Model Accuracy
of Actual Dropout Events Correctly Recognized
Student Engagement
An American higher education provider with campus-based and online programs wanted to determine risk factors undermining student engagement by using machine learning.
Their programs’ dropout rates were high and so they wanted to implement proactive measures to identify students who are not being engaged properly and intervene at an early stage to reduce attrition and improve overall performance.
Google Cloud Platform
Compute Engine
BigQuery
Cloud Storage
Cloud Natural Language API
Google Data Studio
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.
Quantiphi developed a multivariate re-scoring model to predict the likelihood of a student dropping out of the course and use it to improve their retention rate. 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.
