The advice I got when I first started teaching was “make sure your zipper is up and don’t get any chalk on your butt.” No mention of student success. The advice I got when I worked as a database administrator was “anticipate the questions that management will ask; otherwise, you will have no answers.” No mention of business success. This book explores how to bridge these gaps in education, teaching, and learning, with the new knowledge and technology we have today.

A beginner’s guide to learning analytics is one of four volumes in Springer’s “Advances in Analytics for Teaching and Learning” series. The coauthors have impeccable credentials as researchers and educators. The book consists of 11 chapters. Foundational chapters introduce learning analytics, data requirements and educational data management, preparation and tools, and pedagogical approaches in the context of learning analytics. Additional chapters cover learning analytics for massive open online courses (MOOCs), other technologies for learning analytics, and how to move forward. Each chapter includes one or more case studies, and the text concludes with a separate chapter on “Case Studies” and another on “Problems.” The authors are pragmatic but inspiring.

Readers wanting to get up to speed on this hot topic should see Learning Analytics 101 from the Learning Analytics Research Network (LEARN) at New York University (NYU) [1]. Researchers are encouraged to develop and share an open student dataset, analogous to IBM’s Employee Dataset [2], to advance the state of the practice.

From a big picture perspective, can learning analytics help educators help more learners become better at word games such as Wordle? Likely so. Can it help develop more researchers and scientists who develop systems that can stop pandemics before they start [3]? Let’s make it so.