‘I’m not a tech person’

This study contributes to existing research on polymedia by probing into what we call academic polymedia, investigating the constant availability of interpersonal, professional, and social media for constructing scholarly personas. Drawing on the technobiographical narratives of a group of Hong Kong bilingual academics, we analyze academics’ perceptions of their media choices as situated in their professional polymedia environments. In particular, we examine how choices between public and private media shape academic persona development, and the way polymedia engagement impacts the participants’ language choice for academic purposes. This study sheds light on existing research on workplace discourse and identity in sociolinguistics by offering a polymedia dimension that draws on people’s media ideology in developing their professional identities.

Using Personas with Visual Impairments to Explore the Design of an Accessible Self-Driving Vehicle Human-Machine Interface

Recent reports have suggested that most self-driving vehicle technology being developed is not currently accessible to users with disabilities. We purport that this problem may be at least partially attributable to knowledge gaps in practice-oriented user-centered design research. Missing, we argue, are studies that demonstrate the practical application of user-centered design methodologies in capturing the needs of users with disabilities in the design of automotive systems specifically. We have investigated user-centered design, specifically the use of personas, as a methodological tool to inform the design of a self-driving vehicle human-machine interface for blind and low vision users. We then explore the use of these derived personas in a series of participatory design sessions involving visually impaired co-designers. Our findings suggest that a robust, multi-method UCD process culminating with persona development may be effective in capturing the conceptual model of persons with disabilities and informing the design of automotive system.

Development of Personas to Communicate Narrative-Based Information About the HPV Vaccine on Twitter

Introduction: Personas are based on real-life typologies of people that can be used to create characters and messages to communicate important health information through relatable narrative storylines. Persona development is data-driven and can involve multiple phases of formative research and evaluation; however, personas are largely underutilized in digital health research. The purpose of this study was to create and document persona development to deliver narrative-focused health education for parents on Twitter with the goal of increasing uptake of HPV vaccination among adolescents.Methods: Leveraging data from a mixed-method study conducted in the U.S. with a diverse population of parents with adolescents ages 9–14, we used both qualitative and quantitative data (e.g., the National Immunization Survey—Teen, focus groups, and social media) to create personas. These data sources were used to identify and develop key characteristics for personas to reflect a range of parents and their diverse understandings and experiences related to HPV vaccination. A parent advisory board provided insight and helped refine persona development.Results: Four personas emerged and were characterized as the (1) Informed Altruist, (2) Real Talker, (3) Information Gatherer, and (4) Supporter. Characteristics differed across personas and provided insights into targeted narrative strategies. Described attributes included demographics, psychographics, communication style, vaccine goals and aspirations, vaccine challenges and frustrations, and vaccine hesitancy.Discussion: This work demonstrates how multiple data sources can be used to create personas to deliver social media messages that can address the diverse preferences and needs of parents for HPV vaccine information. With increasing usage of social media for health information among parents, it is important for researchers to consider marketing and design thinking to create health communication messages that resonate with audiences.

Applications of Machine Learning in Education

This research aims to explore how to enhance student engagement in higher education institutions using novel chatbots. This study's principal research methodology is design science research, which is executed in three iterations: personas elicitation, a survey and development of student engagement factor models (SEFMs), and chatbot interaction analysis. This chapter focuses on the first iteration, personas elicitation, which proposes a data-driven persona development method (DDPDM) that utilises machine learning, precisely a k-means clustering technique. Data analysis is conducted using two datasets. Eight personas are produced from the two data analyses. The pragmatic findings from this study make two contributions to the current literature. Firstly, the proposed DDPDM uses machine learning, specifically k-means clustering, to build data-driven personas. Secondly, the persona template is designed for university students, which supports the construction of data-driven personas. Future work will cover the second and third iterations.

A User-Centered Approach to Design a Mobile Application for Chronic Pain Management

This paper presents a user-centered approach for the design/re-design of a mobile application for chronic pain management, with a focus on chronic low back pain, sometimes referred to as "invisible disability". Within the framework of a multidisciplinary project involving physicians, patients and computer scientists, the work presented here describes firstly an identification and representation process of the needs/expectations of the users of such an application (patients/physicians), based on a data-driven persona development method and, secondly, an evaluation process of the user experience (UX) of the current version of the application, using among others usability tests. The aim of the approach is to establish (re-)design guidelines in order to improve this kind of application.

A Data Analytics Approach to Persona Development for The Future Mobile Office

The concept of using automated vehicles as mobile workspaces is now emerging. Consequently, the in- vehicle environment of automated vehicles must be redesigned to support user interactions in performing work-related tasks. During the design phase, interaction designers often use personas to understand target user groups. Personas are representations of prototypical users and are constructed from user surveys and interview data. Although data-driven, large samples of user data are typically assessed qualitatively and may result in personas that are not representative of target user groups. To create representative personas, this paper demonstrates a data analytics approach to persona development for future mobile workspaces using data from the occupational information network (O*NET). O*NET consists of data on 968 occupations, each defined by 277 features. The data were reduced using dimensionality reduction and 7 personas were identified using cluster analysis. Finally, the important features of each persona were identified using logistic regression.

Personas for Perfectly Tailored eHealth Technologies: Segmenting Heart Failure Patients using the Persona Approach Twente (Preprint)

BACKGROUND The full potential of eHealth technologies to support self- and disease-management for patients with chronic diseases, is not being reached. A possible explanation for these lacking results is that during the development process, insufficient attention is paid to the needs, wishes and context of the prospective end-users. To overcome such issues, the User-Centered Design (UCD) practice of creating personas is widely accepted as a means to ensure the fit between a technology and the target group or end-users throughout all phases of development. OBJECTIVE In the current study, we integrate several approaches to persona-development into the Persona Approach Twente (PAT), to attain a structured approach that aligns with the iterative process of eHealth development. METHODS In three steps, different parts from the dataset were analyzed using the Partitioning Around Medoids clustering method. First, we used health-related EPR data only. Secondly, we added person-related data that was gathered through interviews and questionnaires. Thirdly, we added log data. RESULTS In the first step, two clusters were found, with average silhouette widths of 0.12, and 0.27. In the second step, again two clusters were found, with average silhouette widths of 0.08, and 0.12. In the third step, three clusters were identified, with average silhouette widths of 0.09, 0.12, and 0.04. CONCLUSIONS The Persona Approach Twente is applicable for mixed types of data, and allows alignment of this UCD method to the iterative approach of eHealth development. Challenges lie in data quality and fitness for (quantitative) clustering.