ML use cases in E-Learning
ML use cases in E-Learning
E-learning was initially conceptualised in the late 1990s, and many educational institutions throughout the world now acknowledge it as a valid method of instruction. Although the pandemic has functioned as a catalyst for change in the way education is delivered, its acceptance in India has been gradual, and it has yet to fully integrate into our educational system. Over the next five years, the market for global e-learning is expected to grow at a CAGR of 14.22% during 2022 –2027 reaching USD 220B in 2022. India is anticipated to reach 20M users in March 2022 with a CAGR of 44%.
Delivers Personalized Learning Paths to Reskill Employees
Continuous learning at the workplace is essential for the success of a business, more so because the half-life of skills is on the decline. For instance, what you learned 10 years ago is obsolete and less than half of what you learned 5 years ago is relevant today. So, there’s a need to continuously reskill employees to equip them for the current demands of the industry.
But how can you predict what your employees already know and what they don’t? Each one may be at different learning levels and may prefer different learning styles. An program that leverages adaptive learning machine learning algorithms can prove to be useful. In this kind of online training, learners are required to answer a structured set of questions. Based on their answers, content they are already familiar with is removed from the course and they are presented with content that they need to know. So each learner is taken on a personalized learning path to help them meet their current training demands.
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.
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.
Final thought
The inclusion of AI/ML applications in the e-learning industry is now at a stage beyond simply a ‘buzz’ and many educational institutions are readily adopting it. While these applications are quite useful on their own, the best results are attained when human instructors oversee the process.
Apart from the obvious benefits, AI/ML applications collect & generate a lot of user data which can result in privacy concerns in jurisdictions where data protection laws are not implemented. The truth be told, if the laws are to impede data collection & processing in every sphere, the digital bubble would burst!
Thank You………….
