case study

Driving Student Learning Outcomes via Content Recommendation Engine

Education

Business Impacts

Hyper personalized recommendations

Improved student learning outcomes

Customizable and reusable solution

Customer Key Facts

  • Country : US
  • Industry : Education & Publishing

Problem Context

To enhance student learning outcomes, the client sought to enhance its content recommendation engine. The existing engine, supplied by a third party, lacked the required transparency for the client’s team to maintain control over the solution’s modeling variables. Consequently, the client aimed to transition to a deep learning-powered content recommendation engine to supplant the existing solution. This upgraded system offers comprehensive visibility and control over generated recommendations, enabling modifications as required.

Challenges

  • Limited customizations: The current solution provides limited visibility and control to the client, depriving them of the flexibility to customize it according to their requirements.
  • Cost considerations: Developing and maintaining a system with customizations and scalability features requires a significant investment of time and resources.

Technologies Used

Amazon Sagemaker

Amazon Sagemaker

Amazon S3

Amazon S3

Amazon Aurora

Amazon Aurora

Amazon Glue

Amazon Glue

AWS Lambda

AWS Lambda

Amazon Cognito

Amazon Cognito

Amazon Cloudwatch

Amazon Cloudwatch

AWS API Gateway

AWS API Gateway

Solution

 Quantiphi leveraged deep learning techniques to craft knowledge-tracing models aimed at shaping student learning achievements through a recommendation engine. The recommendation engine tracks students' interactions with diverse learning resources over time, factoring in variables such as individual learning aptitudes and exercise complexities. Using these insights alongside students' learning abilities, the recommendation engine proposes subsequent learning exercises and adjusts dynamically according to student proficiency levels.

The solution is built on AWS and leverages several AWS services. The recommendation engine is also integrated with their existing learning application and is designed to give the client complete control and visibility. 

Results

  • Improved personalized learning experiences for students
  • Increased engagement with learning materials

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