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Student Retention Modeling

Education
case study

Business Impacts

Increased Student Engagement

95%

Model accuracy

Reduced Number of Dropouts

Customer Key Facts

  • Location : North America
  • Industry : Education Management

Problem Context

With the increased adoption and popularity of virtual classrooms, many universities are struggling to efficiently utilize data to promote engagement with students and minimize the attrition rate. Both of the customer’s campus-based and online programs had high dropout rates across different courses within the first few weeks of enrollment. They wanted to implement proactive measures to identify the reasons behind students who drop out, determine the risk factors undermining student engagement, and intervene at an early stage to reduce attrition and improve overall performance.

 

Challenges

 

  • Performing Exploratory Data Analysis to handle missing values, perform data type conversion (from numerical to categorical, etc.)
  • Final feature selection and identifying relevant and important features and attributes for model building
  • Testing and validation of a variety of models to identify the best performing model to provide maximum accuracy

Technologies Used

Google Compute Engine

Google Compute Engine

Google's BigQuery

Google's BigQuery

Google Cloud Storage

Google Cloud Storage

Google Cloud Composer

Google Cloud Composer

Google Kubernetes Engine

Google Kubernetes Engine

Google Cloud Dataproc

Google Cloud Dataproc

JupyterLab

JupyterLab

Increasing Student Engagement and Reducing Attrition with Machine Learning

Solution

Quantiphi built a multivariate rescoring model to help predict the likelihood of a student dropping out of the course and also identify the important factors driving the student’s dropout rate. The on-prem student dataset obtained in MS SQL format was migrated to Google's BigQuery and leveraged to train the model in making predictions. General transformations, such as binning the similar columns using the six sigma rule and implementing outlier analysis, were performed to suit the model building process. The solution highlights the metric or variable(s) defining the student’s dropout rate, predicts success probability, and uses the output of the model as an input to modulate operational policies to give students more targeted engagement support, while also helping the customer significantly improve retention rates.

Result

  • Robust & scalable architecture
  • Identification of both high-risk and low-risk students
  • More targeted engagement/support for student success

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