Users often asked for more customisation options, like being able to adjust the page width to avoid constant scrolling. When considering adding this feature, I researched external and internal studies on learning science to find the best way to accommodate different reading styles.

I looked into the F-shaped reading pattern, which explains how readers navigate through content. They may jump from section to section to quickly assess if the content is relevant, scan for specific keywords, or read the entire content line by line.

Research indicates that keeping the line length between 45-75 characters helps prevent eye fatigue. Interestingly, most customer feedback focused on expanding the page width beyond 75 characters. To address these different reading preferences, I aimed to offer 3 different width settings: narrow, default, and wide.

The narrow setting helps users with ADHD who have trouble concentrating on large blocks of text. The default setting offers the most suitable reading length for most reading needs, and the wide setting is ideal for very large screens.

The last hurdle was the implementation. Since users can change the font size, we needed to find a way to show the right number of characters per line based on the chosen text size. After several discussions with the engineering team, we chose to use "em" as a flexible unit to adjust the container size based on the selected text size. We also established a maximum width limit to avoid the text overlapping with any UI elements.

All three width settings have found their devoted user bases and have been equally selected since the setting was introduced in 2023.

Perlego users often work with several textbooks at the same time, looking for the most helpful text excerpts for their essays and research papers. A common difficulty they face is quickly finding and accessing previously highlighted and annotated text passages. Until now, they've had to open all relevant books in multiple browser tabs and search for their previous annotations one by one.

I conducted a survey with over 150 students and interviewed 20 Perlego users to understand their annotation strategies. Then, I compared these strategies to note-taking behaviours identified from platform data points. I presented the findings in a design sprint and mapped out the typical note-taking journeys for different user groups on Perlego. As a design team, we brainstormed and generated possible solutions to streamline their note-taking experiences on Perlego.

I created paper prototypes of the best ideas and then presented them to Perlego users to see how well the solutions would meet their needs. It became evident that for a solution to be successful, it would need to be a standalone feature. Integrating it with existing organisational features would require users to also adopt those features. What users truly desired was a quick and simple way to access their notes from multiple books without having to organise their library first.

After feeling confident about the direction, I shared the insights and proposed solution with the product team. They quickly included it in the roadmap since it aligned with their goals of driving user engagement. After aligning with the engineering team regarding limitations and technical constraints, I finalised the designs and validated the final flow once more with users.

The final feature is called "Notebook." It enables users to filter and sort their annotations by book, highlight colour, date added, and book order. It also allows them to reorganise their notes and add further thoughts.

This feature led to a significant increase in notes and highlights created on the platform and helped students to remain focused and engaged with their content.

In our user interviews, we found that undergraduate students struggled with developing effective study habits to complete their assigned reading before class. We discussed ways to keep students motivated in their reading and experimented with adding a reading timer to the interface to see how it would impact their progress.
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After a successful test rum, I looked into various focus techniques, including setting up 25-minute Pomodoro timers, incorporating white noise, and introducing achievement levels to assist users in reaching their goals. When testing these ideas with users, I discovered that pre-set time intervals had the most widespread appeal across different user groups.

I designed an improved version of the initial timer feature. This enhancement allows users to easily select from different time presets. Once the timer starts, it moves to the corner of the interface to avoid becoming too distracting. After the timer finishes, it comes back into focus and offers quick controls to restart the timer or add more minutes to the clock.

All three features achieved success by uncovering underlying user needs and utilising learning science to determine the most optimal approach for various user groups. The time spent on qualitative research enabled the product, design, and engineering teams to put the platform's data points into context with the underlying user motivations, helping to shape three successful learning tools on Perlego.