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Can Big Data Transform Adaptive Learning Systems?

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According to a report by New Media Consortium, adaptive learning (AL) and learning analytics are two crucial developments emerging in the educational technology market. Today, students pay more and more attention to individualized learning and instruction. If you are one of the higher ed institutions ramping up efforts to improve learning outcomes, implementing adaptive learning systems can be the potential solution. In this article at Hackernoon, Shannon Flynn explains how big data shapes AL.

What are Adaptive Learning Systems?

AL is an online educational system that focuses on understanding the student. Furthermore, it modifies the learning materials based on the learner’s progress. The learning system keeps track of what the student does, analyzes their actions, and adapts the training to better suit the learner’s requirements. For example, AL provides the most relevant training materials to those that do not perform well. This improves efficiency by allowing mentees to focus more on in-class activities, peer learning, and interaction. Additionally, adaptive learning systems use performance metrics and students’ cognitive skills to rationalize content and suggest better learning modules.

Big Data’s Role in Adaptive Learning Systems

“Big data is a valuable resource for any institution in the educational sector. Essentially, institutions can uncover greater insight into student performance by analyzing data amounts that are too large for traditional computing processing methods,” says Flynn. Big data allows educators and administrators to make informed decisions and answer questions they did not know existed.

Potential Challenges

  • If education institutions implement AL, teachers must present multiple iterations for a course topic to accommodate students with different learning styles.
  • Dealing with extensive data is another challenge. AL offers a large amount of data, including when they logged in, what topic they repeated, and more. It is up to ed-tech leaders to find the relevant data points they value.
  • Algorithms within AL solutions can vary and can create gaps in their effectiveness.

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