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Expii

Expii Internship

Responsive Web Design, EdTech, AI

 

Helping Expii build a personalized virtual tutor?

Expii is a free personalized learning platform for everyone. I interned at Expii’s product design team over the summer of 2018 as a product design intern. Over the three months, I collaborated with the product design team, engineers and curriculum designers to design Expii Practice: a gamified, adaptive virtual tutor that recommends students practice questions personalized to their learning needs.

Length of the project: 3 months
My role: User research, User experience design, Visual design, Prototyping, User-testing
Teammates: Arnold Syphommorath (Design Lead), Tiffany Jiang (Product Designer)
Collaborating departments: Curriculum Design, Marketing
Keywords: Mobile App, Artificial Intelligence, EdTech

 
 

Problem

Expii practice is a virtual tutor that recommends students questions based on their learning needs. In order for Expii Practice to personalize its experience for students, they have to use Expii Practice regularly. However, the current retention rate for Expii is 6%, The retention rate for Expii’s website has to reach 40% and above in order for the students’ experience to become effective.

How can Expii Product Design team help Expii achieve 40% retention rate for Expii Practice by the end of 2018

 
 

Final Deliverables

As apart of the product design team, I collaborated with the product design team, engineers and curriculum designers to re-design Expii Practice. By the end of the internship, I produced 3 key deliverables:

  1. Engaging points of entry for new and return users

  2. Two modes of practice experiences

  3. A reward system for Expii users

 
 
 
 
 
 

Persona & Customer Journey Map

Customer journey maps that illustrate typical user paths.

 
 

Research on Expii Practice

Our research and design process over the summer is sectioned into 2 two-week design sprints:

Sprint 1: Redefine the Expii practice experience.

For the 1st sprint, we focused on studying the existing designs of the Expii practice. We realized that the greatest hurdles in the current experience is that the algorithm Expii practice uses to grade the students behaves counter-intuitively. Without understanding the algorithm, it is very easy for students to see the system as dysfunctional. However, this algorithm is used widely across multiple popular games such as League of Legend, Overwatch, etc.

  1. Therefore, how do these games succeed at using ELO algorithm, and what Expii can learn from their gamified experience?

  2. What can we learn from Expii’s competitors to improve Expii Practice experience?

In order to answer these questions, we conducted competitive analysis on games that utilize ELO system as well as on competitors such as Khan Academy, etc. Here are our key findings from the research, which helped us define what are the components that current Expii practice experience lacks:

 

Sprint 2: Building the components of the new Expii Practice experience

Now that we understood what are the components we would like to implement in Expii practice, we would like to integrate them into a coherent experience and try to understand where does the user meet each of the component. We used a customer journey map to brainstorm potential locations to put each of the component.

By using customer journey map, we also have a clear view of what are all of the steps we need to take to complete the new experience. At this point in the design process, we start to assign tasks to each other and focus on building each part of the experience.

Customer journey maps

 

Interface Design / Prototyping


User-testing & Findings

We conducted 3 rounds of user-testings with local middle school students. We have learned about the new system  

  1. Does not associate "Start" with problem-solving

  2. Users habitually continue practicing when they do well

 

Reflections & Next steps

The next step is designing feedback. Personalized feedback is important for students to have. Without proper feedback, students rely on personalized feedback to know exactly where they need to improve on. Gaining effective feedback and dramatically reduce the learning curb and learning time while maximizing the learning outcome. In order to better design the learning system, it will be a collaboration between the data scientists and