How an EdTech Client Optimized Course Curriculum and Marketing Campaigns with a Comprehensive Data Strategy

Lack of precision in strategizing the university courses that are beneficial to the student community

As a leading university offering a wide range of online and offline course curriculums, our client recognized the importance of course metrics to drive informed decision-making. The executive team sought to analyze course performance on a periodical cycle, specifically on a term basis, and identify opportunities for retention or optimization based on student feedback.

Additionally, they aimed to develop a performance dashboard that would provide a comprehensive view of course metrics at the term level. By leveraging these data insights, the client aimed to maintain a competitive edge and deliver an engaging learning experience to their student community.

A sentiment analysis framework built on Databricks

To address their business challenge, our client partnered with zeb to enhance their data architecture. After analyzing their existing infrastructure, we recommended the implementation of Azure Databricks to establish a robust sentiment analysis framework.

The client's data ecosystem was centered around a custom Learning Management System (LMS) and Azure Synapse Analytics. Our team devised a comprehensive data strategy that encompassed the extraction, transformation, analysis, and visualization of course feedback. This approach aimed to provide valuable insights to improve the client's learning programs and enhance the student experience.

Here's the workflow of our sentiment analysis framework:

• Consuming course feedback from the LMS platform through REST APIs as JSON files.
• Extracted course datasets from Azure SQL Datawarehouse to correlate with students' feedback. Some of the datasets include the number of students, submissions, and average response time.
• Standardized & transformed the column-level datasets to visualize the key metrics such as high-performing & low-performing courses.
• Implemented the sentiment analysis by considering the student ratings and grading from the LMS platform. We built these implementations by leveraging Azure Databricks Notebooks, MLflow, and PySpark code.
• Categorized the courses based on the performance metrics by utilizing the Azure Cognitive Services API.
• Developed an intuitive performance dashboard based on sentiment analysis. This improved the decision-making on university course plans and funding.

Precise decision-making & funding over university courses

With the implementation of the sentiment analysis framework, our client achieved a significant improvement in the student experience through personalized course feedback, which was previously unattainable. The feedback metrics, visualized at the term level, empowered the client to optimize course curriculums and instructor performance to better cater to the needs of their student community.

These essential course metrics provided valuable insights for the executive team, enabling them to make informed decisions regarding funding, performance optimization, marketing campaigns, and revenue targets. The executive team now had seamless access to real-time metrics and could efficiently address business inquiries through a centralized course performance dashboard.

We're here to build the data strategy for you. With a sentiment analysis framework built on Databricks, our edTech client optimized course curriculum and marketing campaigns, resulting in a reduction of human efforts & AdWords expenses.

We can help your organization achieve similar benefits by leveraging cutting-edge technology solutions tailored to your specific needs.

Contact us today to transform your future business initiatives and drive success.