From: https://www.dreambox.com/adaptive-learning

Adaptive learning, also known as adaptive teaching, is an educational method that uses computer algorithms to orchestrate the interaction with the learner and deliver customized resources and learning activities to address the unique needs of each learner. In professional learning contexts, individuals may "test out" of some training to ensure they engage with novel instruction. Computers adapt the presentation of educational material according to students' learning needs, as indicated by their responses to questions, tasks, and experiences. The technology encompasses aspects derived from various fields of study including computer science, AI, psychometrics, education, psychology, and brain science.

Adaptive learning has been partially driven by a realization that tailored learning cannot be achieved on a large-scale using traditional, non-adaptive approaches. Adaptive learning systems endeavor to transform the learner from a passive receptor of information to a collaborator in the educational process. Adaptive learning systems' primary application is in education, but another popular application is business training. They have been designed as desktop computer applications, web applications, and are now being introduced into overall curricula.

What is Intelligent Adaptive Learning™? Unlike any other adaptive learning technology on the market today, the pioneering Intelligent Adaptive Learning™ of DreamBox Learning© platform adapts in real-time to every interaction a student makes, both within and between lessons.

From: https://www.dreambox.com/adaptive-learning

https://www.mheducation.com/ideas/what-is-adaptive-learning.html

History

Adaptive learning or intelligent tutoring has its origins in the artificial-intelligence movement and began gaining popularity in the 1970s. At that time, it was commonly accepted that computers would eventually achieve the human ability of adaptivity. In adaptive learning, the basic premise is that the tool or system will be able to adjust to the student/user's learning method, which results in a better and more effective learning experience for the user. Back in the '70s, the main barrier was the cost and size of the computers, rendering the widespread application impractical. Another hurdle in the adoption of early intelligent systems was that the user interfaces were not conducive to the learning process. The start of the work on adaptive and intelligent learning systems is usually traced back to the SCHOLAR system that offered adaptive learning for the topic of the geography of South America. A number of other innovative systems appeared within five years. A good account of the early work on adaptive learning and intelligent tutoring systems can be found in the classic book "Intelligent Tutoring Systems".

Technology and methodology

Adaptive learning systems have traditionally been divided into separate components or 'models'. While different model groups have been presented, most systems include some or all of the following models (occasionally with different names):

Expert model – The model with the information which is to be taught
Student model – The model which tracks and learns about the student
Instructional model – The model which actually conveys the information
Instructional environment – The user interface for interacting with the system

From: https://en.wikipedia.org/wiki/Adaptive_learning

Adaptive learning technology for math intervention

The same feedback that improves student learning success is also good for teachers. The ability to see current data allows teachers to understand each student’s performance.

Using current data as part of Multi-Tiered Support Services (MTSS) and Response to Intervention (RTI) helps identify students who are not making adequate progress in the core curriculum and are at risk for poor learning outcomes. Armed with true understanding, teachers can provide interventions appropriate to the student’s level of need and responsiveness.

Continuous formative assessments formed by adaptive learning systems throughout the learning process also help shape the process itself. Because every interaction is tracked in real-time, there is a parallel insight into student strategies. Then, based on that insight, individual learning paths are dynamically created to guide the student to advance through the curriculum.

https://elearningindustry.com/adaptive-learning-in-corporate-training-benefits-know

Evaluating adaptive learning systems and platforms

Many adaptive learning systems and platforms deliver textbook content at variable speeds but don’t have the ability to tailor learning and seamlessly provide an assessment. As you consider various adaptive learning programs, keep these criteria in mind:

Many different curriculum sequences – When teachers or learning guardians work one-on-one with students, they are able to change the sequencing of a curriculum in a way that makes the student’s learning experience most effective. It’s important that whatever adaptive learning system you choose, it’s able to accomplish the same feat.

Adjust to the pace of student learning – Research has shown that allowing students to work at their own optimal pace is an effective learning strategy. Students should progress through the system only after they have demonstrated mastery of the concept they are currently learning.

Take prior knowledge into account – Any adaptive learning program you choose should have the capability to target a student’s starting point based on prior knowledge and help that student make steady academic progress toward desired learning goals. This strategy prevents students who are struggling from becoming frustrated, and students who are gifted from becoming bored.

Strategies to increase student engagement – In a digital age when so many students are used to using technology in every aspect of their lives, gaming has been shown to be an important means of engaging students in learning. Adaptive learning programs that emulate strategy games help students see learning as something that is fun, not tedious.

Interactive support when problem-solving – Rather than telling students what they should do next, it’s important that the system emulates a live tutor, prompting students to rethink strategies that may not be working.

Customized presentation – Adaptive learning systems should customize the presentation of lessons to suit each individual student’s needs. By constantly analyzing students’ responses to and ways of thinking about problems, the presentation of new material is adapted to make sure it makes the most sense to that particular student.

Analysis of student solutions – An online learning platform that retrieves data based on student answers at the end of the lesson is not helpful for the student or the teacher. IALs interact with students as they solve problems, explore new concepts and make decisions, and they analyze the data in real-time to change their approach to instruction.

From: https://www.dreambox.com/adaptive-learning