AI and adaptive learning have a positive correlation, but how does one combine them for success in the classroom? Let us see how.
A customised method of teaching called adaptive learning employs data analytics to adjust the curriculum to meet each student's unique needs. Systems that use adaptive learning use machine learning and data science to give students immediate feedback and adjust to their individual learning styles. As a result, adaptive learning systems can pinpoint each student's areas of strength and weakness and deliver tailored instruction to boost academic performance.
Benefits of adaptive learning
Adaptive learning analytics can also be used to track students' development and give teachers information about their individual learning styles. By making it simpler to access pertinent information about students and identifying their areas of strength and weakness, adaptive learning analytics can improve student success. Adaptive learning analytics can help students who may be in danger of falling behind by identifying them and offering extra assistance. This will boost their academic performance. Personalised course instruction using adaptive learning analytics is useful for teachers and students.
How adaptive learning works
Adaptive learning analyses student learning patterns and data to provide tailored content and feedback to optimise their learning experience. It can be achieved through adaptive models, such as technological programmes and artificial intelligence systems. Take, for instance, gamified learning, where learning objectives are levels. The student then needs to climb up the levels to complete all learning objectives. It is also possible to use a real-time digital assessment tool that gives out an exercise and automatically grades it. Adaptive learning leverages data analytics to provide customised resources and activities to address each student's unique learning needs.
Challenges of adaptive learning
Although this is a very effective method of instruction, there are some difficulties. The first problem is that technology and the brain lack cognition, which reduces adaptive learning systems. Another challenge is designing and constructing adaptive learning systems that successfully address each student's needs. The number of students can also affect how effective adaptive learning is. This is because it gets increasingly difficult for teachers to effectively manage and adjust to the needs of each individual student as the number of students rises. Despite these difficulties, UK higher education institutions are becoming more and more interested in adaptive learning as a cutting-edge, data-driven method of instruction.
Conclusion
Although there are some implementation difficulties, adaptive learning has many benefits and is regarded as the way forward for efficient instruction and training in a hybrid workplace.
