[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118372-en":3,"doc-seo-118372-105":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},118372,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Personalized learning model based on machine learning algorithms - Research overview","Machine learning algorithms are widely used in personalized learning within educational information technology by applying big data analysis and data mining to detect patterns in students’ learning behaviors, preferences, and performance. The proposed direction addresses limitations found in current university teaching applications, including dependence on data, overfitting/underfitting, explanatory challenges, computing cost, bias, outlier sensitivity, and privacy concerns. Through model analysis, the work seeks to expand personalized classroom dimensions and better align learning objectives, content, and methods to learners’ unique characteristics.","Personalized learning model based on machine learning  \nalgorithms  \nZhang Jin1,2, Amirrudin Kamsin3  \n1Faculty of Information Technology, City University, Petaling Jaya, Malaysia 2School of Computer Information Engineering, Nanchang Institute of Technology, Nanchang, China 3Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia  \n\n| Article history:\u003Cbr>Received Sep 17, 2023 Revised Jun 28, 2024 Accepted Aug 12, 2024 | Machine learning algorithms have been widely applied in the field of personalized learning within educational information technology. By leveraging big data analysis and data mining techniques, machine learning can help identify patterns and trends in students' learning behaviors, preferences, and performance. This information can then be used to tailor educational resources and experiences to meet the individual needs and unique characteristics of each learner. Machine learning has made great progress and achievements in the teaching process of universities, but there are also some shortcomings. Such as data dependence, over-fitting and under-fitting, explanatory problems, need a lot of computing resources, data bias, sensitive to outliers, cannot solve all problems, and the challenge of data privacy, through the analysis of machine learning algorithm model, efforts to find ways to expand the dimension of personalized learning classroom, meet the students in learning objectives, learning content, learning methods of the special characteristics and unique needs, to guide students to actively explore and research, obtain innovation and appropriate learning results.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Artificial intelligence Intelligent tutoring systems Learning analytic\u003Cbr>Machine learning algorithms Personalized learning |  |\n\nCorresponding Author:  \nAmirrudin Kamsin  \nFaculty of Computer Science and Information Technology, Univeristiti Malaya 50603 Kuala Lumpur, Malaysia  \nEmail: [amir@um.edu.my](amir@um.edu.my)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nProbability theory, statistics, approximation theory, convex analysis, algorithm complexity theory, and other fields are all integral to the field of machine learning (ML) . According to Tian [1], experts in the study of how machines can learn new information and abilities, as well as how to improve upon their current levels of efficiency and effectiveness by modelling human learning processes. It is the primary means by which intelligent machines can be created and thus the initial subset and core of artificial intelligence (AI) . Many areas of AI can benefit from it, especially those that rely on induction and synthesis rather than deduction. At present, ML is mainly applied in fields such as data mining, network information services, robots and games, problem solving, image recognition, expert systems, and cognitive simulation [2] . As shown in Figure 1 [3], the ML algorithms can be classified into 6 categories: regression, classifcation, time series, clustering, association and oulier detection. In general, the fundamental of ML process is based on feedback iteration as illlustrated in Figure 2 [4], the process can be broken down into several steps: collection of data, clearning of data, selection and feature extraction, model evalation and deployment.  \nPersonalized learning is the process of discovering and solving learning problems for specific children through comprehensive evaluation, tailoring learning strategies and methods that are different from  \nothers, and enabling children to learn effectively [5] . Every child is unique, with their own unique talents, preferences, and innate strengths, as well as weaknesses that are different from others [6] . To solve children's learning problems, personalized methods should be used to adapt to learning requirements.  \nIn today's rapidly evolving information, personalized learning has become an inevitable ","cbCaijCgd1FFpwdB","https://ap.wps.com/l/cbCaijCgd1FFpwdB","pdf",395908,1,6,"English","en",105,"# Introduction\n## Core role of machine learning\n## Feedback-iterative learning process\n# The driving effect of machine learning algorithms on personalized learning\n## Personalized learning and analysis","[{\"question\":\"How do machine learning algorithms support personalized learning?\",\"answer\":\"They use big data analysis and data mining to uncover patterns in students’ learning behaviors, preferences, and performance, enabling tailored learning resources and experiences.\"},{\"question\":\"What limitations are discussed in current machine learning use for teaching?\",\"answer\":\"The document highlights issues such as data dependence, overfitting and underfitting, explanatory problems, high computing requirements, data bias, sensitivity to outliers, and challenges related to data privacy.\"},{\"question\":\"What is the general process underlying the machine learning workflow described here?\",\"answer\":\"The workflow is described as feedback-iterative and can be broken into data collection, data cleaning, selection and feature extraction, model evaluation, and deployment.\"}]","Personalized learning model based on machine learning algorithms - 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