Lingua: an xAPI-Enabled App
Lingua is a learning app for students at a large foreign language school. Faced with lower student performance in German courses, the school needed an accessible way for students to gain targeted practice. As it affords this practice, the Lingua app also utilizes Javascript xAPI to collect information about learner interactions with the materials - how long they took to complete it, what answers they chose, and how many times they visited the help materials. The result is a wealth of valuable learner data that allows the language school to adjust what they’re offering and better meet their learners’ evolving needs.
Tools used:
Storyline 360, Adobe Illustrator, Adobe XD, Visual Studio Code, JSHint, Veracity LRS
Details
In the last year, I’ve become increasingly interested in how data can be leveraged to enhance the digital learning experience and to increase learning efficacy. For me, understanding specifically how learners are interacting with the experiences I build not only helps me target improvements, but allows me to make more strategic decisions for aligned learning design in the future. If my solution is designed well, the data can confirm the user research I conducted before beginning development. At the same time, the data can bring to light certain elements in my design that might not be working as well as I had planned. With this information, I can learn better what works well for particular user group and spend my time - and theirs - more wisely in the future.
This xAPI-enabled Lingua app was built in this spirit. Designed to be used together with students’ existing curricula as a blended learning solution, the app allows users to log in, enter their credentials, and practice their language at a personalized level. Inspired by the UI/UX heritage of popular language-learning apps, Lingua presents learners with a series of staggered exercises that build their competency with the subject matter. The format ranges from multiple choice to free response. At the end, learners receive feedback on their performance, and this information, along with the data collected, is relayed to the LRS for further analysis.
The Code
I love working with Javascript in Storyline 360 and I had so much fun with this project. Being entirely self-taught, I often spend time working my way through xAPI tutorials and connecting with the community online. For this project, I worked with the Javascript xAPI code in Visual Studio Code, first adding lines, then testing them. There’s something exhilarating about that suspenseful moment where you hold your breath to see if what you’ve done is going to work, and that celebratory thrill you get when you suddenly see its happening. Or the disappointing but equally motivating moment when your code breaks and the hunt for answers begins. In these moments, JSHint was my new best friend and I loved the process of troubleshooting.
Seeing what the language is capable of and how it helps serve learners better is incredibly inspiring. I’m currently taking a front-end web development course so I can continue increasing my skill level in this area.
The LRS & the Data
Throughout this process, I relied on Veracity LRS to store the data I was collecting from the app. I programmed the app to gather information on how long it took my users to complete the course, how many times they visited the help resources, their individual answers to the multiple choice and free response questions, and their quiz scores. Each piece of information is tied to the learner via their name and email address, which I also collected.
I used Veracity to visualize this data - the sample below shows answers to Questions 2, 3, and 4. From these pie charts, I can see which percentage of responses are correct, as well as which distractors are most convincing. With this data, I can alter the learning experience to make it more effective. For example, I might decide that Question 2 might warrant more convincing distractors, whereas Question 3 might need a slight adjustment in content.
