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.
- Be able to discuss different uses for prediction algorithms in education
- Be able to discuss the theory behind the CART, Conditional Inference Trees and C5 classification algorithms
- Construct classification models to predict student dropout and state validation metrics for the model
- Compare classification models on appropriate metrics
In this unit you will be working towards buildimg models to predict student course dropout and then comparing those models. As background to this task please read over the follwing materials and watch the methodological videos. If you find any other useful materials please add them under Additional Materials at the end of the this page and pull request the change back to this repo.
Jalayer Academy. (2015). R - Classification Trees (part 1 using C5.0)
Grey, C.G.P. (2017). How Machines Learn.
Once you have completed all tasks in the unit, please complete the knowledge check.
Pradhan, C. (2016). What are the differences between ID3, C4.5 and CART? Quora