The problem with one size fits all is that it fits none! The idea of one teacher teaching 50 different students at the same time is a thing of the past. Customized learning is in and hence the market of learning Apps which facilitate such learning has also soared.
The reason why it works better is that it gives total control to the learner and everything else- exercises, the pace of learning etc. aligns itself to the learner’s preferences.
Why Customized Learning?
Are modern educators positive about learning to design mobile apps through peer support and instructor guidance? Are they considering visual programming tool for developing useful and fully functioning mobile apps? Recent studies show that they really do. The learning activities, including sharing customized apps, providing peer feedback, composing design proposals, and keeping design journals (blogging), complemented each other to support a positive sense of community and form a strong virtual community of learning mobile app design.
As for the students, throughout the semesters, they provide each other feedback regarding the customized apps, ideas for further customizations, design proposals, and final project apps. Students are also likely to share resources such as web tutorials and help answer questions on app programming and debugging.
For a variety of reasons customized learning has grown in popularity and appeal.
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Here are the top reasons:
Understanding the learner needs.
The most fundamental tenet of customized learning via learning apps is to understand each learner and his/her needs and provide customized solutions. An effort is also made to understand the academic objectives, learning styles and pace of learning. This is where AI comes in. Learning more about learner and offering customized, a tailor-made experience of studying is what this effort is all about.
A fine blend of IT and Training
This is what has served to break a new ground in instructional methodologies. Information technology has shattered the generic structures and created customized interfaces for everything. When it comes to learning, IT has done the same. It has emboldened the instruction specialists too and encouraged them to prepare modules which serve individual learners in a customized manner.
Benefits of Leveraging Al in Instructional Processes
It caters to the individual. It personalizes the experience. There’s nothing generic about it. It’s all tailor-made. Since IOS vs Android development is going on, it would be interesting to see which one provides better customization. It very well may be overwhelmingly troublesome for one instructor to make sense of how to address the issues of each understudy in classrooms. Human-made intelligence frameworks effectively adjust to every understudy’s adapting requirements.
This helps a great deal to learners as they can get a lot of support while they are grappling with different subjects. They get customized support and inputs, based on how Al learns more about the learner and processes the learning style. Internet of Things security works in a way to connect information all around and facilitate the flow of inputs in a constant manner.
Taking Grading System a Notch Higher
With Al and advanced IT processes in place, grading has gone to the next level of sophistication. Different aspects of grading have undergone several refinements on account of how Al can provide deeper analysis of learner’s performance. They can assemble information about how understudies performed and even consider more unique appraisals, for example, papers.
If a learner is able to answer certain inquiry kind of questions and not able to attempt analysis kind of questions, it’s easy to spot it now with Al. It means you can put the scores under a microscope and see what’s in there.
- Constant and Constructive Feedback
It’s now possible for the learner to get run-time and constant feedback on his/her learning and scores. Computer Based Learning Structures. Here’s a blend of computer and its structures and social collaboration. It allows social interaction to contribute to the quality of learning.
Assessment and evaluation will undergo the transformation next. How essay and inquiry-based questions or filled-in questionnaires are to be evaluated using AI is being studied at the moment.
Mining of Learner Information
With data mining tools, it would be possible to determine whether the learner is on the right course of action or is drifting toward web-based resources. It would be possible to provide feedback and inputs to check it.
Improving it on a continuous basis.
Like Coursera does it, it would be possible to send auto-notifications for every wrong answer and supply information regarding the correct one. It sends a notification to the instructor as well as the learner.
With Al and mobile Apps, it would be possible to make learning learner-centric and provide an overhaul to the existing generic instructional structures. It would be possible to make learning customized in the future.