ML use cases in E-Learning

ML use cases in E-Learning

Introduction?:

E-learning, as a concept, was first introduced in the late?nineties, and, since then, it has been an accepted mode of learning in many educational institutions across the?globe. In India, however, its adoption was slow, up until the pandemic has acted as a catalyst for transformation in the way education is imparted, though it is yet to become a robust feature of our education system. Global E-learning is estimated to witness an 8X over the next 5 years to reach USD 2B in 2021. India is expected to grow with a CAGR of 44% crossing the 10M users in?march 2021.

The online learning industry is growing stronger with the help of technologies like machine learning and AI. Machine learning is a sub-division of artificial intelligence. The strength of the system lies in its ability to identify patterns and trends in the data and, based on those patterns, to make predictions that can benefit humans. There is huge potential in leveraging technology for the bottom of the pyramid and solving real-world problems. The challenge, as of today, lies in developing the infrastructure of developing economies.

Real-Life Use Cases Of AI & ML In Education?:

  • Personalized And Adaptive Learning:?Artificial intelligence and machine learning can be of great help in this field. AI not only provides highly customized learning for special children, it also learns from the responses to work on specific areas and improves them while considering the student’s learning speed and time.?Machine learning algorithms use pattern recognition to predict outcomes. For example, It can spot when a student repeatedly struggles with a concept, and the system can adjust the e-learning content to provide additional, more detailed information to help the student. So an online student who has not mastered the basic concepts needed to continue a course may receive specific course material to help the student catch up , this will help schools to compensate for the extra effort needed in the new normal by using it as a tool for teachers, to make data-driven decisions in responding to kids with special needs.
  • Digital Exams And Assessment:?The lockdown impacted examinations, and in most cases, schools had to either cancel or postpone the exams. Within some time, institutes realized that online examinations would be the new normal.?AI and ML provide solutions to evaluate online exam environments through retinal tracking, environment stimulus tracking, and IP tracking. The data generated through such digital examinations combined with the power of machine learning will auto-generate evaluation papers as well as a course of action for each student to help teachers focus on the facilitation part.
  • Virtual Assistance:?One of the key problems that faculties face in digital teaching is providing live feedback or support to student queries. By leveraging AI and ML, chatbots can act as virtual assistants and solve real-time queries. This allows a teacher to dedicate more time from admin tasks to actual lesson planning. Many learners struggle to grasp some concepts from the first run. It’s good when the content is pre-recorded, so they can repeat watching the videos or listening to the audio until they sort everything out. But during live webinars and other training sessions many people just don’t ask their “stupid” questions. AI-powered chatbots solve this challenge too, as the learners can ask as many questions as they want without interrupting the lecturer and get detailed answers as many times as they need.
  • Automate Time-Consuming Administrative Tasks:?Machine learning can free lecturers and administrators from time-consuming busy work. For instance, machine learning algorithms can help to automate scheduling and content delivery processes. Scheduling coursework for online learners is a tedious and time-consuming task that can’t be avoided. In the near future, artificial intelligence, through the application of machine learning, will liberate professionals from dull tasks allowing them to proceed with more high-level and satisfying work.
  • Natural Language Processing: Applying NLP solutions to transforming speech into text, enabling voice recognition and translations enables educators to teach learners from all over the world, greatly increasing the eLearning potential as an educational instrument. Using machine translation enables the users to better understand the language, grasp its grammar peculiarities, learn correct sentence structure and improve their vocabulary.

References?:

  • https://www.nagarro.com/en/blog/ai-ml-education-real-life-use-cases
  • https://academysmart.com/ai-ml-in-elearning/
  • https://www.thetechedvocate.org/4-ways-that-machine-learning-can-improve-online-learning/
  • https://timesofindia.indiatimes.com/home/education/news/e-learning-trends-to-focus-on-in-2022/articleshow/88955275.cms

要查看或添加评论,请登录

Avinash Yadav的更多文章

  • ML use cases in Ecommerce

    ML use cases in Ecommerce

    E-commerce — is one of the first industries that started using all the benefits of machine learning. Nowadays, there…

  • Ola trip match making Algorithm

    Ola trip match making Algorithm

    Today, booking cabs by mobile is a common practice. Companies like OLA and Uber have been very successful in this…

  • MakeMyTrip Dynamic pricing

    MakeMyTrip Dynamic pricing

  • ML use cases in E-Learning

    ML use cases in E-Learning

    Introduction Machine learning and e-learning go hand in hand with the education transformation. E-learning, as a…

  • ML use cases in HealthCare

    ML use cases in HealthCare

    Machine learning is aimed at training models to begin recognizing patterns using training data. In healthcare, machine…

社区洞察

其他会员也浏览了