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This chapter introduces a tutorial on using Automated Machine Learning (AutoML) to automate and scale predictive modeling in education. In particular, we illustrate the usefulness of AutoML for idiographic analysis, where each individual student has their own particular model fitted from their own individual data. We demonstrate how AutoML simplifies the ML pipeline, enabling the creation of individually optimized models for multiple datasets. Moreover, we illustrate how to apply explainable artificial intelligence techniques to automate the interpretation of the main model predictors, offering a view of the variables that matter. The complete pipeline demonstrated in this tutorial holds potential to provide automated real-time insights based on idiographic analysis in a transparent and trustable way.

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Automating Individualized Machine Learning and AI Prediction Using AutoML: The Case of Idiographic Predictions

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Mohammed Saqr,
  • Ahmed Tlili,
  • Sonsoles López-Pernas

摘要

This chapter introduces a tutorial on using Automated Machine Learning (AutoML) to automate and scale predictive modeling in education. In particular, we illustrate the usefulness of AutoML for idiographic analysis, where each individual student has their own particular model fitted from their own individual data. We demonstrate how AutoML simplifies the ML pipeline, enabling the creation of individually optimized models for multiple datasets. Moreover, we illustrate how to apply explainable artificial intelligence techniques to automate the interpretation of the main model predictors, offering a view of the variables that matter. The complete pipeline demonstrated in this tutorial holds potential to provide automated real-time insights based on idiographic analysis in a transparent and trustable way.