Prediction of student behavior has been a prominant area of research in learning analytics and a major concern for higher education institutions and ed tech companies alike. It is the bedrock of methodology within the world of cognitive tutors and these methods have been exported to other areas within the education technology landscape. The ability to predict what a student is likely to do in the future so that interventions can be tailored to them has seen major growth and investment, though implementation is non-trivial and expensive. Although some institutions, such as Purdue University, have seen success we are yet to see widespread adoption of these approaches as they tend to be highly institution specific and require very concrete outcomes to be useful.
- Create predictions of which students are likley to drop out of which courses and use these predictions to inform semester planning
- Construct classification models (CART, C4.5 and C5.0) to predict student dropout and state validation metrics for the model
- Compare classification models on appropriate metrics
The data (drop-out.csv) comes from a university registrar's office. The code book for the variables are available in the file code-book.txt.
- Examine the variables and their definitions
- Sseparate data set into a training set and a test set
- Randomly select 25% of the students to be the test data set and leave the remaining 75% for training data set
- Predict the student level variable "complete"
- Visualize the relationships between the chosen variables as a scatterplot matrix
- Construct a classification tree that predicts complete using the caret package
- Train a Conditional Inference Tree using the
partypackage on the same training data - Install the C50 package, train and then test the C5.0 model on the same data
- Compare the models with caret