Adaptive learning

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Adaptive Learning
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Definition of Adaptive Learning

• Adaptive learning or adaptive teaching is the
  delivery of custom learning experiences that
  address the unique needs of an individual through
  just-in-time feedback, pathways, and resources
  (rather than providing a one-size-fits-all
  learning experience).
• adaptive learning refers to an educational
  approach or system that leverages technology to
  provide personalized and tailored instruction
  based on an individual learner's needs,
  abilities, and progress. It utilizes data and
  algorithms to continuously analyze and adapt the
  learning experience to optimize learning outcomes.

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Enter adaptive learning technology

• Adaptive learning technology aims to emulate and
  support (not replace!) the talents of great
  educators to provide the best possible learning
  experience for every single student. It helps
  scale the benefits of adaptive learning to tens,
  hundreds, or thousands of students at a time.
• The mechanisms of how technology adapts to
  students has several variations

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Designed
Algorithmic Adaptivity
Adaptivity

A method of adaptivity in which an educator designs the expert teaching sequence to guide their learners toward content mastery. They drive how the lessons adapts feedback and content sequences in the lesson. Adaptations can occur based on a variety of factors called adaptivity factors. A method of adaptivity in which one or more algorithms answer the following two questions1. What does the learner 'know'?2. What should the learner experience next?Based on those answers, the algorithm can select the right item at the right time for students as they learn.
This expert-model approach to adaptivity tells the technology how to react in unique situations an "If THIS, then THAT" approach. It can be used to provide needed remediation, share advanced content with excelling learners, encourage people racing through screens to slow down, and more A common algorithm is is Bayesian Knowledge Tracing (BKT) which estimates the rate at which learning occurs. Another known framework is Item Response Theory (IRT), developed in the field of psychometrics to model the interaction of a learner with discrete items.




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Different adaptivity types and adaptivity factors
Adaptivity factors Adaptables
Learner information/profile, behaviours, and performance benchmarks that can be used adapt a learning experience, e.g. The ways the lesson can adapt based on the learner's performance. For example
Performance Was the correct or incorrect answer was provided? How has the learner done across a series of tasks/lessons? Real-time Feedback Offer appropriate help when it is needed most, e.g. hints based on a shown misconceptions (as if students are being guided by a tutor looking over their shoulder).
Behaviours How long did the learner take to complete the task? Differentiated Pathways Offer varying sequences of content to each student, e.g. provide extra help to a student for a particular concept before continuing to the next topic, fast-track advanced learners, or allow learners to decide what theyll learn next.
Information Learners can share how confident they feel in material, content type preferences, or personal data to alter what happens next.



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Adaptive learning in the global market

• Increased Demand The demand for adaptive
  learning solutions is on the rise as educational
  institutions, corporations, and online learning
  platforms seek innovative ways to deliver
  personalized and effective instruction. The
  global market for adaptive learning is projected
  to grow significantly in the coming years.
• Technological Advancements Advances in
  artificial intelligence (AI), machine learning,
  and data analytics have paved the way for more
  sophisticated adaptive learning systems. These
  technologies enable the collection and analysis
  of vast amounts of learner data, facilitating
  personalized recommendations and adaptive content
  delivery.
• K-12 Education Adaptive learning is increasingly
  being integrated into K-12 education systems
  worldwide. Adaptive learning software and
  platforms are utilized to individualize
  instruction, identify learning gaps, and support
  differentiated learning in classrooms. Teachers
  can access real-time data and insights to inform
  their instruction and provide targeted
  interventions.

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• Higher Education Adaptive learning is gaining
  momentum in higher education institutions, where
  it is used to personalize learning experiences,
  support self-paced learning, and improve student
  retention rates. Adaptive learning platforms
  offer interactive content, adaptive assessments,
  and personalized feedback to enhance the learning
  process in college courses.
• Corporate Training Adaptive learning is also
  being employed in corporate training programs to
  provide customized learning pathways for
  employees. Adaptive learning systems can identify
  employees' skill gaps and offer tailored training
  modules to address specific needs. This approach
  increases engagement, knowledge retention, and
  skill development in professional settings.
• Global Accessibility Adaptive learning
  technologies have the potential to bridge
  educational gaps and provide quality education in
  underserved regions. Online adaptive learning
  platforms can reach learners in remote areas,
  offering personalized learning experiences and
  expanding access to educational resources.
• Challenges and Opportunities While adaptive
  learning holds immense promise, challenges such
  as implementation costs, data privacy, and
  ensuring equitable access to technology need to
  be addressed. However, the global market for
  adaptive learning presents opportunities for
  education technology companies to innovate,
  collaborate with educational institutions, and
  provide scalable adaptive learning solutions.
• Reference
• Simplify Assessments with EasySIM Cloud The Best
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• Role of technology-induced training towards
  efficient competency building