The term “adaptive learning” refers to the process of using algorithms to tailor learning to the needs of a person. It entails observing the learner’s actions in order to construct the interventions needed during the learning process.
For example, the algorithms can examine a learner’s responses during a test. As a result, the study materials can be tailored accordingly. Also, for questions that take longer to answer, these can provide supplementary and much simpler course information. Similar responses can be obtained when learners seek hints on questions.
Adaptive learning systems are created to dynamically modify the level or kind of course content based on an individual student’s talents or skill attainment, accelerating a learner’s performance with both automated and instructor interventions. The goal of these machine learning systems is to use proficiency to determine what a student really knows and to take students through a sequential learning route to achieve predetermined learning outcomes and skill mastery. First-generation digital learning systems will be transformed by these qualities.
Some of the benefits of adaptive learning include:
- Adaptive systems have the ability to overcome the basic and persistent difficulty in public education: the daunting task of teachers or faculty achieving learning mastery among a demographically varied group of students.
- Adaptive systems address fundamentally diverse levels of prior knowledge, as well as course content advancement based on students’ skill and outcomes mastery measurement, shifting faculty workload from teaching and remediation to teaching and facilitation.
- When used correctly, the cost savings from automated feedback and correction without the need for formal teacher engagement have resulted in significant increases in student learning.
- Students are in charge of their own education: real-time feedback on ongoing assignments provides precise feedback for self-mediation.
- Through automatic feedback cycles, adaptive systems empower students to take control of their learning and progress independently of the class instructor.
- Adaptive systems adapt to the diverse lifestyles of individual students rather than forcing them to comply with the system.
- Both staff and students are informed too late in the learning cycle by traditional assessment approaches. Adaptive systems can provide real-time information by using fast and thorough data-driven feedback.
By delivering a student-centric design, adaptive systems have the ability to transform education in the service of students. The student-centered capabilities that these systems strive to provide are substantially different from present classroom assistance approaches, which are heavily anchored in student administration.