How to Build a Discovery Feature for Course Platforms with PredictionIO
17 Sep 2013

Many startups of late are taking the education industry by storm. Take Coursera as an example. It partners with top universities and colleges in the world to offer free higher education courses to anyone in the world. More examples of online course platforms include Udemy, Memrise, edX, Codecademy, Udacity, Treehouse, Lore, Chegg, 2u, Knewton and Minerva Project.

With the increased number and variety of courses on offer, users often have to spend a lot of time browsing the courses and picking what they want. If you are building a similar service, you can make this discovery process more time-efficient by integrating PredictionIO into your app.

We are going to use two examples to show you how to give more relevant choices to users while they are browsing courses, and how to suggest courses to users personally based on their own previous choices, and the preferences of other users who have similar studying patterns.

 

1. People who like this course may also like….

Udemy and others recommend related courses when users are browsing course information pages. It is similar to Amazon’s famous “Customers Who Bought This Item Also Bought” feature.

UseCase-CourseDiscovery-Udemy

 

Building this feature with PredictionIO takes just a few steps:

Step 1: Install PredictionIO Server

Follow the instructions in http://docs.prediction.io/install/ to install PredictionIO.

Step 2: Integrate PredictionIO

Follow the instructions in http://docs.prediction.io/templates/similarproduct/quickstart/ to integrate PredictionIO to your application’s data collection, and prediction querying stages.

 

2. Courses We Recommend to You Personally

Not only that, PredictionIO can help predict users’ future study preferences personally by analyzing their previous choices. These choices are closely linked to the users’ previous choices and truly reflect their interests. This is how Memrise’s course recommendation looks like:

UseCase-CourseDiscovery-Memrise

Building this feature with PredictionIO is also straightforward.

Assuming you have installed PredictionIO and have integrated the SDK for importing data as described above, all you need to do is to add a new Item Recommendation Engine, and add code to retrieve prediction results from it.

 

Step 1: Install PredictionIO Server

Follow the instructions in http://docs.prediction.io/install/ to install PredictionIO.

Step 2: Integrate PredictionIO

Follow the instructions in http://docs.prediction.io/templates/recommendation/quickstart/ to integrate PredictionIO to your application’s data collection, and prediction querying stages.

 

With the above code, the personalization feature has been built!

In fact, PredictionIO can go one step further and make it even more personalized for your users. For example, after a user has finished a course, why not send a congratulatory email to the user alongside a few course suggestions? Based upon what other users with similar learning patterns and interests have studied, or simply some popular choices in a related subject. It is up to you what you want to do with PredictionIO to make your app smarter and easier to use!