[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122961-en":3,"doc-seo-122961-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122961,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Machine Learning Skills To K-12","Data-driven approaches in computer disciplines have grown rapidly through real-world machine learning (ML) implementations over the last decades. ML is increasingly integrated into computer education for grades K–12, where understanding how student agency and intuition develop is essential as computational learning becomes more common. Research also must address barriers to teaching algorithmic learning across K–12, especially when educators struggle to integrate traditional learning methods. The study outlines prospects in data-mining schooling, including shifts in philosophy, technology, and practice, and highlights key elements for embedding ML into broader K–12 computer courses.","Machine Learning Skills To K–12  \nAmira Bibo Sallow1, Renas RajabAsaad2*, Hawar Bahzad Ahmad2, Saman Mohammed Abdulrahman2, Ahmed Alaa Hani1, Subhi R. M. Zeebaree3  \n1 Department of Information Technology, Duhok Technical College, Duhok Polytechnic University, Duhok, KRG, IRAQ  \n2 Department of Computer Science, College of Science, Nawroz University, Duhok, KRG, IRAQ  \n3 Energy Engineering Department, Technical College of Engineering, Duhok Polytechnic University, Duhok, KRG, IRAQ  \n*Corresponding Author: [eman.khorsheed@nawroz.edu.krd](eman.khorsheed@nawroz.edu.krd)[ ](eman.khorsheed@nawroz.edu.krd)DOI: [https://doi.org/10.30880/jscdm.2024.05.01.011](https://doi.org/10.30880/jscdm.2024.05.01.011)  \n\n| Article Info | Abstract |\n| --- | --- |\n| Received: 1 December 2023\u003Cbr>Accepted: 25 April 2024\u003Cbr>Available online: 21 June 2024 | The promise of data-driven methodology in various computer disciplines has been shown by the many real-world implementations of methods based on machine learning (ML) over the last couple of decades. ML is finding its way into the computer curriculum in higher |\n| Keywords\u003Cbr>Machine learning, K-12 education, computational thinking, curriculum, pedagogy, artificial intelligence, programming, technology integration, data-driven methodology, educational innovation | education, and an increasing number of organizations are introducing it into computer education in grades K–12. Researching how agency and intuition grow in these situations is critical as computational learning becomes increasingly common in K–12 computer instruction. However, knowing the difficulties associated with teaching algorithmic learning through grades K–12 presents an even more difficult barrier for computer education research, given the difficulties educators and schools now face in integrating traditional learning. This article describes the prospects in data mining schooling for grades K–12. These developments include adjustments to philosophy, technology, and practice. The research addresses several distinctions that K–12 computer educators should consider while addressing this problem and places the current results into the broader context of computing education. The research focuses on crucial elements of the fundamental change needed to properly incorporate ML into more comprehensive K– 12 computer courses. Giving up on the idea that rule-based,\"traditional\" programming is necessary for next-generation computational thinking is a crucial first step. |\n\n1. Introduction  \nEarly educators were aware of shifts in the work economy. They were the first to acknowledge that everyone needed to be aware of the latest technological advancements, which they believed would fundamentally alter society and the workplace [1] . Guzdial asserts that computer and software education proponents for students in grades kindergarten through twelve have offered various justifications for their efforts. Proponents said that comprehension of computer fundamentals was required to appreciate the one-state record-keeping of the algorithm-driven virtual and physical worlds [2]. The new technique was being used by scientists for population studies and large-scale computing more and more. Understanding these ideas also helps individuals better use technologies and consider algorithms' significant impact on their lives. They contend further that learning math, science, solving problems, and life in general may be facilitated by computers. Workplace computing is becoming more and more important. It has recently become a crucial skill strongly associated with industrial innovation  \nand economic success. The advantages of increased business efficiency and the transfer of computing power from a small number of people to a large number have led to a steady increase in the levels of abstraction of programming [3, 4] .  \nComputer power connectors have been quickly shifting away from the hardware since the 1950s, and every ten years, new educational initiatives a","cbCaipUfYoJsNw3B","https://ap.wps.com/l/cbCaipUfYoJsNw3B","pdf",559580,1,10,"English","en",105,"# Introduction\n## Evolution of computer education\n## ML and AI adoption in K–12\n## Historical initiatives and tools","[{\"question\":\"Why is integrating machine learning into K–12 computer education important?\",\"answer\":\"The document explains that ML is increasingly entering K–12 curricula, making it crucial to understand how learners develop agency and intuition as computational learning becomes more widespread.\"},{\"question\":\"What barriers make algorithmic learning difficult to teach in grades K–12?\",\"answer\":\"It highlights difficulties educators and schools face in integrating traditional learning methods, which creates an additional obstacle for computer education research focused on algorithmic learning.\"},{\"question\":\"What does the article emphasize as a key change for incorporating ML into K–12 courses?\",\"answer\":\"It stresses moving beyond the belief that rule-based, traditional programming is necessary for next-generation computational thinking, as a foundational step toward effective ML inclusion.\"}]","Machine Learning Skills To K-12 | 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is integrating machine learning into K–12 computer education important?","Question",{"text":76,"@type":77},"The document explains that ML is increasingly entering K–12 curricula, making it crucial to understand how learners develop agency and intuition as computational learning becomes more widespread.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What barriers make algorithmic learning difficult to teach in grades K–12?",{"text":81,"@type":77},"It highlights difficulties educators and schools face in integrating traditional learning methods, which creates an additional obstacle for computer education research focused on algorithmic learning.",{"name":83,"@type":74,"acceptedAnswer":84},"What does the article emphasize as a key change for incorporating ML into K–12 courses?",{"text":85,"@type":77},"It stresses moving beyond the belief that rule-based, traditional programming is necessary for next-generation computational thinking, as a foundational step toward effective ML 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