© 2014

Learning Analytics

From Research to Practice

  • Johann Ari Larusson
  • Brandon White

Table of contents

  1. Front Matter
    Pages i-xii
  2. Johann Ari Larusson, Brandon White
    Pages 1-12
  3. Preparing for Learning Analytics

    1. Front Matter
      Pages 13-13
    2. Abelardo Pardo
      Pages 15-38
    3. John T. Behrens, Kristen E. DiCerbo
      Pages 39-60
    4. Ryan Shaun Baker, Paul Salvador Inventado
      Pages 61-75
  4. Learning Analytics for Learning Communities

    1. Front Matter
      Pages 77-77
    2. Matthew D. Pistilli, James E. Willis III, John P. Campbell
      Pages 79-102
    3. Andrew E. Krumm, R. Joseph Waddington, Stephanie D. Teasley, Steven Lonn
      Pages 103-119
  5. Learning Analytics for Teachers and Learners

  6. Back Matter
    Pages 191-195

About this book


In education today, technology alone doesn't always lead to immediate success for students or institutions. In order to gauge the efficacy of educational technology, we need ways to measure the efficacy of educational practices in their own right. Through a better understanding of how learning takes place, we may work toward establishing best practices for students, educators, and institutions. These goals can be accomplished with learning analytics.

Learning Analytics: From Research to Practice updates this emerging field with the latest in theories, findings, strategies, and tools from across education and technological disciplines. Guiding readers through preparation, design, and examples of implementation, this pioneering reference clarifies LA methods as not mere data collection but sophisticated, systems-based analysis with practical applicability inside the classroom and in the larger world.

Case studies illustrate applications of LA throughout academic settings (e.g., intervention, advisement, technology design), and their resulting impact on pedagogy and learning. The goal is to bring greater efficiency and deeper engagement to individual students, learning communities, and educators, as chapters show diverse uses of learning analytics to:

  • Enhance student and faculty performance.
  • Improve student understanding of course material.
  • Assess and attend to the needs of struggling learners.
  • Improve accuracy in grading.
  • Allow instructors to assess and develop their own strengths.
  • Encourage more efficient use of resources at the institutional level.

Researchers and practitioners in educational technology, IT, and the learning sciences will hail the information in Learning Analytics: From Research to Practice as a springboard to new levels of student, instructor, and institutional success.


Data Mining and Education Knowledge Assessment in Education LA and Education Learning Analytics and Education Learning Analytics in CSCL Predicitve Modeling and Education

Editors and affiliations

  • Johann Ari Larusson
    • 1
  • Brandon White
    • 2
  1. 1.Center for Digital Data, Analytics and Adaptive Learning at PearsonBostonUSA
  2. 2.Dept. of EnglishUniversity of California BerkeleyBerkeleyUSA

About the editors

Johann Ari Larusson is a Senior Research Scientist in the Center for Digital Data, Analytics and Adaptive Learning at Pearson. He first joined Pearson as a Senior Recommendation Engineer where he led research and development of recommendation engine technologies and adaptivity and analytics for the Alleyoop product. His research and applied work are primarily in the fields of educational technology, learning analytics, computer-mediated collaborative learning (CSCL) and software engineering. Prior to Pearson, he held various R&D positions in the technology industry, academia and banking. Even prior to educational technology, his work centered around transmission, manipulation and analysis of large volumes of (unstructured) data. Johann has authored and co-authored several peer-reviewed and award-winning publications, was a founding member and chair of the First North East Regional Learning Analytics Symposium, and has served as a reviewer for a number of journals and conferences. He holds a Ph.D. in Computer Science from Brandeis University.

Brandon White is a doctoral candidate in English at the University of California, Berkeley, where his theoretical interests in pedagogical history intersect with inquiries in applied educational technology. His work and research in learning analytics primarily concerns language recasting as deployed in student writing, and as taken up by educational theory, linguistics, and psychology. He is particularly invested in developing methods for identifying, isolating, and weighing markers for students’ success based on their written content. Before coming to Berkeley, he was a Teaching and Learning Fellow at Brandeis University, where he also earned his Master’s degree in Cultural Production, and continued to work as a consultant in educational technology for Research and Instruction Services. He is the author of a number of peer-reviewed publications and has served on several advisory committees for teaching and learning in higher education.

Bibliographic information


“This book is an edited volume concerning the current emerging topics of learning analytics (LA). … The book attempts to provide the first comprehensible reference book for LA by showcasing results, strategies, guidelines, methods, models and tools. … Larusson and White have produced an accessible and interesting introduction to LA, aimed at students, educators, researchers and educational stakeholders.” (Cristóbal Romero and Sebastián Ventura, Technology, Knowledge and Learning, Vol. 20, 2015)

“The book presents a range of excellent topics that are all interlinked with learning analytics. Each paper has a unique perspective on the topic and provides suitable examples and case studies. I would highly recommend this book to educational professionals seeking an informed view on learning analytics.” (S. M. Godwin, Computing Reviews, November, 2014)