RDBI (Ross Bell)
As part of a class on Human-Centered Computing, we were tasked with redesigning an existing mobile app using user feedback that had been collected earlier in the semester.
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Working in groups of four, our objective was to analyze the app’s usability, identify key pain points, and then overhaul its UI (User Interface), UX (User Experience), and core functionality based on that analysis.
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The app assigned to our group was "IrishJobs", a job search platform widely used in Ireland. Through collaborative research and iterative design, we focused on several key areas for improvement:
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Dark Mode Implementation: To enhance accessibility and reduce eye strain, especially for users browsing in low-light environments.
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Typography Enhancements: We refined the font size, style, and color to improve readability and ensure better visual hierarchy across the interface.
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UI Animations: We introduced subtle transitions and animations to create a smoother, more responsive user experience, making the app feel more modern and intuitive.
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The final result was a fully developed interactive prototype, which we presented to the class for feedback. The session allowed us to gather fresh insights from peers and instructors, which we then incorporated into the final version of our redesign.
For this project, my role on the team was that of the data analyst.
I began by gathering and reviewing the datasets collected by my teammates during their earlier user research. Each dataset was carefully examined and compiled into a structured document, which I then translated into a list of potential features and user requirements for the app redesign.
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To better visualize and collaborate on this information, I transferred the refined data into a shared workspace. From there, I created two unique user personas, using real data points to humanize our findings. These personas helped us better understand our target users and the potential impact of our design decisions on their experience with the app.
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Once the personas were finalized, I led the feature prioritization process using the MoSCoW method, which allowed us to clearly define which features were must-haves, should-haves, could-haves, and will-not-haves. Our team placed strong emphasis on issues related to visual design and navigation, as these were elements we could directly address using our chosen design tools. Less actionable concerns, such as performance-related issues, were noted but deprioritized.
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In addition to the MoSCoW method, I took the initiative to map our features onto an Action Priority Matrix, giving our team a clear visual representation of which tasks offered the most value for the effort required. This helped streamline our workflow and focus our attention on the most impactful improvements for the user.
