Online Learning, the official journal of the Online Learning Consortium (formerly Sloan-C) promotes the development and dissemination of new knowledge at the intersection of pedagogy, emerging technology, policy, and practice in online environments. This special issue is dedicated to scholarly research on the use of learning analytics within online teaching and learning. Learning analytics involves the collection of data to analyze relationships relating to learner characteristics, their learning processes (interactions with content, other learners and educators) and outcomes for the purpose of understanding, informing and improving student learning, retention, and other desired results (Long & Siemens, 2011). Recent development, innovation, and adoption of learning analytics has allowed educators to leverage big data to provide more tailored support for learners, including: early identification and intervention to increase success and retention for at-risk students, personalization of content and interactions, and individuated feedback to student and instructors.

The focus of this special issue of Online Learning is to present rigorous empirical research and theory-based work on the use of learning analytics in online education in the areas of data capture, processing and reporting approaches, applications of learning analytics in practice, processes for scaling, and explorations of ethical issues. Potential topics include, but are not limited to: the use of learning analytics in developing effective models for identifying at-risk online students; the creation of analytics that provide feedback to students and/or instructors on learning processes; the use of analytics for adaptive learning or personalized recommendations; how to scaffold the use of analytics for faculty and/or students; and institutional readiness and scaling of learning analytics efforts. The ultimate goal is to advance our understanding of the particular issues involved in developing and implementing learning analytics in the context of online learning environments.

Submissions Due: September 30, 2015

For questions please email

Guest Editors:

  • Patsy Moskal, EdD, University of Central Florida
  • Matthew Pistilli, PhD, Indiana University-Purdue University Indianapolis
  • Karen Vignare, PhD, University of Maryland University College
  • Alyssa Wise, PhD, Simon Fraser University

To submit in response to this call for papers please read about the journal:

Then navigate to the submission site at

 Be sure to select “Special Issue on Learning Analytics” from the Journal Section dropdown menu when submitting your manuscript.