Learning With Machines: How Machine Learning Will Influence The Future Of Education
So before you ask how Machine Learning is going to change the face of education, we need to understand how we learn, and the most important thing to understand here is that we all learn differently.
Imagine you’re fifteen and you’re heading to Delhi next week for the first time for a school trip to Rashtrapathi Bhavan and Sansad Bhawan. The following week you have a paper to submit on the way the Indian Parliament functions. How are you going to prepare? How do you make the most of trip? Imagine again you're a corporate banker and have been suddenly placed on oil refinery infrastructure project and you need to learn about the entire industry in three days. How would you ensure you knew what you needed for the job at hand?
Traditional Learning Methodologies are outdated
So before you ask how Machine Learning is going to change the face of education, we need to understand how we learn, and the most important thing to understand here is that we all learn differently. Some of us work well in groups, while others prefer to work in silence. Some of us are tactile in that we like to touch and feel to imbibe, while others prefer to imagine. Learning styles are as different as our personalities and yet today we still teach young children, and train adults, like we are all the same. So it’s no wonder some of us do well, while the rest of us flounder not because we’re unintelligent, but because we all have different learning styles. Many pedagogues have tried to change this problem be it Steiner or Montessori, online or blended but our biggest obstacle remains both price and content. It’s just too expensive and time-consuming to constantly adapt the curriculum to suit the needs of one person. If our student-teacher ratio was indeed one-on-one, every child/adult would learn efficiently assuming, of course, the teacher also appreciates the learning of the student. In a perfect world, you would learn what you want, when you want, in the matter you best learn in. Pipe dream? Not anymore. Machine learning could possibly deliver that.
What is Machine learning?
Machine learning is not just a computer replacing a teacher. Machine Learning is the application of artificial intelligence to computer programs. Complex algorithms focus on developing computer programs that evolve with student interaction, learning from and with the student thereby making information more accessible and useful. The programs use these algorithms to observe, capture trends, recognize patterns and then design teaching programs and curriculums that make the most sense. So let’s go back to our earlier example. You're fifteen and heading to Rashtrapathi Bhavan and the computer asks you some basic questions about your preferences after which it perhaps realizes you learn best from cartoons and so shows you a cartoon that explains our political system and then because it finds you concentrate best between 7 and 9 pm (teenagers tend to become nocturnal creatures) creates a learning system that caters to your time preference and perhaps develops a game to keep you interested while feeding current daily headlines into the content to make it relevant. So suddenly the fifteen years old is discussing the political system and daily headlines with his or her parent at dinner. But another fifteen years old prefers to tactile learning, so perhaps the program logs into Walmart - Flipkart and buys the most recent book on the subject- delivering it in an hour.
The primary aim of machine learning is that computer programs learn automatically without human intervention. This is imperative to the field of education where everyone learns differently. It helps harmonize the goals and objectives of both teachers, as well as students, making it easier to create personalized learning experiences because the machine is constantly learning. Machine Learning is also playing a critical role in shedding the one-size-fits-all style of teaching to adopt a more personalized style of pedagogy that caters to the learning needs of each individual student. Not only are the learning speeds and styles of each student different, their knowledge and educational backgrounds are also usually quite diverse. This is where its algorithms, through digital and adaptive learning tools, can identify the studying pattern of a student, along with their strengths and weaknesses. This can aid them in creating and launching tailored and customizable solutions to make the entire learning process favorable to each student’s aptitude.
Even in a more traditional learning model - ten students to one, teachers still have more access to data. As teachers gain greater access to vast amounts of data on the content students consume, and their learning progress for various subjects and topics, the insight derived from the analysis of these data points allows teachers to adjust their lessons accordingly and help weaker students from falling behind on their course schedule. Predictive algorithms can also create a suitable learning path for them when they are studying. As students navigate the course and curriculum with the help of adaptive learning software, algorithms can simultaneously deliver additional content for the learners that helps strengthen their grasp on a particular concept they are trying to understand. AI can identify gaps in instruction and understanding of the course content by analyzing student performance on assessments. For instance, if a substantial number of students incorrectly answer a specific question, the AI algorithm can identify which concepts students are unable to comprehend and then make necessary modifications to the learning materials and methods to drive a better understanding of the subject.
Furthermore, students can be provided immediate and appropriate feedback, helping them to overcome the fear or hesitation when it comes to approaching teachers with their doubts. In a typical classroom environment, students may not like to receive critical feedback in front of their peers. However, with AI-enabled learning systems, students can easily receive feedback through private communication channels. With the help of this feedback, they can approach the subject in a different manner and improve their performance in assessments. This way, higher education institutes can enhance student success rates and devise the right strategies to reduce churn and boost student retention rates.
Imarticus Learning, for instance, incorporates an advanced set of Machine Learning algorithms in its learning management system (LMS). The LMS, designed based on the philosophy of experiential learning, helps learners manage and improve their performance constantly by ‘studying’ and ‘reviewing’ through the duration of the course. At the same time, the LMS enables the faculty and students – irrespective of where they are located – to connect, collaborate and share feedback related to the course content, performance, etc. on a real-time basis.
The concept of Learning Purpose - relevance and Just-In-time learning
Another major issue is relevance. Our traditional learning methods were built before the time of not just the internet but computers themselves. Education has failed miserably with respect to harnessing the power of technology in education. For instance, we still have massive textbooks and children and university students carrying backpacks yet they spend all their time on their smartphones on social media. And we wonder why they’re not reading. Others like the tactility of textbooks but wish they were more up to date. Obsolescence is becoming synonymous with traditional teaching methods and schools.
Obsolescence not just in content but type of content. Let’s go back to the corporate banker. What does she really need to know about the Oil Refinery business? If she has three hours- how much of that should be about the chemistry of oil and how much of that should be about the business of it, the costs involved and the global oil market. Machine Learning makes this just-in-time learning possible. The algorithms decide what you need to know depending on your learning purpose. Learning purpose is something we completely ignore. Why does someone who needs to spend two days in Paris need to know how to conjugate verbs?
But Machine Learning is not without its challenges. What if we have a world where everyone is a jack of something and a master of none? How does all this increased technological interaction affect our brains, especially those of young children when doctors constantly warn us about the hazards of screen time? Our second challenge is that of content creation. Who is going to create this content? And what is going to happen to our teachers? And finally for the human element, what about the benefits of the classroom and human interaction? There’s this scene from the first Star Trek where Spock is learning in a halved sphere set in the ground, a solitary almost confined space. So perhaps Machine Learning will result in a Vulcan mindset, exceptionally smart but lack in emotional intelligence. Or perhaps not. What we do know is that Machine Learning is not an ‘if’ but a ‘when’ and the culture that adopts it the fastest will win the race in education.
Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house
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