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MACHINE LEARNING IN EDUCATION
How to Boost Learning Efficiency and Student Engagement
How to Boost Learning Efficiency and Student Engagement
Discover how to use machine learning in education to
✓ personalize learning pathways
✓ improve the online learning engagement
✓ predict student performance
✓introduce administrative task automation.
✓ personalize learning pathways
✓ improve the online learning engagement
✓ predict student performance
✓introduce administrative task automation.
Education is gradually moving away from traditional "one size fits all" classroom practices. Today, the use of AI and big data promises to bring new levels of personalization and support to teaching, learning, and research.
In this blog post, we'll explore the benefits of machine learning services for online learning and on-campus courses. We'll delve into the most common applications of AI and machine learning in the education sector. Finally, we'll give examples of how AI and ML support learning activities in existing projects.
In this blog post, we'll explore the benefits of machine learning services for online learning and on-campus courses. We'll delve into the most common applications of AI and machine learning in the education sector. Finally, we'll give examples of how AI and ML support learning activities in existing projects.
How ML is Used in Education Today, and Where It is Going
Before we dive into the mobile application development examples of AI and ML in education, let's cover the basics first. Machine learning is a branch of AI where algorithms can learn and improve their accuracy from experience without further programming.
Artificial intelligence is one of the fastest-growing technologies in the global education market. According to the latest market research report by Technavio, the AI and ML in the US education sector are expected to grow at a CAGR of 47.77% during the period 2018–2022. Another market research firm, HolonIQ, expects AI adoption in education to reach a global expenditure of $6 billion by 2025.
Machine learning is opening up new possibilities for educators and learners. A recent research by McKinsey suggested that technology can potentially save teachers about or 13 hours a week to redirect toward activities that lead to better student outcomes.
Now, let's explore how AI, machine learning, and automation can help educators create better learning environments in face-to-face and online education.
Artificial intelligence is one of the fastest-growing technologies in the global education market. According to the latest market research report by Technavio, the AI and ML in the US education sector are expected to grow at a CAGR of 47.77% during the period 2018–2022. Another market research firm, HolonIQ, expects AI adoption in education to reach a global expenditure of $6 billion by 2025.
Machine learning is opening up new possibilities for educators and learners. A recent research by McKinsey suggested that technology can potentially save teachers about or 13 hours a week to redirect toward activities that lead to better student outcomes.
Now, let's explore how AI, machine learning, and automation can help educators create better learning environments in face-to-face and online education.
How to Use Machine Learning in Education, Training, and Employment
Enhance learning outcomes through personalization
Students can learn at their own pace, create their schedule, and enjoy every part of their lives. They no longer need to sacrifice their careers or personal and social lives to obtain a degree or improve their skills.
Improve assessment procedures
Education professionals expect AI and machine learning to contribute to a more consistent and accurate grading of students' performance. ML-powered systems can automatically help teachers grade work and with less human interference, giving teachers the potential to save time and decrease grading bias.
Improve management operations
ML can help educators forecast enrollment and optimize the capacity to meet the demand. Institutions can use ML-powered predictive maintenance to manage facilities and equipment more effectively. ML can also be used to prevent fraud and help students and staff control their data.
Improve research and discovery
AI and machine learning in higher education are helping researchers improve content discovery and creation. Possible applications include detecting paraphrased plagiarism and image modification, predicting high impact research and emerging subject areas, augmenting the peer review process, automated or semi-automated content creation based on human input, and using human language to discover and organize content collections.
Predict career paths and provide smart support
Machine learning systems can analyze information from a student's learning track, essays, test results, grades, extracurricular activities, and give course recommendations to students interested in a specific career. Another advantage of ML is its ability to help students ask questions and find help in human language instead of inputting search terms. So students will spend less time online stuck on subjects and assignments they struggle with.
Improve accessibility
AI-powered systems can verbalize visual content for visually impaired learners and create automated video captions for the deaf and hard of hearing. What's more, AI can synthesize realistic voices for text to speech reading and help operate an on-screen mouse, keyboard, and text-to-speech for those with communication and mobility difficulties.
Simultaneously, technology can predict the student's areas of weakness and suggest extra courses and extracurriculars, helping them achieve their professional goals. Colleges and universities can use ML to assist students with answers to their questions about such topics as campus services, grades, schedules, and course requirements for graduation.
Simultaneously, technology can predict the student's areas of weakness and suggest extra courses and extracurriculars, helping them achieve their professional goals. Colleges and universities can use ML to assist students with answers to their questions about such topics as campus services, grades, schedules, and course requirements for graduation.
How Machine Learning Can Be Used in Online Education
Personalize online content
One of the significant benefits of machine learning in education is personalization.
Machine learning allows for a better learning experience — an essential factor for the very competitive e-learning market. Machine learning-based personalization provides e-learning platforms with a more scalable and accurate way to deliver unique one-to-one learning experiences. ML algorithms can help spot when a student struggles with a concept, and adjust the e-learning content, time, and pace to their needs.
Machine learning allows for a better learning experience — an essential factor for the very competitive e-learning market. Machine learning-based personalization provides e-learning platforms with a more scalable and accurate way to deliver unique one-to-one learning experiences. ML algorithms can help spot when a student struggles with a concept, and adjust the e-learning content, time, and pace to their needs.
Improve online learning reach
Communicating across languages can be tricky, especially with complex concepts and high-speed online learning content such as video lectures or course discussion groups. This becomes even more difficult when people with different levels of language ability are not in the same physical location. ML-powered transcription, translation, and text-to-speech algorithms make content more accessible for students worldwide.
Automate administrative busywork
AI and other data-driven technologies remove the need for teachers to perform routine administrative tasks such as scheduling, keeping student logs, attendance, and study material records. Instead of replacing teachers, AI is freeing them from the burden of tedious busywork, thus giving them more time to devote to the educational process.
Improve ROI and retention
One of the challenges often faced by educators in online learning environments is low retention. Online learners struggle with low motivation, poor course design, or lack of communication. All of these can affect a student's decision to drop a class. By using machine learning in online education, teachers can get a better understanding of their students, and create high-quality, accessible, and individualized programs that lead to higher satisfaction from the course.
With ML, institutions can get insights into student retention by analyzing a variety of factors, including individual student factors such as financial aid, academic performance, course load, and external factors such as national unemployment rates.
With ML, institutions can get insights into student retention by analyzing a variety of factors, including individual student factors such as financial aid, academic performance, course load, and external factors such as national unemployment rates.
Practical Applications of ML in Online Education
Course Hero
Course Hero is an online learning platform providing access to millions of study materials on a variety of subjects.
The AI team at Course Hero has developed the company's semantic knowledge graph. This graph enables students to access personalized learning experiences and gives educators tools to create unique course content.
Students and educators get free access to the Course Hero's document library in exchange for uploading their content. The platform vets all uploaded documents for spam, fraud, plagiarism, and copyright infringements. The documents then move to further processing and labeling using ML models. This way, Course Hero can track what kind of materials are used by a given student, and predict other relevant content for their studying needs.
The AI team at Course Hero has developed the company's semantic knowledge graph. This graph enables students to access personalized learning experiences and gives educators tools to create unique course content.
Students and educators get free access to the Course Hero's document library in exchange for uploading their content. The platform vets all uploaded documents for spam, fraud, plagiarism, and copyright infringements. The documents then move to further processing and labeling using ML models. This way, Course Hero can track what kind of materials are used by a given student, and predict other relevant content for their studying needs.
Course Hero
Course Hero is an online learning platform providing access to millions of study materials on a variety of subjects.
The AI team at Course Hero has developed the company's semantic knowledge graph. This graph enables students to access personalized learning experiences and gives educators tools to create unique course content.
Students and educators get free access to the Course Hero's document library in exchange for uploading their content. The platform vets all uploaded documents for spam, fraud, plagiarism, and copyright infringements. The documents then move to further processing and labeling using ML models. This way, Course Hero can track what kind of materials are used by a given student, and predict other relevant content for their studying needs.
The AI team at Course Hero has developed the company's semantic knowledge graph. This graph enables students to access personalized learning experiences and gives educators tools to create unique course content.
Students and educators get free access to the Course Hero's document library in exchange for uploading their content. The platform vets all uploaded documents for spam, fraud, plagiarism, and copyright infringements. The documents then move to further processing and labeling using ML models. This way, Course Hero can track what kind of materials are used by a given student, and predict other relevant content for their studying needs.
Duolingo
A popular online digital education platform for language learning, Duolingo, applies machine learning to personalize the learning experience to each student.
Duolingo uses machine learning and natural language processing in many ways. Users start with a quick AI-driven adaptive placement test in which each question is related to the previous one and whether the user got it right or wrong. This way, Duolingo adjusts the configuration of the course to each student's language ability.
Duolingo lessons are designed in a way to make students revise material over longer intervals. For this purpose, Duolingo implemented half-life regression (HLR). This trainable spaced repetition algorithm keeps track of every word a user has interacted with and uses these stats to accurately predict recall rates for any given word at any time. Thanks to introducing HLR, Duolingo improved their daily student retention by an impressive 12%. The Duolingo team explores new possibilities with deep learning, exploring and evolving models for non-native speech recognition, automated scoring, understanding context, and prioritizing course improvements based on user feedback.
Duolingo uses machine learning and natural language processing in many ways. Users start with a quick AI-driven adaptive placement test in which each question is related to the previous one and whether the user got it right or wrong. This way, Duolingo adjusts the configuration of the course to each student's language ability.
Duolingo lessons are designed in a way to make students revise material over longer intervals. For this purpose, Duolingo implemented half-life regression (HLR). This trainable spaced repetition algorithm keeps track of every word a user has interacted with and uses these stats to accurately predict recall rates for any given word at any time. Thanks to introducing HLR, Duolingo improved their daily student retention by an impressive 12%. The Duolingo team explores new possibilities with deep learning, exploring and evolving models for non-native speech recognition, automated scoring, understanding context, and prioritizing course improvements based on user feedback.
Duolingo
A popular online digital education platform for language learning, Duolingo, applies machine learning to personalize the learning experience to each student.
Duolingo uses machine learning and natural language processing in many ways. Users start with a quick AI-driven adaptive placement test in which each question is related to the previous one and whether the user got it right or wrong. This way, Duolingo adjusts the configuration of the course to each student's language ability.
Duolingo lessons are designed in a way to make students revise material over longer intervals. For this purpose, Duolingo implemented half-life regression (HLR). This trainable spaced repetition algorithm keeps track of every word a user has interacted with and uses these stats to accurately predict recall rates for any given word at any time. Thanks to introducing HLR, Duolingo improved their daily student retention by an impressive 12%. The Duolingo team explores new possibilities with deep learning, exploring and evolving models for non-native speech recognition, automated scoring, understanding context, and prioritizing course improvements based on user feedback.
Duolingo uses machine learning and natural language processing in many ways. Users start with a quick AI-driven adaptive placement test in which each question is related to the previous one and whether the user got it right or wrong. This way, Duolingo adjusts the configuration of the course to each student's language ability.
Duolingo lessons are designed in a way to make students revise material over longer intervals. For this purpose, Duolingo implemented half-life regression (HLR). This trainable spaced repetition algorithm keeps track of every word a user has interacted with and uses these stats to accurately predict recall rates for any given word at any time. Thanks to introducing HLR, Duolingo improved their daily student retention by an impressive 12%. The Duolingo team explores new possibilities with deep learning, exploring and evolving models for non-native speech recognition, automated scoring, understanding context, and prioritizing course improvements based on user feedback.
ETS
Educational Testing Service (ETS) administers more than 50 million tests annually, including international tests such as the TOEFL, TOEIC, GRE, HiSET, and Praxis tests.
The ETS team is actively working on improving their content assessment and validation algorithms. Recently, they've introduced machine learning techniques that score responses to test questions, where the system only requires an appropriate set of responses that have already been scored by a human. This approach allowed ETS to save the human effort involved in content scoring as it does not require someone to enter all possible correct responses into the system manually.
ETS's approach to oral language assessment relies on an automated speech recognition system designed to work with non-native English. Based on the system's output, natural language processing and speech-processing algorithms are used to assess speech on many linguistic dimensions, including fluency, grammatical complexity, and pronunciation. The model is trained on previously observed data scored by human raters, and it is also continually reviewed by experts to maximize its performance.
The ETS team is actively working on improving their content assessment and validation algorithms. Recently, they've introduced machine learning techniques that score responses to test questions, where the system only requires an appropriate set of responses that have already been scored by a human. This approach allowed ETS to save the human effort involved in content scoring as it does not require someone to enter all possible correct responses into the system manually.
ETS's approach to oral language assessment relies on an automated speech recognition system designed to work with non-native English. Based on the system's output, natural language processing and speech-processing algorithms are used to assess speech on many linguistic dimensions, including fluency, grammatical complexity, and pronunciation. The model is trained on previously observed data scored by human raters, and it is also continually reviewed by experts to maximize its performance.
ETS
Educational Testing Service (ETS) administers more than 50 million tests annually, including international tests such as the TOEFL, TOEIC, GRE, HiSET, and Praxis tests.
The ETS team is actively working on improving their content assessment and validation algorithms. Recently, they've introduced machine learning techniques that score responses to test questions, where the system only requires an appropriate set of responses that have already been scored by a human. This approach allowed ETS to save the human effort involved in content scoring as it does not require someone to enter all possible correct responses into the system manually.
ETS's approach to oral language assessment relies on an automated speech recognition system designed to work with non-native English. Based on the system's output, natural language processing and speech-processing algorithms are used to assess speech on many linguistic dimensions, including fluency, grammatical complexity, and pronunciation. The model is trained on previously observed data scored by human raters, and it is also continually reviewed by experts to maximize its performance.
The ETS team is actively working on improving their content assessment and validation algorithms. Recently, they've introduced machine learning techniques that score responses to test questions, where the system only requires an appropriate set of responses that have already been scored by a human. This approach allowed ETS to save the human effort involved in content scoring as it does not require someone to enter all possible correct responses into the system manually.
ETS's approach to oral language assessment relies on an automated speech recognition system designed to work with non-native English. Based on the system's output, natural language processing and speech-processing algorithms are used to assess speech on many linguistic dimensions, including fluency, grammatical complexity, and pronunciation. The model is trained on previously observed data scored by human raters, and it is also continually reviewed by experts to maximize its performance.
Carnegie Learning
Carnegie Learning (CL) is a leading publisher of math curriculum, textbooks, and learning software for grades 6–12.
CL's best-known product is MATHia, an adaptive learning math software for middle-school and high-school learners. MATHia is designed to mirror a human tutor, delivering personalized learning to the students who are struggling and those who need more challenging tasks. The platform doesn't only tell learners what they got wrong, but also helps them understand why they got it wrong, and what they need to know to give the right answer. MATHia also includes LiveLab, a live facilitation tool that highlights each student's progress in real-time, and displays alerts that help teachers assist struggling students. LiveLab uses machine learning to determine which students are most likely to benefit from immediate teacher intervention. It can help pinpoint the specific skills that might prevent students from hitting certain progression milestones.
CL's best-known product is MATHia, an adaptive learning math software for middle-school and high-school learners. MATHia is designed to mirror a human tutor, delivering personalized learning to the students who are struggling and those who need more challenging tasks. The platform doesn't only tell learners what they got wrong, but also helps them understand why they got it wrong, and what they need to know to give the right answer. MATHia also includes LiveLab, a live facilitation tool that highlights each student's progress in real-time, and displays alerts that help teachers assist struggling students. LiveLab uses machine learning to determine which students are most likely to benefit from immediate teacher intervention. It can help pinpoint the specific skills that might prevent students from hitting certain progression milestones.
Carnegie Learning
Carnegie Learning (CL) is a leading publisher of math curriculum, textbooks, and learning software for grades 6–12.
CL's best-known product is MATHia, an adaptive learning math software for middle-school and high-school learners. MATHia is designed to mirror a human tutor, delivering personalized learning to the students who are struggling and those who need more challenging tasks. The platform doesn't only tell learners what they got wrong, but also helps them understand why they got it wrong, and what they need to know to give the right answer. MATHia also includes LiveLab, a live facilitation tool that highlights each student's progress in real-time, and displays alerts that help teachers assist struggling students. LiveLab uses machine learning to determine which students are most likely to benefit from immediate teacher intervention. It can help pinpoint the specific skills that might prevent students from hitting certain progression milestones.
CL's best-known product is MATHia, an adaptive learning math software for middle-school and high-school learners. MATHia is designed to mirror a human tutor, delivering personalized learning to the students who are struggling and those who need more challenging tasks. The platform doesn't only tell learners what they got wrong, but also helps them understand why they got it wrong, and what they need to know to give the right answer. MATHia also includes LiveLab, a live facilitation tool that highlights each student's progress in real-time, and displays alerts that help teachers assist struggling students. LiveLab uses machine learning to determine which students are most likely to benefit from immediate teacher intervention. It can help pinpoint the specific skills that might prevent students from hitting certain progression milestones.
Massive Open Online Courses (MOOCs)
The MOOC movement made education accessible to anyone with Internet access. According to Class Central, in 2019, there were over a million learners enrolled in MOOCs, with the top platforms being Coursera, edX, Udacity, and FutureLearn.
MOOCs use machine learning to analyze student inputs, grade tests, and other computer-based assignments, and in some cases, employ computer vision capabilities. In this way, machine learning allows MOOCs to support a large number of online students and allocate human resources to less routine activities.
MOOCs use machine learning to analyze student inputs, grade tests, and other computer-based assignments, and in some cases, employ computer vision capabilities. In this way, machine learning allows MOOCs to support a large number of online students and allocate human resources to less routine activities.
Massive Open Online Courses (MOOCs)
The MOOC movement made education accessible to anyone with Internet access. According to Class Central, in 2019, there were over a million learners enrolled in MOOCs, with the top platforms being Coursera, edX, Udacity, and FutureLearn.
MOOCs use machine learning to analyze student inputs, grade tests, and other computer-based assignments, and in some cases, employ computer vision capabilities. In this way, machine learning allows MOOCs to support a large number of online students and allocate human resources to less routine activities.
MOOCs use machine learning to analyze student inputs, grade tests, and other computer-based assignments, and in some cases, employ computer vision capabilities. In this way, machine learning allows MOOCs to support a large number of online students and allocate human resources to less routine activities.
Our Experience
The Fayrix team of 1500+ data scientists, analysts, and developers are ready to implement machine learning into projects of any scale — from initial data analysis and consulting to target models and software development. Our developers are vastly experienced in developing custom elearning solutions. Take a look at our case studies or contact us to learn how we can help your EdTech project grow.
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What Fayrix can help you do:
What Fayrix can help you do:
Create personalized learning pathways and improve learning outcomes through adaptive learning;
Recommend courses and curricula based on patterns in learner behavior;
Understand student performance and reveal new ways to assist students through in-depth comparative analysis and predictive modeling;
Highlight learners strengths and development areas to provide students with the assistance they need in choosing career paths down the road;
Predict learners success based on historical and real-time data;
Predict learner attrition rates and improve learning engagement;
Assist learners through text and visual content recognition capabilities;
Improve assessment and grading of students' performance;
Optimize management processes and resource allocation to meet the changing demand of students and staff.
Final thoughts
Final thoughts
Will machine learning lead to any change in education? The answer is, it already has. AI and ML in education bring significant benefits to online, blended, and face-to-face learning environments. The new technology opens up possibilities for educators to improve student engagement, reduce administrative workload, create better communication channels between lecturers and learners, and develop less biased grading systems. Not sure which technology you will need in your future project, check Fayrix's technological competencies and choose the best solution that will solve all your business goals.
It is high time for the education industry to embrace machine learning, and doing so can give you a competitive edge in the e-learning industry. But more importantly, it will provide you with the ability to make a difference for your students.
ML is not a silver bullet. To deliver real value, it needs to be implemented by a cross-functional team. Consider hiring a trusted partner, such as Fayrix, who fully understands the processes, workflows, and infrastructure required to solve your specific challenges. Our scrum offshore development team have experience in in developing machine learning projects such as machine learning hr applications, computer vision, fraud detection and others.
It is high time for the education industry to embrace machine learning, and doing so can give you a competitive edge in the e-learning industry. But more importantly, it will provide you with the ability to make a difference for your students.
ML is not a silver bullet. To deliver real value, it needs to be implemented by a cross-functional team. Consider hiring a trusted partner, such as Fayrix, who fully understands the processes, workflows, and infrastructure required to solve your specific challenges. Our scrum offshore development team have experience in in developing machine learning projects such as machine learning hr applications, computer vision, fraud detection and others.
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