The Learning Analytics Roadmap: SOLAR’s Handbook Of Hard Theory [LAR Series #11]

The Learning Analytics Roadmap: SOLAR's Handbook Of Hard Theory [LAR Series #11]

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SOLAR, the Society for Learning Analytics Research, has released the first edition of its “Handbook of Learning Analytics.” A 356-page volume, the handbook aims to provide an “extensive view” from leading experts in the field. It was conceived in 2014 at the Learning Analytics and Knowledge Conference. The Indianapolis gathering started the effort to bring “coherence” and broader impact to the field. As a result, the handbook includes four sections, which proceed in order of complexity. Its authors are aware of the rapid pace of evolution in the field, so the handbook focuses more on theories and long-standing principles, but it does also cover the development of applied cases.

The density of its theoretical content makes the handbook a key background resource. It does not offer straightforward advice. Rather, it outlines the key elements dominating the academic conversation. The fours sections in which it is organized reflect as much.

Foundational concepts

This section discusses the epistemological issues associated with the quantification of psychological activity. It stresses the importance of “constructs,” upon which experiments are based on. The section classifies the computational methods available, but does not discuss tools. A final chapter deals with ethical concerns.

Techniques & approaches

The nine chapters in this section include a general description of some of the existing techniques, with each getting its own chapter: Predictive learning analytics, explanatory models with examples, content analytics, natural language processing, discourse analytics, emotional models, and multimodal learning. Ideas on analytics dashboard and implementation design close the section.


Section three continues with more specific models and a few examples of practical research. It touches on some of learning analytics’ ongoing themes: big data, prediction, recommendations, sentiment analysis, video analytics, self-regulation, and more.

Institutional strategies & systems perspectives

The last section includes general chapters on technical and institutional issues. It reviews some of the existing model in architecture and policy. A chapter offers an academic’s view of advocacy for learning analytics. Another deals with replication and the process of turning analytics into science. Two chapters cover linked data, a proposed standard for managing information hosted across the internet.

In summary, the Handbook is trying to centralize the academic writing in learning analytics. It will be beneficial for interested researchers from faculties and centers. It is not recommended for developers and practitioners looking for a practical resource.

Access the Handbook at

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