Machine Learning Use Cases in eLearning
Online learning has shown significant growth over the last decade, as the internet and education combine to provide people with the opportunity to gain new skills. This tendency has been accelerated by the Covid-19 pandemic as more colleges, businesses, and institutions have shifted to remote work. Global Market Insights estimates that the eLearning Market surpassed USD 315 billion in 2021 and will grow at a 20% CAGR from 2022 to 2028. In this blog let us discuss a few machine learning use cases in the field of E learning.
We have observed the adoption of ML in eLearning applications as more and more people choose online learning over traditional learning methodologies. AI and ML in education have completely changed the way we teach and learn, from virtual classrooms to mobile digital courses to online references. Let us discuss some of the use cases for AI and ML as Ed-Tech companies increasingly use these technologies.
1. Personalized Learning:
Traditional learning methodologies involved a basic framework that a student had to follow to graduate or earn credits. For all students enrolled in that class, this framework essentially remained the same. The idea of personalized learning for each student was absent from the traditional educational system.
AI in education ensures that the educational software is personalized for every individual. Moreover, the system supports how the learner interprets various concepts with supporting technologies like AI and ML to adjust the learning path of each individual.
ML algorithms keep track of how students are learning and modify the curriculum to suit their requirements. The language-learning application Duolingo is a good example of this. It keeps track of the user’s grammatical or lexical errors and provides relevant activities to fill in knowledge shortfalls.
2. Course Recommending System:
There are several eLearning companies that provide a variety of courses. A lot of prospective students are unclear about what they want to study. This is where ML comes to the rescue. A recommender system build using ML provides unbiased advise, unlike a human, based on the user’s interests, test scores, academic qualifications, etc.
3. Using Chatbots as Virtual Instructors:
Chatbots are software built with ML and AI to interact with humans. This software can take the place of an eLearning instructor to clear simple doubts of a learner, thereby saving a lot of his/her time and effort. By using chatbots, simple questions from a learner may be answered immediately and at any time, something an instructor cannot accomplish.
4. Assignment Auto Grader:
ML technologies in eLearning provide an unbiased check of student’s knowledge. Instead of focusing on how effectively students recalled the material, they examine their answers to see how well they understood it.
In conventional learning, instructors usually prefer having the course assignments questions in MCQ format, since it is easier to evaluate. With ML technologies being integrated with eLearning, it has become easy to auto grade assignments irrespective of whether the type of question asked is MCQ or objective type questions.
An Ed-Tech firm called Quizlet offers its users this feature. Their smart grading option goes beyond merely comparing a student’s response with the accurate response stored in the database. Instead, even if the response is paraphrased or contains typos or small grammatical errors, the algorithm assesses the content of what is stated and provides a fair score.
5. Exam Proctoring:
There are questions about unfair methods being utilized to pass examinations and tests when they are given online. Using online proctoring methods will help control this. These systems can track browser activities, face and eye movements, record audio, etc. to identify if the learner has employed unfair means to pass the test.
6. Advanced Analytics:
It is challenging to assess a student’s engagement just on the basis of exam scores. It takes a lot of time and indicates very little about how engaged the learners are. On the contrary, analytics driven by AI may evaluate the amount of time spent on the exam, the number of attempts, and other performance-related criteria. This may be used to evaluate a learner’s progress as well as identify any gaps in the learner’s knowledge of particular course material and offer the best ways to address them.
In conclusion, an AI-based software and application solution may help an eLearning business in more ways than you would imagine. Since AI technology solutions successfully address a wide range of customer pain points, EdTech companies and businesses are drawn towards them. We may say that ML and AI have fundamentally transformed how we teach and learn. Despite this, there still remain difficulties, such as unmotivated students dropping out before finishing their coursework, students lacking in leadership and teamwork abilities, inexperienced instructors, etc. Businesses involved in eLearning must address these challenges in the future in order to realize the full potential of e-learning.
