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  1. Christine Massey
  2. http://www.ircs.upenn.edu/pennlincs/html/index.html
  3. Director of Research & Education, Institute for Research in Cognitive Science
  4. Presenter’s NSFRESOURCECENTERS
  5. University of Pennsylvania
  1. Philip Kellman
  2. https://www.psych.ucla.edu/faculty/page/kellman
  3. Distinguished Professor, Psychology Department
  4. Presenter’s NSFRESOURCECENTERS
  5. University of California, Los Angeles
  1. Everett Mettler
  2. http://kellmanlab.psych.ucla.edu/
  3. Postdoctoral Researcher
  4. Presenter’s NSFRESOURCECENTERS
  5. University of California, Los Angeles
  1. Rachel Older
  2. http://kellmanlab.psych.ucla.edu/
  3. Lab Manager
  4. Presenter’s NSFRESOURCECENTERS
  5. University of California, Los Angeles

Adaptive Sequencing and Perceptual Learning Technologies in Mathematics and S...

NSF Awards: 1109228

2015 (see original presentation & discussion)

Grades K-6, Grades 6-8, Grades 9-12, Undergraduate

This research and development collaboration between labs at UCLA and the University of Pennsylvania creates and studies STEM learning technology that integrates principles of perceptual and adaptive learning based on well-established research in cognitive science to help students become more effective and efficient learners. Perceptual learning accelerates learners’ abilities to recognize and discriminate key structures and relations, helping students develop expert-like abilities to recognize and extract important information in complex science and math representations, such as models of molecules in chemistry and algebraic equations in math. Adaptive learning algorithms use a constant stream of individual performance data, combined with principles of learning and memory, to adapt the learning process to each individual. These algorithms, known as the ARTS system (Adaptive Response Time-based Sequencing), continuously track the speed and accuracy of a learner’s responses to different types of problems and use the learner’s own data to optimize how items should be spaced and sequenced in during learning. The software also uses the student’s data to guide him or her to objective mastery criteria. The methods and algorithms we study are currently being instantiated in web-based Perceptual and Adaptive Learning Modules (PALMs) in areas including K-12 mathematics, high school and community college chemistry, and medical education.

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