Task-based and Problem-based heuristics: Could they be mobilized as a Verification Mechanism for Usability Heuristics?

Usability enhancement based on usability heuristics: An examination of the underlying mechanism utilizing the psychological–refractory–period paradigm

BackgroundRecent clinical practice guidelines from major national organizations, including a joint United States Department of Veterans Affairs (VA) and Department of Defense (DoD) committee, have substantially changed recommendations for the use of the cholesterol-lowering statin medications after years of relative stability. Because statin medications are among the most commonly prescribed treatments in the United States, any change in their use may have significant implications for patients and providers alike. Prior research has shown that effective implementation interventions should be both user centered and specifically chosen to address identified barriers.ObjectiveThe objectives of this study were to identify potential determinants of provider uptake of the new statin guidelines and to use that information to tailor a coordinated and streamlined local quality improvement intervention focused on prescribing appropriate statins.MethodsWe employed user-centered design principles to guide the development and testing of a multicomponent guideline implementation intervention to improve statin prescribing. This paper describes the intervention development process whereby semistructured qualitative interviews with providers were conducted to (1) illuminate the knowledge, attitudes, and behaviors of providers and (2) elicit feedback on intervention prototypes developed to align with and support the use of the VA/DoD guidelines. Our aim was to use this information to design a local quality improvement intervention focused on statin prescribing that was tailored to the needs of primary care providers at our facility. Cabana’s Clinical Practice Guidelines Framework for Improvement and Nielsen’s Usability Heuristics were used to guide the analysis of data obtained in the intervention development process.ResultsSemistructured qualitative interviews were conducted with 15 primary care Patient Aligned Care Team professionals (13 physicians and 2 clinical pharmacists) at a single VA medical center. Findings highlight that providers were generally comfortable with the paradigm shift to risk-based guidelines but less clear on the need for the VA/DoD guidelines in specific. Providers preferred a clinical decision support tool that helped them calculate patient risk and guide their care without limiting autonomy. They were less comfortable with risk communication and performance measurement systems that do not account for shared decision making. When possible, we incorporated their recommendations into the intervention.ConclusionsBy combining qualitative methods and user-centered design principles, we could inform the design of a multicomponent guideline implementation intervention to better address the needs and preferences of providers, including clear and direct language, logical decision prompts with an option to dismiss a clinical decision support tool, and logical ordering of feedback information. Additionally, this process allowed us to identify future design considerations for quality improvement interventions.

Most software developers become preoccupied with the question of what makes good design at some point in their careers, usually after witnessing the effects of bad design first hand. At that point, we start to reflect. We go through a stage where we feel we know what good design is but can't really define it. Then we learn various design principles and rules of thumb that make it easier to judge what constitutes good design. But when these principles and rules conflict, we have to make trade-offs and decide what's most important in each situation. The blanket rule of thumb: Keep design as clear as possible. Regardless of the trade-offs, the most important thing was clarity. If a system uses a straightforward coding style - the classes and methods are well named and small enough to be clearly understood, and the system isn't littered with snarls of obscure code - then you can do just about anything. You can change the system with impunity, write tests for it, make adjustments, and add features, all with relative ease. So "clear design is good design" seemed like a reasonable rule of thumb because so much of what makes code impossible to maintain comes down to a lack of clarity. If you can understand your system, you can change it effectively.

Prior studies of individual attributes needed for data analytics (DA) roles reveal a lengthy list of requirements that make it difficult to elucidate and identify their key characteristics. In addition, researchers and practitioners use various terms such as skills, abilities, traits, dispositions, and competencies to describe these characteristics. We address this gap in the literature through a thematic analysis of multiple sources of stakeholder input and use terminology consistent with the professional bodies of the ACM and AIS. Thus, we use the word ‘skills’ to represent the ability to perform DA tasks and execute actions resulting from one’s acquired knowledge and we use the word ‘dispositions’ to represent the tendency or manner in which an individual approaches a DA task. We synthesize the skills and dispositions needed by DA professionals into six key characteristics: technical skills, Business domain skills, communication and collaboration skills, innovativeness, grit, and critical thinking, along with supporting descriptions and details. We find corroboration for our study results from DA job postings and explore the alignment of the results with studies of information systems (IS) professionals. Our study intends to provide clear and actionable guidance to academics, practitioners, and students interested in the DA profession.

Additive manufacturing (AM) can produce designs in a manner that greatly differs from the methods used in the older, more familiar technologies of traditional manufacturing (TM). As an example, AM's layer-by-layer approach to manufacturing designs can lead to the production of intricate geometries and make use of multiple materials, made possible without added manufacturing cost and time due to AM's “free complexity.” Despite this contrasting method for manufacturing designs, designers often forgo the new design considerations for AM (AM design heuristics). Instead, they rely on their familiarity with the design considerations for TM (TM design heuristics) regardless of the intended manufacturing process. For designs that are intended to be manufactured using AM, this usage of TM design considerations is wasteful as it leads to unnecessary material usage, increased manufacturing time, and can result in designs that are poorly manufactured. To remedy this problem, there is a need to intervene early in the design process to help address any concerns regarding the use of AM design heuristics. This work aims to address this opportunity through a preliminary exploration of the design heuristics that students naturally leverage when creating designs in the context of TM and AM. In this study, 117 students in an upper-level engineering design course were given an open-ended design challenge and later tasked with self-evaluating their designs for their manufacturability with TM and AM. This evaluation of the students' designs was later repeated by relevant experts, who would identify the common design heuristics that students are most likely to use in their designs. Future studies will build on these findings by cementing early-stage design support tools that emphasize the significant heuristics found herein. For example, this work found that the design heuristic “incorporating complexity” was the most significant indicator of designs most suited for AM and should therefore be highly encouraged/emphasized when guiding designers in the use of AM. In doing so, it will be possible for early-stage design support tools to maximally improve designs that are intended to be manufactured for AM.

This paper intends to explore methods of constructing a design simulator. Two methodologies, approached differently, imitate the human design processes. The first component is an algorithmic method which has a cognitive model embedded. This cognitive model hypothesizes that human design has certain design logic applied. The design rationales are based on knowledge stored in a designer_ memory. Each time a similar design task is encountered, the same design procedures will be repeated for completion. What makes the results different are the design information used and sequences of processing it. A kitchen design using procedural algorithms is developed to simulate this design aspect. The second component simulates an intuitive design approach. Intuition is defined as design by rules of thumb, or heuristic design. This study investigated how to simulate an intuitive design process. The method involves building up a set of inductive rules symbolizing cultural aspects that need to be addressed in a design. A residential foyer design is the simulation task. The driving force is the heuristics. Results in this study have shown that there are many variables to include but impossible to capture and simulate any of the design processes, which are the reasons why studies in this area are difficult.

People often rely on workspace awareness to collaborate with one another, especially in learning and problem-solving tasks. Workspace awareness is defined as an understanding of a current state of the work environment and collaborators’ activities. However, workspace awareness is difficult to maintain in virtual environments (VE), since some information from the real world is missing. Fortunately, VEs are flexible and can be designed to compensate for a lack of real world cues. This thesis aims to improve collaborative workspace awareness through enhancing the design of VEs. Our proposed approaches improve workspace awareness in three ways. The first approach examines space configurations that could improve awareness in a collaboration. The second explores different strategies for updating the visual environment so that users are aware of their collaborators’ actions. The third focuses on finding visual cues that raise the user’s awareness when collaborating with multiple users. Prior research studies suggested that users operate in workspace to manage their attention and divide their tasks when working in the real world. The space within the users’ reach is often assigned as a private space and the space out of users’ reach is assigned as a group space. Following this approach, we propose to set up a virtual space in a desktop virtual reality environment by assigning the space in front of the desktop screen as a private space and the space behind the screen as a group space. According to our user study, this setup improves collaborations between users. To gain further insight into information sharing strategies in VEs, a user study was conducted to compare different strategies for updating the VEs in complex remote problem-solving tasks. From the study, real-time updating is found to be not suitable for workspace awareness, especially when users are performing different tasks. On the other hand, an updating strategy that updates only information that is relevant to the users’ actions performs the best. Moreover, users change collaboration strategies depending on the update timing. Design guidelines are proposed from the user study as a direction for different collaborative situations. While there are many research works that allow users to work together in one-to-one collaborations, less attention has been paid to one-to-many collaborations. This is due to the lack of visual awareness cues that allow users to spectate multiple users. To alleviate this limitation, a prototype system named ObserVAR is proposed. ObserVAR utilizes Augmented Reality (AR) technology to help an instructor in spectating multiple Virtual Reality (VR) users by augmenting the instructor’s environment with virtual awareness cues generated from the VR users. In the development of ObserVAR, different awareness cues are compared in a user study to determine its suitability under various circumstances. The virtual awareness cues are then optimized further using techniques derived from graph visualization to reduce visual clutter. Finally, the knowledge gained from improving workspace awareness using different approaches is used to formulate a collaborative framework for learning and problem-solving tasks. The contributions of this thesis include an enhanced design of desktop virtual reality for collaborations; multiple improvements in workspace awareness across different aspects; the design and development of the ObserVAR system for one-to-many collaborations; and a framework for collaborative problem-solving that utilizes the proposed designs. This thesis explores various enhancements of VEs, which lead to a better performance in collaborative learning and problem-solving tasks. The VE designs in this thesis can be used as guidelines for future collaborative problem-solving systems.

Understanding data can be tricky and sometimes overwhelming. Using automation helps a data analysis team improve their methods more consistently. Specifically, automating software tasks is the best way to manage the many repetitions and repetitive jobs that come with thorough data analysis. When starting a project that involves software, automation, and working together, DevOps is the best way to go. DevOps improves all parts of the data analysis process, allowing teams to automatically handle software-related tasks in data analysis and work well with everyone involved in the project. The combination of DevOps, Data Analytics, and Data Engineering introduced the new DataOps which is used for data processing and analytics. DataOps improves communication, collaboration, and faster data pipeline

Usability is key to achieve quality in software products. The client applications with a high score in usability might impact on the power consumption when they are run in a PC. For this reason, energy savings turn to be critical in green software systems. In this paper the relationship between the usability evaluations of the GUIs and the power consumption measurements of the main components of a PC were analysed. A set of 5 web-based personal health records (PHRs) were selected as a case study. The usability assessment was performed by an expert, employing the 14 principles of design by Alan Dix as heuristics. They were scored on a Likert scale after performing a collection of common tasks in the PHRs. At the same time, an equipment to measure the energy consumption of hard disk drive, graphics card, processor, monitor and power supply was used. Spearman’s index was studied for the correlations between the usability assessments and the power consumption measurements. As a results, some weak relationships were found. A total of 5 usability heuristics were observed to may influence energy consumption when they were considered in the implementation of the PHRs. These heuristics were the following ones: consistency, task migratability, observability, recoverability and responsiveness. Based on the results, the usability principles of design cannot always be related to lower energy consumption. Future research should focus on the tradeoffs between usability and power consumption of client applications when they are used in a computer.

BackgroundMobile device–based tools to help adolescents practice skills outside of cognitive behavioral therapy (CBT) sessions for treating an anxiety disorder may lead to greater treatment gains.ObjectiveThis study aimed to develop, design, and test the acceptability, learnability, heuristics, and usability of MindClimb, a smartphone-based app for adolescents with anxiety to use between CBT sessions to plan and complete exposure activities using skills (cognitive, relaxation, exposure practice, and reward) learned in treatment.MethodsThis 3-phase study took place from August 2015 to December 2018. In phase 1, the app was designed and developed in consultation with young people and CBT therapists to identify desired functions and content. Feedback was subjected to thematic analysis using a general inductive approach. In phase 2, we conducted 2 high-fidelity testing sessions using the think-aloud approach (acceptability, learnability, usability) and 10-item System Usability Scale with 10 adolescents receiving CBT. The high-fidelity MindClimb app was evaluated by 5 app developers based on Nielsen’s usability heuristics and 5-point severity ranking scale. In phase 3, a total of 8 adolescents and 3 therapists assessed the usability of MindClimb during CBT sessions by recording the frequency of skills practice, use of MindClimb features, satisfaction with the app, and barriers and facilitators to app use during treatment.ResultsFeedback from phase 1 consultations indicated that the app should (1) be responsive to user needs and preferences, (2) be easy to use and navigate, (3) have relevant content to the practice of CBT for anxiety, and (4) be aesthetically appealing. Using this feedback as a guide, a fully functional app prototype for usability testing and heuristic evaluation was developed. In phase 2, think-aloud and usability data resulted in minor revisions to the app, including refinement of exposure activities. The average system usability score was 77 in both testing cycles, indicating acceptable usability. The heuristic evaluation by app developers identified only minor errors (eg, loading speed of app content, with a score of 1 on the severity ranking scale). In phase 3, adolescents considered app features for completing exposure (6.2/10) and relaxation (6.4/10) modestly helpful. Both adolescents (average score 11.3/15, SD 1.6) and therapists (average score 10.0/12, 2.6 SD) reported being satisfied with the app.ConclusionsThe user-centered approach to developing and testing MindClimb resulted in a mobile health app that can be used by adolescents during CBT for anxiety. Evaluation of the use of this app in a clinical practice setting demonstrated that adolescents and therapists generally felt it was helpful for CBT practice outside of therapy sessions. Implementation studies with larger youth samples are necessary to evaluate how to optimize the use of technology in clinical care and examine the impact of the app plus CBT on clinical care processes and patient outcomes.

A much publicised ‘rule of thumb’ is that errors which are transferred from one design phase to the next increase detection costs by a factor of 10. Thus a fault at the initial design phase may have cost £500 to detect, but let this reach, say, the gate array and it will cost £5,000, the printed circuit board and it will cost £50,000, and so on. Recognition of this fact, together with increasing complexity of designs, has resulted in a generation of stochastic architectural simulation tools. Typically these are used by system designers who are provided with precise analytical data enabling them to make design decisions based upon fact rather than intuition. The ever increasing demand of quality and time to market pressures will, the author anticipates, increase the interest in the availability of such products.

The Materiality of Accounting Errors: Evidence from SEC Comment Letters and Implications for Research Proxies

Many psychology studies are statistically underpowered. In part, this may be because many researchers rely on intuition, rules of thumb, and prior practice (along with practical considerations) to determine the number of subjects to test. In Study 1, we surveyed 291 published research psychologists and found large discrepancies between their reports of their preferred amount of power and the actual power of their studies (calculated from their reported typical cell size, typical effect size, and acceptable alpha). Furthermore, in Study 2, 89% of the 214 respondents overestimated the power of specific research designs with a small expected effect size, and 95% underestimated the sample size needed to obtain .80 power for detecting a small effect. Neither researchers’ experience nor their knowledge predicted the bias in their self-reported power intuitions. Because many respondents reported that they based their sample sizes on rules of thumb or common practice in the field, we recommend that researchers conduct and report formal power analyses for their studies.

A structure and layout of printed-circuit-board (PCB) have a considerable impact on the overall system efficiency and switching performance for high-frequency applications. The current distribution of the copper traces can be significantly distorted due to eddy and proximity effects as the switching frequency increases. Also, stray inductances are the main source of destructive switching ringing and electromagnetic interference (EMI). They both are often designed to be minimized, however, it is difficult to quantify and design the effects in the complex geometry of PCB layout. PCBs for power transfer has been often designed by a rule of thumb. The practical case of the printed circuit board for a resonant-type grid-tied switching converter is studied to reduce the stray inductances and effective resistances at switching frequency of several hundreds of kHz in this paper. Quantitative studies with the exact geometry of PCB from Gerber files are conducted using three-dimensional finite element analysis (FEA) tools. The overall process is integrated and linked by scripting glue language, Python. The suggested structure and layout show considerable improvement by rearranging patterns of copper traces and properly handling high-frequency effects.

Este estudo teve como objetivo verificar se um Sistema de Apoio à Decisão (SAD) reproduz o processo de decisão humana. Para isso, tomaram-se, como base, os elementos fundamentais do método Processo Analítico Hierárquico (AHP), de Saaty (1991), que permitiu constituir cinco categorias de análise. Assim, por meio de um experimento em laboratório, foi realizada a tarefa de escolha de notebook sob duas maneiras: com e sem auxílio de um SAD, no qual foi utilizado o protocolo verbal think aloud, tendo somente a mente do indivíduo como guia da decisão. A apresentação dos resultados exibiu diferença significativa para quatro das cinco categorias de análise. Por meio dos testes de hipóteses, a única categoria que não teve diferença significativa em ambas as tarefas foi a ordem de escolha dos critérios. Com relação às demais categorias, não foi encontrada comprovação estatística para afirmar que o processo se assemelha ao processo de decisão com o auxílio de um SAD, embora o resultado final da decisão tenha sido o mesmo. Por tal razão, a hipótese deste estudo não pode ser confirmada, não sendo possível afirmar que um SAD reproduz o processo de decisão humana. Com relação ao comportamento do decisor, sobre as opções de busca de informações, pode-se dizer que o processo se assemelha em ambas as tarefas, uma vez que o decisor buscou as informações por critério.