Driving Student Learning Outcomes via Content Recommendation Engine
EducationBusiness 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 S3
Amazon Aurora
Amazon Glue
AWS Lambda
Amazon Cognito
Amazon Cloudwatch
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
