Techniques used to adjust the class distribution of a data set (i.e., the ratio between the different classes/categories represented), in which new data points are added/removed.
Published in Chapter:
Best Practices in Dropout Prediction: Experience-Based Recommendations for Institutional Implementation
Juan J. Alcolea (DIMETRICAL, The Analytics Lab, Spain), Alvaro Ortigosa (Universidad Autonoma de Madrid, Spain), Rosa M. Carro (Universidad Autonoma de Madrid, Spain), and Oscar J. Blanco (DIMETRICAL, The Analytics Lab, Spain)
Copyright: © 2020
|Pages: 23
DOI: 10.4018/978-1-7998-5074-8.ch015
Abstract
This chapter focuses on the key practical aspects to be considered when facing the task of developing predictive models for student learning outcomes. It is based on the authors' experience building and delivering dropout prediction models within higher education contexts. The chapter presents the information used to generate the predictive models, how this information is treated, how the models are fed, which types of algorithms have been used, and why and how the obtained results have been evaluated. It recommends best practices for building, training, and evaluating predictive models. It is hoped that readers will find these recommendations useful for the design, development, deployment, and use of early warning systems.