Adaptive Learning Systems: Personalizing Education Through Machine Learning and Big Data
Year: 2026
Type: conference-paper
Abstract: With growing importance on technology as an interface of education, the teaching and learning process has transformed. One of the most promising innovations is adaptive learning systems - intelligent systems that incorporate aspects of machine-learning and big data to help make learning experiences more personal for students based on individual skills, needs and preferences. It is also a significant leap forward from traditional one-size-fits-all education strategies, where content, pacing, feedback and assessment are all individualized and personalized as they happen. Adaptive learning is simply the process to access and analyze large series of data generated by consequences of the learner’s activity on the digital platforms: styles and the traits, performance measures, time Merci 4 to 4 response behavior etc. Machine learning algorithms seek out of patterns to predict the behavior of the learner and determine the adaptations in the course, based on this new information. In other words, students receive personalised learning paths as well that change as their level and commitment to learning increase. This is where the big data comes in and can provide further support to the system in terms of increasing its prowess. Adaptive learning systems will somehow become increasingly sophisticated; they will be able to gather and analyze new kinds of information from different learning environments and will be able to create new and improved-informed learning profiles on individuals, to be able to benchmark that and identify students who are at risk of problems promptly. This not only helps in better academic performance but helps in timely intervention through, inter alia, educators. Real-time data analytics help create a feedback loop that is implemented at all times which is used by students and teachers to make adjustments and pursue ideal learning outcomes. Combined with machine learning, these systems will be able to modify itself as people are able to interact with them and these systems will be able to upgrade their models behind the scenes, in order to give relevant, easy to understand, and accurate answer. In the development of smarter, more adaptable and highly interactive learning platforms, technologies like neural networks, reinforcement learning and natural language processing are getting their share of usage.
Institution: Fergana State Technical University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7203667321