How Machine Learning is Making Learning Interactive?
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Artificial intelligence (AI), an offshoot of computer science, incorporates machines carrying out tasks considered smart. Without being programmed, machines can learn things on their own if given access to data. This is called machine learning. It is a subset of AI, which has started to influence all aspects of our lives, including education. Artificial Intelligence in Education (AIED) has made adaptive learning and predictive analytics possible and opened new avenues to make learning fun and interactive. Adaptive learning has transformed pedagogy through computer-mediated personalized learning solutions that cater to the unique needs of learners.
The Need for Machine Learning
No concepts are isolated isles in the vast universe of learning. Only when students master fundamental concepts can they move forward in the learning process and understand other related concepts. However, if the basics are not solid, children often grow up with looming learning gaps, which ought to be identified and addressed at an early stage. Educators can use machine learning to provide a personalised learning environment and address the learning gaps at an early stage.
Personalized Learning Path
There can be multiple ways to gain the desired levels of knowledge. In a traditional learning method, students often have to adopt a linear path, but adaptive learning allows them to skip a few lessons if their learning progress supports the jump. Unlike sequential learners, who gain understanding through linear steps, global learners tend to learn with long jumps and gain knowledge in a seemingly random fashion. They are often able to solve complex problems once they have understood the bigger picture. Adaptive learning definitely benefits global learners. However, certain concepts cannot be skipped. For example, one cannot skip addition before understanding multiplication, but learners can tread on parallel pathways in their learning process. They can learn trigonometry and algebra simultaneously at their own pace. Adaptive learning maximizes learning in the least amount of time possible. Fast learners often become bored when not enough challenges come their way. With adaptive learning, they can learn at their own pace, as a personalized learning path is carved out for all types of learners.
Optimal Learning Tool
Once a system integrated with AI learns about the performance of students, it uses clustering algorithms to categories the students according to their learning capacity, needs, style and preferences. It then teaches itself to recommend students belonging to each category the shortest learning path based on the data pertaining to students from the same category. Richard Felder has grouped learning styles into four dimensions -- visual and verbal; intuitive and sensing; sequential and global; and active and reflexive. Lighting, sound and time also influence students' learning process and experience. While a student may read better in the morning, another student may grasp concepts better in the afternoon. Based on their preferences and learning styles, AI can suggest the best schedules and tools to learners. For instance, it can suggest audiovisual content to one learner and hands-on activities to another, depending on their requirements.
AI can make the learning experience engaging for learners by making them aware of their learning habits, encouraging meta-learning, and thus facilitating their holistic development.