Breakthrough Approaches in Data-Driven Instructional Design That Optimize Teaching, Learning and Continuous Course Improvement

Audience Level: 
All
Institutional Level: 
Higher Ed
Abstract: 

To better address the critical need to improve student achievement, particularly course completions, higher education institutions are closely evaluating new technology-enabled learning solutions.  Leading among these are tools and services that enable adaptive learning and data-driven learning optimization – two strategies that uses computer algorithms to parse learning analytic data collected as students interact with online learning environments.  In this session, we will discuss strategies and best practices for implementing learning science-based design and data-driven learning optimization techniques at scale.

Extended Abstract: 

NOTE: I could not add the two presenters, as they were not in the system so adding them here and bios below.

Presenters:

Maria Anderson, Director of Learning Design, Western Governors University

Benny Johnson, PhD - Director of Research & Development, Acrobatiq

This presentation will discuss how advances in data-driven instructional design and learning analytics are enabling improvements in learning outcomes, particularly in high enrollment, general education gateway courses. Using predictive analytics, learning curve analysis and related education data science techniques, we will discuss and demonstrate new tools and services that are emerging to make learning science-based design and data-driven learning optimization more accessible to everyday educators than ever before.    Building on more than a decade of research from Carnegie Mellon University's Open Learning Initiative, a pioneer in cognitive and learning science research in online learning, data-driven instructional design methodology and adaptive learning technology  is proven to produce measurable learning gains for students, including improved retention and recall, faster course completion, and better long-term knowledge retention and retrieval.

Bios:

Maria Anderson: As Director of Learning Design at WGU, Dr. Andersen leads the university’s instructional design by creating innovative learning strategies to ensure seamless, intuitive, and engaging curriculum. She leverages research about learning, UI / UX, instructional design, motivation, and social engagement to create effective and relevant learning experiences across IT, Business, College, General Education, and Health Professions. Prior to this position, Anderson worked built educational games, directed MOOC design, built adaptive learning software, and spent 10 years in Academia. Dr. Andersen's Ph.D. is in Higher Education Leadership, and she holds other degrees in math, chemistry, business, and biology.

Benny G. Johnson, Ph.D., is Director of Research and Development for Acrobatiq. He holds Bachelor of Science degrees in Chemistry and Mathematics from the University of Kentucky, and received his Ph.D. in Theoretical Chemistry from Carnegie Mellon University.  For the past fifteen years, Dr. Johnson has worked in the field of artificial intelligence for education, leading the research and development effort of the Quantum technology for tutoring and assessment in chemistry, mathematics, accounting and special education.  He joined Acrobatiq in June, 2014 as Director of Research.

 

Session Type: 
Education Session - Individual or Dual Presentation