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Elsevier Introduces Adaptive Study Solution for Improved Learning and Memory Retention for Nursing and Health Professions
Date:11/12/2013

"how to" applicable knowledge) only to a level of recognition. The Cerego Method teaches at a level of automaticity instead of just recognition to prepare students for real world performance
  • Market-leading Content: Key content, concepts, objectives and images will be gleaned from each text and will be organized by chapter in the respective text
  • Cerego's Patented Algorithms: The underlying set of rules decide which items should be presented to users over the duration of their learning experience and calculates the optimal moments to review/receive these items to convert them to stored knowledge

  • Elsevier Adaptive Learning uses scientifically-based principles to determine an appropriate schedule for each student to learn and review, helping to ensure readiness for exams and long-term success in his or her professional field. Additionally, the solution emphasizes individualized learning, cognitive skills training and time management. Product features also include instructor and student performance dashboards with at-a-glance access to data on individual knowledge usage and retention at the course and item level, as well as a mobile app.

    "The power of Elsevier Adaptive Learning is that it supports the needs of the individual learner. Students respond well to personalized learning tools that prepare them to not just pass their courses, but to become well-rounded professionals in the workplace," said John Danaher, President, Elsevier Education. "Cerego adds a technology for learning and memory to Elsevier's content that we believe will transform health/science education by delivering personalized, responsive solutions that directly support learning objectives. This is just one of the many parts of our overall initiatives to continue our leadership with adaptive technologies in health science education."

    Learn more about Elsevier Adaptive Learning, powered by Cerego, at

    SOURCE Elsevier
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