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The role of machine learning in education<\/h2>\n
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Source: SoloWay<\/a><\/em><\/p>\n Artificial intelligence (AI) and machine learning (ML) is a field of computing that enables algorithms to learn without continual reprogramming or any other external input. By analyzing new data, a system using machine learning becomes more intelligent thanks to its ability to notice and classify patterns and trends. Ultimately, this results in improved performance over time.<\/p>\n The global size of the machine learning for education market size was valued at USD 1.82 billion in 2021 and is forecasted to rise at a compound annual growth rate (CAGR) of 36.0%<\/a> from 2022 to 2030. Implementing machine learning in education helps students, teachers, and administrators improve their processes and enhance the learning journey. Machine learning uses adaptive learning to identify difficulties students may be experiencing. Due to heavy workloads or overcrowded classrooms, tutors often fail to distinguish struggling students from uninterested participants. Machine learning can help to flag learning issues and enable a tutor or program to alter teaching methods in response.<\/p>\n ML algorithms allow educational software to personalize content, schedules, and learning goals to students\u2019 needs and capabilities, thereby improving the efficiency and quality of teaching and learning. This lets tutors shift their focus to processes that benefit from a human perspective.<\/p>\n Educators use ML to predict future learning outcomes and change teaching methods based on these outcomes. Predictive analytics detects patterns in student behavior and determines the probability of each student passing a course or pursuing extracurricular activities.<\/p>\n Machine learning is particularly beneficial at the K12 level. With ML, behavioral problems and poor academic performance can be identified or predicted with a high degree of accuracy. Educators can then intervene and address issues before it\u2019s too late. ML is also used to improve security and to enable students and parents to access self-service tools.<\/p>\n ML can predict enrollment levels and help higher education institutions find potential applicants. There are also applications for research, as machine learning algorithms can quickly and accurately analyze large, growing datasets.<\/p>\n It isn\u2019t just schools and colleges that can benefit from machine learning. Learning and EdTech companies also use ML to improve learning outcomes, customer service, and targeted marketing. Capabilities such as text-to-speech, translation, transcription, chatbots, and content classification are a bonus.<\/p>\n ML for eLearning software development by Intellias<\/p>\n Using machine learning in the education sector improves teaching and learning processes in various ways. Experts predict that ML will become an increasingly significant part of the education sector over the coming years due to the many benefits of machine learning applications in education, such as:<\/p>\n eLearning courses and individual materials (video lectures, eBooks, discussion forums, etc.) can be expensive to translate into different languages. ML programs can use natural language processing<\/a> and deep learning in education to translate and transcribe content quickly, accurately, and cost-effectively.<\/p>\n Predictive analytics allows a course to adjust to individual needs. Using machine learning, skills gaps can be identified, and specific pieces of content can be delivered to learners based on their performance. Learners can also see other metrics, including how long they spend on each section and their degree of interaction with course materials. This ensures that ML-based courses are more efficient and effective than traditional one-size-fits-all courses.<\/p>\n ML-based eLearning courses can automatically carry out many administrative and management tasks including onboarding, scheduling, providing instructions, tracking attendance, and grading work. This frees up time for educators to spend on more creative, subjective, and human-focused tasks.<\/p>\n One limitation of traditional teaching processes is the homogenous nature of educational courses. Learning styles vary, and homogenous approaches give certain styles an advantage while placing others at a disadvantage. Machine learning systems enable differentiated instruction, where teaching is optimized to suit each individual learner. This allows educators to provide targeted, personalized learning pathways without the extra workload needed to do it manually.<\/p>\n Using ML in education can improve the experiences of all eLearning system users, including teachers, learners, and administrators. Personalization of course content and the streamlining of administrative tasks increases engagement and motivation, leading to improved learning outcomes. Self-service capabilities can also be useful for students who lack the confidence to approach teachers or other staff with concerns.<\/p>\n While manual grading can be affected by human error and bias, machine learning systems don\u2019t have these limitations. This results in fairer and more accurate grading and assessments. ML algorithms also automatically detect plagiarism in learners\u2019 projects.<\/p>\n Machine learning dramatically benefits higher education research departments. Deep data science capabilities (for example, text mining<\/a>) of ML systems have many implications for the discovery and management of research depositories.<\/p>\n ML is used extensively in advertising and marketing to identify and reach target audiences. Educational institutions use these capabilities to attract the most suitable students. Each system can also detect and prevent fraud or security concerns during enrolment.<\/p>\n Educators seek technology-powered approaches to teaching and learning that would be effective and scalable. Teachers capture the potential of emerging tech to make studying more engaging, immersive, and fun. The uses of machine learning in education, including adaptive learning, inclusive learning, and AI assistants, ensure the development of accessible learning tools and environments.<\/p>\n Adaptive learning is a teaching approach that aims to personalize study plans, lessons, assessments, and practice activities applicable for elementary classrooms and high schools. The name speaks for itself, meaning that the learning process will be adjusted for individual students according to their skills, performance, and learning pace.<\/p>\n Source: Adaptive Learning 101: An Ultimate Guide for Educators and Learners<\/a><\/em><\/p>\n ML algorithms analyze data to tailor educational content, delivering personalized pathways and ensuring students encounter material that is optimally challenging. Moreover, adaptive learning systems provide educators with detailed insights into each student’s learning journey, highlighting strengths, weaknesses, and progress. This data-driven perspective enables teachers to fine-tune their instructional strategies and offer specific support where it’s needed, enhancing the overall quality of education.<\/p>\n Inclusive learning customizes experiences for each student’s unique needs, ensuring equitable access to educational opportunities and resources. Backed by machine learning in schools, the approach can provide personalized learning experiences to accommodate various abilities, learning styles, and backgrounds. Through detailed analysis of individual performance and engagement, ML algorithms tailor content in terms of difficulty and format.<\/p>\n Such an adaptability ensures that education is accessible to students with disabilities, special educational needs, and those from different linguistic and cultural backgrounds. Inclusive learning paves the way for a more equitable educational landscape.<\/p>\n Machine learning also offers tools for language translation and content localization, enhancing language and cultural inclusivity within the learning environment. By providing educators with data-driven insights, ML supports the development of inclusive teaching practices and policies.<\/p>\n Chatbots and robots are revolutionizing education by making learning more accessible around the clock. Used in both high schools and universities, AI tutors offer personalized content and exercises, available 24\/7, to enhance students’ learning experiences. They provide instant feedback on assignments and quizzes, helping students quickly identify and correct their mistakes. Moreover, as scalable solutions, they can support a large number of students simultaneously, alleviating the burden on teachers by handling many of the learners\u2019 questions and queries. This allows teachers to focus more on personalized instruction and managing overcrowded classrooms.<\/p>\n Chatbots cater to students with special educational needs by offering customized learning paths. The data collected by AI systems on students’ learning behaviors offers insights that can inform more effective teaching strategies and help students refine their learning approaches.<\/p>\n Intellias applied machine learning to build a conversational virtual learning assistant<\/a> for one of our clients. In this case, students with special educational needs were our client\u2019s sales representatives. Our chatbot helped them improve their product knowledge with engaging and personalized training. Powered by artificial intelligence and machine learning, the chatbot makes corporate learning highly interactive and contains easily digestible information.<\/p>\n McKinsey predicts that ML-driven education will help teachers ease operational and administrative burden<\/a>, focusing on students. Having approximately 50 working hours a week, teachers spend less than half the time in direct interaction with students.<\/p>\n Machine learning can help educators reallocate up to 30% of their time toward activities with students, like coaching and direct engagement for better motivation and social skill development. Technology can help teachers come up with better lesson plans and strategies as well as deal with study assessments faster.<\/p>\n Source: McKinsey<\/a><\/em><\/p>\n There are many machine learning use cases in education. Well-known examples include:<\/p>\n At UC Berkeley, a tool called Gradescope<\/a> streamlines grading workflows during times of high demand. Teachers determine a list of criteria against which the tool compares students\u2019 work to provide accurate performance assessments.<\/p>\n Source: Gradescope screenshot<\/em><\/p>\n The University of Michigan\u2019s M-Write<\/a> automated text analysis tool was developed using ML principles. It analyzes written assignments to determine which students may need help and aids them with drafting and revising.<\/p>\n
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<\/div>\n\t\t\tIdentifying learning difficulties<\/h4>\n
Personalizing teaching and learning processes<\/h4>\n
Predictive analytics<\/h4>\n
Identifying and predicting problems in K12 education<\/h4>\n
Covering applications in higher education<\/h4>\n
Improving marketing for EdTech companies<\/h4>\n
\u200b\u200b\u200b8 key benefits of machine learning in education<\/h2>\n
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<\/p>\nGreater global access to online learning<\/h4>\n
More efficient courses<\/h4>\n
Reduced administrative workload<\/h4>\n
Personalized learning pathways<\/h4>\n
A more effective user experience<\/h4>\n
More accurate assessment and grading<\/h4>\n
Faster research<\/h4>\n
Improved enrolment processes<\/h4>\n
Applications of machine learning in education<\/h2>\n
Adaptive learning<\/h3>\n
![\"Adaptive](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2023\/05\/BP-ML-in-Education-UPD_body-infographic_3.jpg\")
<\/p>\nInclusive learning<\/h3>\n
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<\/p>\nAI tutors and chatbots<\/h3>\n
Task automation<\/h3>\n
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<\/p>\n\u200b\u200bReal-world examples of machine learning in education<\/h2>\n
UC Berkeley\u2019s Gradescope<\/h3>\n
![\"Machine](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2023\/05\/BP-ML-in-Education-UPD_body-03.jpg\")
<\/p>\nUniversity of Michigan\u2019s M-Write<\/h3>\n