Prediction

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.

Purpose of the Project

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

Data

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.

Procedure

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 party package 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