Transforming The Future of Education with Data Visualization

ELEA, an education tech startup in Myanmar, partnered with Boonmee Lab to design and implement data visualization features for their TechLearn application. The goal was to create intuitive dashboards and reports that present key academic data, such as student assessments, grades, and progress tracking. These tools help students, teachers, and principals better understand academic performance and outcomes across various educational levels.

Key Challenges

The project presented several challenges, particularly in managing complex use cases and ensuring data accuracy:

User Flow & Complex Use Cases: One of the main design challenges was completing the user flow to meet the client’s vision while handling complex scenarios. For instance, the application had to accommodate students taking exams outside their primary school and teachers managing classes across multiple institutions. These diverse use cases required adaptable and flexible user flows to ensure a seamless and smooth experience for all users.
Consistency: It was essential to maintain consistency in data presentation, such as student names, class levels, and assessment results, to avoid confusion as users navigated different sections of the platform.
Data Accuracy & Continuity: Typically, mock data is used during UI design without much focus on accuracy. However, in this project, the complexity of the data and use cases required us to ensure that the mock data accurately represented real-world scenarios. The system had to manage multiple views and complex data layers, making it crucial for the mock data to align with real data and effectively cover all use cases.
Design and Data Hand-off: One of the main challenges was creating detailed design and data hand-off documents for the client’s development team. As we had not worked together before, it was crucial to establish clear communication through comprehensive documentation. The challenge lay in ensuring that the design interactions and data structures were clearly outlined, allowing both teams to align effectively and avoid miscommunication during the development process.

Process

To address the various challenges, our teams worked closely together throughout the project. The design process began by refining the user flow to align with the client’s vision while accommodating complex scenarios. We prioritized ensuring the user flow was adaptable and seamless for all user types. Consistency was crucial, particularly in the presentation of key data points, to prevent confusion as users navigated the application.

On the data side, we recognized the complexity of the project’s requirements and ensured that the mock data used during design accurately represented real-world scenarios. Managing multiple views and data layers required careful attention to align mock data with real data and effectively cover all use cases. Additionally, to facilitate smooth collaboration with the client’s development team, we prepared detailed design and data hand-off documents.

Comparison between original and proposed designs for a data visualization report, highlighting improvements in user interface, clarity of score analysis, and presentation of academic performance metrics.

Example of design and data hand-off documents, showcasing detailed color palettes and interface structures for seamless collaboration with the development team.

Results and Impact

The project resulted in a user-friendly and powerful tool for visualizing academic data, offering clear insights into student performance across various levels and subjects. By incorporating academic assessment models like Bloom’s Taxonomy and the LBAT model, the application allows teachers and administrators to track progress and outcomes more effectively.

Designed to be scalable, the platform is flexible enough to adapt to future requirements, such as supporting international curricula and additional institutions. The result is a solution that enhances the learning experience while providing educators with a clear, intuitive interface for making data-driven decisions.

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