By Fang Zhou, Microsoft Data Scientist; Hong Ooi, Microsoft Senior Data Scientist; and Graham Williams, Microsoft Director of Data Science
Education is a relatively late adopter of predictive analytics and machine learning as a management tool. A keen desire for improving educational outcomes for society is now leading universities and governments to perform student predictive analytics to provide better-informed and timely decision making.
Student predictive analytics often aims to solve two key problems:
- Predict student academic outcomes so as to better target support.
- Predict students at risk of dropping out so as to prevent attrition.
Education systems face enormous diversity across regions and countries. Two case studies demonstrate the novel and unique landscape for machine learning in the education world.
- A mixed effects regression model has been developed in conjunction with an Australian education department to measure the influence of student characteristics and to predict student test scores in the presence of variation across students and schools. The model was implemented using R and then integrated with Azure Machine Learning for deployment to production through Power BI.
- A predictive model for student drop out has been developed in conjunction with an Indian state government using machine learning two-class boosted decision trees. For deployment an end-to-end pipeline was built using Azure services including Azure SQL Database, Azure ML and Azure Data Factory
Microsoft Data Scientists assisted with the analysis in both cases and we present details below with R code provided in a git repository to replicate the modelling on artificial data.