Focal points for a more user-centred agile development

The integration of user-centred design and Agile development is becoming increasingly common in companies and appears promising. However it may also present some critical points, or communication breakdowns, such as a variable interpretation of user involvement, a mismatch in the value of documentation and a misalignment in iterations. We refine these themes, emerging from both literature and previous fieldwork, by analysing a case study performed in an IT company that adopts both software engineering approaches, and we further extend the framework with a new theme related to task ownership. We argue that communication breakdowns can become focal points to drive action and decision for establishing an organisational context acknowledging the value of user involvement: to this end, we suggest the adoption of design thinking and the active engagement of the customer in embracing its values

Who is in the sample? An analysis of real and surrogate users as participants in user study research in the information technology fields

Background Constructing a sample of real users as participants in user studies is considered by most researchers to be vital for the validity, usefulness, and applicability of research findings. However, how often user studies reported in information technology academic literature sample real users or surrogate users is unknown. Therefore, it is uncertain whether or not the use of surrogate users in place of real users is a widespread problem within user study practice. Objective To determine how often user studies reported in peer-reviewed information technology literature sample real users or surrogate users as participants. Method We analyzed 725 user studies reported in 628 peer-reviewed articles published from 2013 through 2021 in 233 unique conference and journal outlets, retrieved from the ACM Digital Library, IEEE Xplore, and Web of Science archives. To study the sample selection choices, we categorized each study as generic (i.e., users are from the general population) or targeted (i.e., users are from a specific subpopulation), and the sampled study participants as real users (i.e., from the study population) or surrogate users (i.e., other than real users). Results Our analysis of all 725 user studies shows that roughly two-thirds (75.4%) sampled real users. However, of the targeted studies, only around half (58.4%) sampled real users. Of the targeted studies sampling surrogate users, the majority (69.7%) used students, around one-in-four (23.6%) sampled through crowdsourcing, and the remaining 6.7% of studies used researchers or did not specify who the participants were. Conclusions Key findings are as follows: (a) the state of sampling real users in information technology research has substantial room for improvement for targeted studies; (b) researchers often do not explicitly characterize their study participants in adequate detail, which is probably the most disconcerting finding; and (c) suggestions are provided for recruiting real users, which may be challenging for researchers. Implications The results imply a need for standard guidelines for reporting the types of users sampled for a user study. We provide a template for reporting user study sampling with examples.© 2022 Salminen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.fi=vertaisarvioitu|en=peerReviewed

Der Einfluss von User Interface-Attributen auf die Ästhetik

In der vorliegenden Arbeit wird eine empirische Studie mit 40 Teilnehmern präsentiert, die zum einen das Zusammenwirken von verschiedenen Definitionen der User Interface-Ästhetik und zum anderen den Einfluss von Symmetrie, Buntheit und visueller Komplexität auf die UI-Ästhetik untersucht. Die UI-Ästhetik wird dabei in intuitive (erster Eindruck) und reflektive (überlegte) Ästhetik unterteilt. Die reflektive Ästhetik wiederum gliedert sich in klassische Ästhetik (Attraktivität) und expressive Ästhetik (Kreativität). Als Untersuchungsgegenstand wird ein Korpus aus Webseiten erstellt. Es kann gezeigt werden, dass die intuitive ästhetische Beurteilung stark mit der reflektiven korreliert. Symmetrie korreliert positiv und visuelle Komplexität negativ mit allen drei Definitionen. Für Buntheit ergeben sich differenzierte Ergebnisse. Abschließend werden Implikationen für das User Interface-Design diskutiert

Good GUIs, Bad GUIs: Affective Evaluation of Graphical User Interfaces

peer reviewedAffective computing has potential to enrich the development lifecycle of Graphical User Interfaces (GUIs) and of intelligent user interfaces by incorporating emotion-aware responses. Yet, affect is seldom considered to determine whether a GUI design would be perceived as good or bad. We study how physiological signals can be used as an early, effective, and rapid affective assessment method for GUI design, without having to ask for explicit user feedback. We conducted a controlled experiment where 32 participants were exposed to 20 good GUI and 20 bad GUI designs while recording their eye activity through eye tracking, facial expressions through video recordings, and brain activity through electroencephalography (EEG). We observed noticeable differences in the collected data, so we trained and compared different computational models to tell good and bad designs apart. Taken together, our results suggest that each modality has its own “performance sweet spot” both in terms of model architecture and signal length. Taken together, our findings suggest that is possible to distinguish between good and bad designs using physiological signals. Ultimately, this research paves the way toward implicit evaluation methods of GUI designs through user modeling

Desenvolvimento de um modelo para avaliação da estética visual de interfaces de usuários de aplicativos usando deep learning

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Ciências da Computação.O desenvolvimento tecnológico dos últimos anos tornou a computação indispensável no cotidiano da população, tanto na carreira profissional quanto na vida pessoal. Devido a sua grande importância foram criadas várias iniciativas de ensino de computação já na educação básica. O ensino de computação para esses jovens, utilizando ambientes de desenvolvimento como o App Inventor, auxilia os alunos a aprender competências básicas de programação e pensamento computacional. Além da programação em si, outro aspecto importante é o design das interfaces de usuário de apps visando a sua usabilidade. Por isso, como parte do processo de ensino-aprendizagem torna-se importante, também, o ensino e a avaliação da estética visual dos aplicativos criados pelos alunos. Atualmente esta avaliação se dá de maneira manual, exigindo tempo, esforço e preparação por parte dos instrutores, pontos estes que até mesmo dificultam a adoção do ensino de computação em escolas brasileiras. Nesse contexto, o objetivo deste trabalho é o desenvolvimento de um modelo utilizando deep learning para avaliar automaticamente a estética visual de projetos desenvolvidos com o ambiente App Inventor, a ser utilizada em unidades instrucionais para ensinar a computação na educação básica. Como resultado é desenvolvido um modelo de regressão, “Appsthetics”, para avaliar a estética visual de interfaces de usuários a partir de uma screenshot da mesma. A análise de desempenho demonstra resultado aceitável (erro quadrático médio = 0.051369) e a demonstração da correlação/concordância com os avaliadores humanos por meio da correlação Pearson (r = 0.74) e a análise Bland & Altman (0.0051) também indicam uma relação aceitável entre as predições e os valores verdades. O modelo desenvolvido é posteriormente integrado à ferramenta CodeMaster, para incluir então uma forma de avaliar o aprendizado de alunos em relação a estética visual de interfaces em seus apps. Com isto espera-se facilitar a avaliação da aprendizagem dos alunos, contribuindo dessa maneira ao ensino de computação mais amplamente nas escolas brasileiras

Who is in the sample? An analysis of real and surrogate users as participants in user study research in the information technology fields

Background: Constructing a sample of real users as participants in user studies is considered by most researchers to be vital for the validity, usefulness, and applicability of research findings. However, how often user studies reported in information technology academic literature sample real users or surrogate users is unknown. Therefore, it is uncertain whether or not the use of surrogate users in place of real users is a widespread problem within user study practice.Objective: To determine how often user studies reported in peer-reviewed information technology literature sample real users or surrogate users as participants.Method: We analyzed 725 user studies reported in 628 peer-reviewed articles published from 2013 through 2021 in 233 unique conference and journal outlets, retrieved from the ACM Digital Library, IEEE Xplore, and Web of Science archives. To study the sample selection choices, we categorized each study as generic (i.e., users are from the general population) or targeted (i.e., users are from a specific subpopulation), and the sampled study participants as real users (i.e., from the study population) or surrogate users (i.e., other than real users).Results: Our analysis of all 725 user studies shows that roughly two-thirds (75.4%) sampled real users. However, of the targeted studies, only around half (58.4%) sampled real users. Of the targeted studies sampling surrogate users, the majority (69.7%) used students, around one-in-four (23.6%) sampled through crowdsourcing, and the remaining 6.7% of studies used researchers or did not specify who the participants were.Conclusions: Key findings are as follows: (a) the state of sampling real users in information technology research has substantial room for improvement for targeted studies; (b) researchers often do not explicitly characterize their study participants in adequate detail, which is probably the most disconcerting finding; and (c) suggestions are provided for recruiting real users, which may be challenging for researchers.Implications: The results imply a need for standard guidelines for reporting the types of users sampled for a user study. We provide a template for reporting user study sampling with examples.</p

GUIComp: A GUI Design Assistant with Real-Time, Multi-Faceted Feedback

Department of Computer Science and EngineeringMaintaining the high quality of mobile Graphic User Interfaces (GUIs) is essential to make a mobile application more useful for its users. For this reason, there are many mobile GUI prototyping tools. However, users may face challenges while designing graphic user interfaces, due to a lack of relevant skills, experience, and guidance. In particular, the users can often be overwhelmed in the first prototyping stage and it is hard to recognize mistakes for users in advance. These challenges can be significantly alleviated by supporting immediately feedback (e.g., recommendation and evaluation) while designing GUI. Due to absence of feedback, users still rely on their own ideas and intuition with mobile GUI prototyping tool that requires timeconsuming and error-prone design procedures. In this thesis, we aim at investigating what causes users to become frustrated during the design process, and how to resolve the issues. To achieve this goal, first, we conducted semistructured interviews with 16 users, to understand their challenges with an existing tool, and to identify features that can facilitate the design process. Second, based on the interview results, we built a GUI prototyping tool, called GUIComp that provides real-time multi-faceted feedback on a user???s current design, such as visual complexity scores, viewer???s attention heatmap, and recommended example designs. Third, we performed a between-user experiment, where 30 participants were asked to create mobile GUIs with either GUIComp or an existing design tool. Fourth, we asked 26 online workers to assess the designs produced in the experiment. The results indicate that GUIComp users produced more acceptable designs than the non-GUIComp users and designing with GUIComp results in a more enjoyable, satisfactory, and affordable user experience during the design process than that with the existing tool. We discuss how to design for multi-facted feedback while designing GUI to effectively interact human and AI and limitations of the study. The fundamental idea of this thesis is to go beyond traditional GUI prototyping to create more acceptable designs to general users using real-time multi-faceted feedback. The resulting systems establish a research framework where real-time multi-faceted feedback can alleviate the challenges while designing GUIsclos

Thinking with data visualisations: cognitive processing and spatial inferences when communicating climate change

Data visualisations can be effective for communicating scientific data, but only if they are understood. Such visualisations (i.e. scientific figures) are used within assessment reports produced by the Intergovernmental Panel on Climate Change (IPCC). However, IPCC figures have been criticised for being inaccessible to non-experts. This thesis presents a thematic analysis of interviews with IPCC authors, finding that a requirement to uphold scientific accuracy results in complex figures that are difficult for non-experts to comprehend, and which therefore require expert explanation. Evidence is subsequently presented showing that figures with greater visual complexity are associated with greater perceived comprehension difficulty among non-experts. Comprehension of complex data visualisations may require readers to make spatial inferences. When interpreting a time-series graph of climate data, it was found that non-experts did not always readily identify the long-term trend. Two experiments then show that linguistic information in the form of warnings can support spatial representations for trends in memory by directing visual attention during encoding (measured using eyetracking). This thesis also considers spatial inferences when forming expectations about future data, finding that expectations were sensitive to patterns in past data. Further, features that act on bottom-up perceptual processes were largely ineffective in supporting spatial inferences. Conversely, replacing spatial inferences by explicitly representing information moderated future expectations. However, replacing spatial inferences might not always be desirable in real-world contexts. The evidence indicates that when information is not explicitly represented in a data visualisation, providing top-down knowledge may be more effective in supporting spatial inferences than providing visual cues acting on bottom-up perceptual processes. This thesis further provides evidence-based guidelines drawn from the cognitive and psychological sciences to support climate change researchers in enhancing the ease of comprehension of their data visualisations, and so enable future IPCC outputs to be more accessible

Sensemaking with learning analytics visualizations: Investigating dashboard comprehension and effects on learning strategy

In the provision of just-in-time feedback, student-facing learning analytics dashboards (LADs) are meant to aid decision-making during the process of learning. Unlike summative feedback received at its conclusion, this formative feedback may help learners pivot their learning strategies while still engaged in the learning activity. To turn this feedback into actionable insights however, learners must understand LADs well enough to make accurate judgements of learning with them. For these learners, LADs could become an integral part of their self-regulatory learning strategy. This dissertation presents a multifaceted examination of learners’ sensemaking processes with LADs designed to support self-regulatory learning. The in-situ studies detailed therein examine learners’ understanding of the data visualized in LADs and the effects of this understanding on their performance-related mental models. Trace data, surveys, semi-structured in-depth qualitative interviews, and retrospective cued recall methods were used to identify why, when, and how learners used LADs to guide their learning. Learners’ qualitative accounts of their experience explained and contextualized the quantitative data collected from the observed activities. Learners preferred less complex LADs, finding them more useful and aesthetically appealing, despite lower gist recall with simpler visualizations. During an early investigation of how LADs were used to make learning judgments in situ, we observed learners’ tendency to act upon brief LAD interactions. This inspired us to operationalize gist as a form of measurement, describing learners’ ability to make sense of a LAD after a brief visual interrogation. Subsequent comparisons of the accuracy and descriptiveness of learners’ gist estimates to those of laypeople repeatedly showed that laypeople were more apt than learners to produce accurate and complete gist descriptions. This dissertation culminates in a final study examining the evolution of learners’ mental models of their performance due to repeated LAD interaction, followed by a discussion of the contextual factors that contributed to what was observed. Trends observed across this work suggest that learners were more apt to “get the gist” with LAD after repeated interaction. This dissertation contributes a novel method for evaluating learners’ interpretation of LADs, while our findings offer insight into how LADs shape learners’ sensemaking processes