Best Practices in Dropout Prediction: Experience-Based Recommendations for Institutional Implementation
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 i...
| Autores: | , , , |
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| Formato: | capítulo de livro |
| Fecha de publicación: | 2020 |
| País: | España |
| Recursos: | Universidad Autónoma de Madrid |
| Repositorio: | Biblos-e Archivo. Repositorio Institucional de la UAM |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.uam.es:10486/710884 |
| Acesso em linha: | http://hdl.handle.net/10486/710884 https://dx.doi.org/10.4018/978-1-7998-5074-8.ch015 |
| Access Level: | acceso abierto |
| Palavra-chave: | Return on Investment (ROI) Receiver Operating Characteristic (ROC) Curve Sensitivity Specificity Accuracy Holdout Method Over/Under Sampling Techniques K-Fold Cross-Validation Informática |
| Resumo: | 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. |
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