Argo is an augmented reality application that helps people to learn a new language by challenging them to identify objects around them in the language that they want to learn. It facilitates peer-to-peer learning that is engaging, fun and takes advantage of user’s environment.
Adobe XD - Design and Prototyping
UserTesting.com - Usability Testing
Adobe Indesign - Final Report
Adobe After Effects - Video Prototype
Keynote - Presentation Deck
Qualtrics - Survey
Excel - Data Analysis
Asana - Project Management
Mona Mishra(Me) - Researcher and Designer
Tim Salau - Researcher and Designer
Weixuan Fu - Researcher
Han Han - Researcher
Shih-Tsai Wei - Researcher and Designer
Our brain stores visual information in a way that is easy to recall because visual images have more things that we instantly associate with and hence, that connection makes the information more memorable and easy to recollect. In this day and age where people rely heavily on smartphone and smart devices to learn independently, mobile applications like Duolingo, Babble and Rosetta Stone have made tremendous progress in the eld of language learning. They have taken advantage of visual memory to help users learn a new language that requires very less e ort. They use the vector images of food, people and items that most users are familiar with, and connect them with the word in a foreign language. Although, they have made the language learning process far easier than before, they still fail to engage most users for a longer period of time. We recognized this gap and made use of the principles of contextual augmented reality to engage and motivate users to construct their knowledge during real-world observation.
The popularity of augmented reality applications has been increasingly recognized, especially in the field of education, because it has made possible for virtual objects to co-exist in the real environment. This has made it easy for learners to visualize concepts and spatial relationships that cannot be easily realized or enacted. With the help of pattern recognition, an application can be triggered to do something. Argo recognizes images as patterns and triggers the application to identify its label with the help of computer vision technology. We have also used the concepts of gamification and the process of peer-to-peer learning in order to increase engagement.
Given the technical challenge we had, we needed to investigate current Computer Vision technology to and the feasibility of realizing the app. We concluded the requirements as below:
Argo has three main users groups, namely casual language learners, gamers and travellers.
Before jumping into any design tool, our team decided to start with storyboarding and diagramming what we wanted the Argo experience to be like for our users. When we conducted initial interviews with users, we learned that people enjoy language learnings apps that kept them engaged, was easy to learn, and allowed them to track their daily progress. In addition, we discovered that some of the biggest challenges to learning a new language for people was nding the time, staying engaged throughout gameplay. Feedback from our interviews also indicated that people are motivated to learn a new language based on job-related goals, if they are traveling and want to learn more about culture, or if they are learning a new language for school.
Storyboarding and sketching allowed each individual member of our team the opportunity to articulate and share their vision of Argo.
From this point on, we moved forward towards wireframing the design for the following task flows: app sign-up, setting language preference, exploration mode, browsing through the gallery, playing round types, and finding nearby friends. Then we created functional prototypes and a test plan, where we had users atempt to complete set tasks and talk aloud while completing those tasks.
The purpose of this testing was to garner feedback from users and evaluate what they find confusing or delightful about the app. By utilizing UserTesting.com, we were able to complete 3 rounds of user testing with around 20 participants. We outlined 7 tasks and some follow up questions after each task and a few post-test question. We noted the time taken by them to complete the task, their difficulty level for performing each task.
Please follow this link for the working prototype