Things you Should Know about Adaptive Learning

Personalized learning to reduce learning biases

Imaging 200 students from different backgrounds joining a course that will provide the scientific requirement that will give them the common set of skills for science curricula, including social sciences. This curricula includes some math, a survey of basic science and the scientific method, some logic and critical analysis, and a unit on quantitative reasoning. Naturally, some of these students may have an aptitude for logical thought, even if their basic math skills are weak. Others may have a shaky understanding of basic science concepts but are strong in statistics and research. Still, others may have weaknesses across the board, and a few are strong in all areas. For all students, the adaptive learning system provides content and sequencing appropriate for where they are in the curriculum. By using adaptive learning, the general shape of the course is the same for all students, but within that structure, frequent assessments give insight about how students are faring, pointing to optional paths that could help keep them challenged. Depending on their progress, students can see different learning activities in different media and order. Adapting to students learning is what is called Adaptive Learning.

What is Adaptive Learning?

Adaptive learning in its fundamental form is a learning methodology that changes the pedagogical approach based on the student’s input and a predefined response. Adaptive learning more recently is being associated with a large-scale collection of learning data and statistically based pedagogical responses. It can be seen as a subset of personalized learning that includes affective and somatic computing approaches.

How does it work?

The systems “learn” from student interactions and then adjust the path and pace of learning. The systems generally fall into three groups.

• Closed systems come with existing, off-the-shelf course content for rapid implementation.
• Open systems allow users to control all of the configuration and content decisions.
• Hybrid systems allow for limited configuration, such as selecting lessons to be included in the course or importing course content.

There is a movement toward the hybrid model because it balances the time needed to develop an adaptive learning course with the flexibility for course developers to control content and assessments.

For modules within the adaptive learning system, the learning content, the concept sequencing, and the assessments are set up to reflect the learning objectives for the course. Systems display content based on students’ performance of similar demographics or abilities, or they use predetermined learning paths based on assessment data. As students progress through a lesson, they may see information presented in various ways tailored to their learning needs.

Why is it significant?

Adaptive learning enables the delivery of personalized learning at scale, contributing to greater levels of academic success for more students in a cost-efficient manner while reducing cheating because the content and assessments can vary for each student.

Reference:

7 Things You Should Know About Adaptive Learning (educause.edu)

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