learning analytics

A technology to improve STEM retention?

Educational data mining, learning analytics will prove invaluable in helping improve students' course retention.

Dave Johnson, Director of Research and Analytics for Colorado State University Online, which has been leading CSU’s learning analytics research, said: “Through our collaboration with McGraw-Hill Education, we are looking at how we can provide instructors with actionable, data-driven insights that will allow them to help students, at all levels, successfully complete their courses. We are already starting to see some exciting results and look forward to incorporating our findings in new practical applications.”

The research study is focusing initially on testing and validating predictive models for course completion. The predictive model will be combined with a set of interactive insights for advanced diagnostics and intervention. These patterns can serve as early indicators as to which students will most likely complete a course and which ones are in danger of failing–and ultimately help instructors identify at-risk students that they can work with more closely to ensure course completion. Preliminary results from the research project are expected in the first half of this year.

“The mission of our Learning Science Research Council is to promote further research and exploration into the science of how students learn to help inform the continued growth and refinement of technology-supported learning in the years to come,” said David Levin, president and CEO of McGraw-Hill Education. “We are seeking a wide range of collaborations with institutions like Colorado State University to deepen our collective understanding of how to effectively use technology to improve learning outcomes. We want to make our researchers and extensive resources available to test new theories and content in real-world educational settings – to solve real-world education problems.”

McGraw-Hill Education’s Learning Science Research Council is focused on four key areas of research:

  • Learning Analytics: applying data science to generate predictive models and actionable insights for learners and instructors
  • Learning Algorithms: creating personalized algorithms based on learning science to help students learn better
  • Learning Quality: applying statistics rigor to evaluate and improve the quality of learning content and assessments
  • Learning Efficacy: incorporating causal inferencing and modeling methodologies for establishing learning efficacy

The Council draws upon the collective expertise of McGraw-Hill Education’s own senior researchers, who guide the company’s learning science research initiatives, as well as a leading group of researchers, scientists and academics committed to examining the use of technology in improving learning outcomes. The Council’s external advisory board members include:

  • Dr. Ryan Baker, Associate Professor, Graduate School of Education, University of Pennsylvania
  • Robert S. Feldman, Deputy Chancellor and Professor of Psychological and Brain Sciences, University of Massachusetts Amherst
  • Dr. Xiangen Hu, Professor, Department of Psychology, University of Memphis, and Dean of Psychology, Central China Normal University
  • Richard Larson, MITSUI Professor of Engineering Systems, MIT Institute for Data, Systems and Society, Massachusetts Institute of Technology (MIT)
  • Rosemary Luckin, Professor of Learner Centred Design, UCL Knowledge Lab

Material from a press release was used in this report.

Laura Ascione

"(Required)" indicates required fields