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 solve 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 :

• Personalised 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 examination would be the new normal. AI and ML provides solution 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.

Abhimanyu Saxena, co-founder, InterviewBit & Scaler Academy, “We have been extensively using AI-based proctoring tools in our to ensure the authenticity of solutions submitted by applicants. For instance, in the Scaler Academy entrance test, the entire length of the test is tracked by proctoring tools that record everything. After the exam, all the submitted solutions are run through the tools that check plagiarism, the text, and also the semantic flow of solutions. We strongly believe that AI proctoring tools ensure a much more reliable and accurate examination process. Some of the companies that we work with are also using AI proctoring tools to conduct assessments on potential employees as part of their recruitment process.”

• Virtual Assistance : One of the key problems that faculties face in digital teaching is to provide 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 audios 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