devices. To do so, this dissertation presents three studies that investigated user interaction with and experience of intelligent systems. In particular, we look at intelligent technologies that employ sensing technology and machine learning algorithm to perceive and respond to user behavior, and that support energy savings in the home. We first investigated how people understand and use an intelligent thermostat in their everyday homes to identify problems and challenges that users encounter. Subsequently, we examined the opportunities and challenges for intelligent systems that aimed to save energy, by comparing how people’s interaction changed between conventional and smart thermostats as well as how interaction with smart thermostats changed over time. These two qualitative studies led us to the third study. In the final study, we evaluated a smart thermostat that offered a new approach to the management of thermostat schedule in a field deployment, exploring effective ways to define roles for intelligent systems and their users in achieving their mutual goals of energy savings. Based on findings from these studies, this dissertation argues that supporting user understanding and user control of intelligent systems for the home is critical allowing users to intervene effectively when the system does not work as desired. In addition, sustaining user engagement with the system over time is essential for the system to obtain necessary user input and feedback that help improve the system performance and achieve user goals. Informed by findings and insights from the studies, we identify design challenges and strategies in designing end-user interaction with intelligent technologies for the home: making system behaviors intuitive and intelligible; maintaining long-term, easy user engagement over time; and balancing interplay between user control and system autonomy to better achieve their mutual goals.PhDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133318/1/rayang_1.pd

LEVERAGING DESIGN KNOWLEDGE: STRATEGIES TO IMPROVE SYSTEMS UNDERSTANDING

The engineering design process is often characterized as a step-by-step process that guides the development of new products from initial problem identification, through prototyping stages, and towards a final realized solution. Understanding and designing engineering systems involves recognizing the context of a design problem and utilizing appropriate strategies that lead to worthwhile solutions. This research first focuses on measuring functional decomposition as a design strategy by refining a functional modeling scoring rubric that can be used as an instructional tool or a general evaluation metric of function decomposition quality. This led to a curiosity about how functional modeling instruction affects students' ability to understand and represent engineering systems. A mental model elicitation instrument was developed to investigate the effects of functional decomposition on systems understanding. A study involving graduate engineering students showed that instruction on functional modeling increased the completeness of students’ mental model representations of common engineering systems attributed to a stronger framework for system communication. A follow-up study compared engineering and non-engineering undergraduates' mental models of these systems and yielded similar improvements in mental model representation. This follow-up study also revealed that technological literacy can be increased for non-engineering majors through instruction on functional modeling and product teardown activities. Next, this research investigates how different strategies during the prototyping process affects design success. Results show that a parallel prototyping strategy (as opposed to the more traditional iterative strategy) yields better design success, improved engineering design self-efficacy, and broader exploration of the solution space. Yet, students show a strong preference for an iterative prototyping strategy despite the benefits of using a parallel prototyping strategy. While the work on functional decomposition and mental models describes effective strategies for design representation and systems thinking communication, the latter prototyping study explores design strategies in a context with tangible application. Taken as a whole, this work showcases different design strategies that improve the engineering design process and facilitate the development of innovative engineering solutions.Ph.D