Technology has come up with brilliant ways to make sure that we keep progressing. In the field of education, it has injected itself into a students learning methods, and has played the role of a game changer. Technology, over the past years, has introduced the concept of personalized learning, wherein the learning pattern of a student would be studied and analyzed, further helping them through challenges they face.
The reason behind the rise of personalized learning is post secondary education. Online tools, such as Byju's and Khan Academy have become a part of its central component. These programs allow students at the college level to study through coursework at their own pace, and also integrate artificial intelligence to adapt to the learner's study pattern. As the coursework proceeds, the learner is presented with statistics that cover the student's strengths and weaknesses, along with the time taken to move through its modules.
Can this replace the role of a teacher?
At McGraw-Hill Education, hundreds of data scientists and analysts work to understand the science of learning, and have confirmed that teachers are here to stay. Yet their roles, will change. The impact created by personalized and adaptive learning should be to ease and speed up change. The change of introducing to the classroom a method of learning that allows students to learn something challenging and help them with their struggles. Schools have also started to embrace this new technology by naming it a teacher's companion.
Learning becomes efficient. Students are supposed to learn a lot of material. It can be tackled when there's nothing else in the picture, but as the day, week and semester pass by, it becomes challenging. Helping students track what they have mastered, and what they haven't yet, is one of the key features of adaptive learning. McGraw-Hill's adaptive learning platform (Connect) and artificial intelligence learning system (ALEKS) have used these concepts to offer a better understanding of the learner's interactions. These tools help to build a better understanding of the learner's interactions, which allows students to not only understand, but also come to class better prepared.
Cramming? Not anymore. Cramming does play an inefficient and ineffective role in learning. This can be avoided using the SmartBook platform, which helps students avoid this by allowing them to focus on what's important over time. Recharging selectively, based on the learner's individual needs, filters the student's learning objectives from earlier weeks that should be recharged. This may look easy, but is a monumental challenge to execute.
You're a teacher, with at least 30 students in your classroom. Which means its 30 different ways to understand and analyze what you teach. Imagine making a study plan that has to dig deep into each individual's study patterns to actually offer any help. To be able to make a personalized repetition plan for each student, taking into account how they fared in the past, is impossible.
Accommodating your way of thinking for problem solving. By the end of 2014, data scientists has realized that our thinking of how a student learns, was conservative. The below graph, credits to McGraw-Hill, illustrates the number of correct ways to solve an adaptive learning math problem by a 100+ college students.
To accommodate the ways a student gets from the problem at the top, to the solution at the bottom, seems like a nearly impossible task. This is most helpful as it allows the student to fully and freely plan steps on how to get from A to B. Without this freedom, students would be penalized for using a unique manner to solve a problem. The major advantage of such technologies is to help analyze individual differences in how students learn to achieve the best outcomes.
Personalized learning can truly transform the way students study when they don't have access to a teacher. Development of these cutting-edge technologies that discuss all these points can help create an environment addresses the full spectrum of subjects. Technologies have evolved to discuss computational based learning adaptively, and more exciting developments are well underway.
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