Machine Learning in K–12 Educational Settings
Abstract
The many real-world applications of machine learning-based approaches over the past several decades have shown the potential of data-driven methodology in a variety of computing fields. Higher education computer curricula are beginning to include machine learning, and more and more institutions are incorporating it into K–12 computer instruction. Given the growing prevalence of computational learning in K–12 computer teaching, it is imperative to investigate how agency and intuition develop in these kinds of settings. However, considering the challenges educators and schools already have in integrating conventional learning, understanding the challenges connected with teaching algorithms for learning across grades K–12 provides an even more challenging barrier for computer education research. the curriculum's incorporation of artificial intelligence and technology.This article outlines the potential for data mining education in grades K–12. These advancements include modifications to technology, philosophy, and practice. The study puts the present findings in the larger framework of computing education and discusses some differences that K–12 computer educators should keep in mind while tackling this issue. The study focuses on key components of the fundamental shift that is required in order to successfully integrate machine learning into more thorough K–12 computing curricula. A critical first step is to abandon the notion that next-generation computational thinking requires rule-based, "traditional" programming.