Personas based support tool for requirements elicitation

Lack of user understanding can bring a damaging effect to software development projects. As we acknowledge, there is a wide variety of users in each software project and consequently there are variation of goals, needs and attitudes within the establishment of users' requirements.So, there are difficulties in organizing and categorizing these information and also, in validating these requirements within a development project. Using personas to capture and analyze users can facilitate user’s understanding.However, identifying personas from large user information is difficult.This research examines the persona technique and proposes a software support tool to help developers in gathering and analyzing requirements by using personas.Likewise, the goals and tasks of each persona are established via actual requirements of the users.The creation and utilization of persona have been investigated via proposed tool aimed to elicit and represent users' requirements in a manageable format. A “Personas based Support Tool for Requirements Elicitation (PSTRE)” facilitates software developers to gather and analyze user requirements.The resulting personas which are suggested by the proposed tool, have to be well understood and well presented to the development team and also provide easy way to extract main functionalities and features of the proposed system

Augmented education within a physical space

This research aims to explore how to enhance student engagement in higher education institutions (HEIs) using a novel conversational system (chatbots). The study applies a design science research (DSR) methodology and is executed in three iterations: persona elicitation, survey and student engagement factor models (SEFMs), and chatbots interactions analysis. In the first iteration, two k-means clustering analyses are applied to student data, including engagement on campus and student interaction with a virtual learning environment (VLE). The first analysis produces four different types of students based on their engagement and performance data, while the second analysis produces two clusters based on the students’ interactions with a VLE (in this case, Blackboard). The second iteration will produce SEFMs, which will include the factors that affect student engagement, confirmed using structural equation modelling (SEM). Finally, the third iteration will produce effective and usable chatbots that enhance student engagement. The pragmatic findings from this study will make three contributions to the current literature. Firstly, machine learning is used to build data-driven personas using k-means clustering. Secondly, a persona template is designed for university students, which supports the construction of data-driven personas. Thirdly, SEFMs will be built. Future iterations will build tailored interaction models for these personas and evaluate them using chatbots technology

The design of a composite folding bike to improve the user experience of commuters

Over the last years, the popularity of folding bikes has been increasing as a result of the rise of multi-modal transport. They are used by commuters as a complement to public transport. Despite the increasing popularity, the current market offer of folding bikes still represents quite some restrictions and downsides which decrease their usability. This paper shows a user-centred process of designing and prototyping a composite folding bike with the aim of improving the user experience of folding bike using commuters. By improving the usability and ease of use of folding bikes, their full potential can be unlocked. The design process led to a disruptive folding bike design with front and rear single-sided offset wheel mounting. The concept excels in its intuitive and quick folding mechanism, superior riding performance and comfort, adjustability and overall ease of use. In addition to the design process and prototyping of the folding bike, this extended abstract elaborates on the performed user tests and its results. These tests range from the impact of offset wheels to the intuitiveness of the folding mechanism and were performed in order to prove different aspects of the design. This research shows how rethinking and redesigning a bike concept (product) from scratch, using a user-centred design process and taking into account the three aspects — business, technology and people — can lead to a disruptive design that improves usability and the overall user experience of the stakeholders

Personas: A Strategy for More Inclusive and Usable Reproductive Health Tracking Technologies

In this report, I argue for the inclusion of personas in the development process of technologies that are designed to allow users to collect and track their health information. Personas provide a profile of a user and their needs, goals, and contexts. This tool can help designers to better understand their users, in order to create better, more usable end products. I examine one particular self-tracking technology, smartphone applications that allow users to record information about their menstrual cycles. Many of the most popular period tracking applications available today only meet the needs of a narrow user group, resulting in a technology that is unusable for certain populations of users. In this project, I collect data from individuals who had attempted to use period tracking technologies in the past, but felt they were unable to manage the information that was important to them. Using Lene Nielsen’s Engaging Personas approach, I crafted three personas that reflected the needs and goals of these users. This project serves as a model for developers of period tracking applications and health-tracking technologies more broadly

Toxic text in personas: An experiment on user perceptions

When algorithms create personas from social media data, the personas can become noxious via automatically including toxic comments. To investigate how users perceive such personas, we conducted a 2 × 2 user experiment with 496 participants that showed participants toxic and non-toxic versions of data-driven personas. We found that participants gave higher credibility, likability, empathy, similarity, and willingness-to-use scores to non-toxic personas. Also, gender affected toxicity perceptions in that female toxic data-driven personas scored lower in likability, empathy, and similarity than their male counterparts. Female participants gave higher perceptions scores to non-toxic personas and lower scores to toxic personas than male participants. We discuss implications from our research for designing data-driven personas.info:eu-repo/semantics/publishedVersio