How EdTech uses ML to create a personalized educational experience

How EdTech uses ML to create a personalized educational experience

Technological innovations go side by side with increasing the quality of education to improve students' personal, professional, and social development. Machine learning algorithms analyze how students perceive the information, allowing them to go back and repeat material or progress further. 

Unlike traditional teaching methods, where it might be difficult to check if everyone has absorbed the educational material, the use of machine learning in education gives the advantage of deeper information perception. 

Personalized learning as a new perspective in education

It is never too late to study. Everyone can be a student now, including adults who already have a family, a job, and possibly an academic background. For such students, learning with modern technology is the most comfortable way to get an additional diploma or skills and combine education with other daily tasks. 

According to Statista, the global education software market will be worth about $11.6bn billion by 2025.

ML-based software in education helps to test knowledge, act as individual virtual tutors, tailor courses, predict learning outcomes, advise an advanced curriculum, and recommend the most appropriate content, providing a personalized experience. 

It could be effective for public schools, colleges, universities, and courses, which often serve a diverse student population and provide a broader range of educational services. 

There are different approaches to personalized learning:

  • Adaptive learning

Students get necessary human and digital resources based on their unique needs.

  • Individualized learning

The pace of learning is adjusted relative to the needs of individual learners.

  • Competency-based learning

Students progress through learning relative to their ability to demonstrate competence,  knowledge, and skills.

Based on the performance history and the most meaningful and relevant learning activities, ML mechanisms will select the best approach to build the unique educational process and increase motivation to study. 

Benefits of personalizing the learning experience

  • Faster results. Based on each person's strengths, weaknesses, and qualifications, personalized methods save students time learning irrelevant content that does not match their level and experience. ML-enabled solutions provide more relevant educational data, stimulate students to interact more, and help to increase the rate of memorizing information.
  • Improved learning outcomes. ML algorithms predict results by tailoring content to the learner's experience and personal goals. For example, a student in an online course can point out a specific ability gap and then receive customized recommendations to enhance expertise. 
  • Tailored learning materials. ML helps create new content and select existing materials based on students' academic performance and individual requirements. This way, educational institutions and online portals can properly plan learning processes and provide personalized educational materials and recommendations. 

ML-enabled educational methods

Learning through online platforms

Modern online learning platforms provide access to the best professors and universities worldwide. They offer multilingual educational content, blended online and offline learning, and personalized experiences without geographic barriers. For example, many countries support teachers and trainers by providing online training to enhance information and communication technology (ICT) skills, help prepare online training materials, and run online classes, especially after COVID-19.

ML algorithms analyze the online course content, identify whether the information is relevant to current standards, and find if users understand what they learn. 

For example, Udemy, one of the largest educational marketplaces worldwide, unites students and teachers worldwide and offers more than 150,000 courses. By answering a few simple questions, members get an optimized set of suitable courses and relevant materials on demand. 

Carnegie Learning uses ML in its educational platforms for high school and undergraduates. The platforms offer solutions in math, literacy, and world languages. Among the key features of Carnegie Learning's platforms are simulated human tutors and one-on-one private instruction. The company has won numerous education awards, including "Best Artificial Intelligence/Machine Learning Application" at the Tech Edvocate Awards.

The University of California at San Diego used ML to create a platform for online bioinformatics courses. The main goal is to enable students to learn in their own way and achieve maximum results in a comfortable environment. The course curriculum adapts to the student's pace, personalizing data and providing as much information as they can understand at once.

All-round mobile learning

Mobile learning is part of e-learning, which gives 24/7 access to educational content via mobile devices. Mobile educational apps significantly reduce the dependence on a specific location and adapt the materials not only to the user's needs, lifestyle, and knowledge level but also to the fast-paced environment, as users mostly learn on the go.

Online educational resources Udemy and Coursera provide thousands of courses in various disciplines, available through web portals and mobile applications that allow students to continue learning whenever and wherever they are.  

ML-enabled educational apps increase students' engagement and improve their learning experience by personalizing the content they need according to multiple parameters and their learning behavior.

Another prime example of mobile learning is Duolingo. It is an app that helps learn foreign languages. Duolingo uses ML to predict the probability of remembering certain words or phrases. If the app detects that the user frequently misses these words or phrases, it recommends practicing them until the user becomes proficient. The app can also correct students if they make grammatical errors or mistranslations. 

Advanced grading

No learning process is complete without calculating grades. As each class and course reflects students' learning abilities, technology develops expertise in grading. ML pushes the boundaries of student achievement, allowing teachers to identify those who need more support and find students' pain points. Progress monitoring tools track growth and evaluate lessons more specifically to determine what practice works best. In the business world, this process has already been successfully used for quite some time. With the help of a wide range of data analysis tools for businesses that include advanced technologies such as ML, AI, and predictive analytics, organizations can monitor and optimize their daily operations to make better strategic decisions.

When it comes to teaching, Kahoot!, for example, offers a free platform that allows teachers to create and share quizzes to check who has mastered a topic or needs more work.

Quizlet comes up with an intelligent grading option. It goes beyond comparing students' answers to the correct ones. It analyzes the meaning of what is written and gives a fair grade, even if the answer is paraphrased or contains typos and minor grammatical errors.

The University of California at Berkeley has created the Gradescope tool, which grades students' work in voluminous courses. Teachers can set the assessment parameters and get an accurate picture of students' knowledge by adjusting multiple settings for each course.

ML for classwork personalization

Engagement level indicates teaching quality. It displays how students comprehend the material and tracks time spent on homework. It requires tools to measure activities during the learning process and to track the students' competence profiles, which will help improve their educational experience.

Knewton was one of the first companies that actively applied data analytics technology to education. It built an adaptive educational platform that connects to any modern learning management system (LMS).

The idea is that the application adapts to a student's unique learning curve, identifying each strength and weakness. Then teachers at Knewton create adaptive lessons to meet each student's needs.  

Some students have not decided what they want to study. This is especially true for schoolchildren who are about to enter universities. ML-based apps provide advice based on users' interests, test scores, and high school diplomas. 

SchooLinks is an ML-based platform with a curriculum that engages students in career education, planning, and college applications. The platform works with colleges and universities in many US states. ML algorithms use student data to personalize college recommendations and content to boost engagement. 

The bottom line 

These days, the most competitive characteristic of an educational project is the ability to provide customized content in the most favorable format for learning. 

The massive implementation of machine learning has changed educational services. As a result, we see learning become more individualized and personalized, offering students control over their preferences at the most convenient time and format. ML-based tools are driving a significant shift in the ability to improve learning outcomes.

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