Review of Siemens's "Learning analytics: the emergence of a discipline"

Citation
Siemens, G. (2013). Learning analytics: the emergence of a discipline. American Behavioral Scientist, 57(10), 1380-1400.

Summary
Learning analytics is a rapidly emerging field that seeks to provide better understanding of the behavior and effectiveness of learners and learning programs through the application of modern data collection and analysis tools. As with any new field, it is easy to see the potential benefits of the work being done, and stakeholders are eager to get results. However, expertise is limited, and the work to establish analytic routines difficult and time intensive. There is yet a long ways to go.

Siemens makes an important distinction between learning analytics and educational data mining, arguing that where the former is concerned with understanding patterns and trends in order to improve learning and learning environments, the latter is targeted toward finding unique types of data that occur in an educational setting. Learning analytics can take place at multiple levels, from the level of an individual course or program, to that of an entire organization or university. At each level, the types of information gathered and analyzed are bound to differ.

A brief history of the roots of learning analytics is provided, from early citation analysis techniques in the 1950s through the popular adoption and increased sophistication of e-learning. Siemens also gives a brief overview of the analytics tools in use at the time of writing. The tools available are rapidly evolving, and this has become a highly competitive, if not yet saturated, market space.

One challenge facing learning analytics is the scope of the data to be captured, which can lead to significant challengs in storage, sorting, searching, and the development of analytical algorithms. However, modern tools are making this somewhat easier, enabling organizations to personalize the learning process for learners and more accurately model knowledge domains. Other challenges facing the field of learning analytics include a shortage of workers skilled in all of the areas necessary to design and implement the necessary systems, and the necessary organizational support to engage in such a difficult and time consuming project.

Siemens argues that issues such as data quality and scope, privacy concerns, and ethics represent even bigger challenges. Privacy, in particular, is an issue when you consider the circumstance of students attending online courses across international borders. Even in a simpler case, there are questions as to who actually owns the data? The legal system is still a long ways from addressing this significant issue.

Discussion
This paper appears to be one of several written around the same time covering this topic, though it has been written from a somewhat different perspective than its contemporaries. Neither is there any new information or theory contained in the paper, merely a "round-up" of what is currently understood on the topic of learning analytics.

Although this paper is primarily focused on learning analytics for higher education, it does occasionally address organizational learning. In this regard, there is one rather striking ommission from the paper as regards privacy concerns. That is to say, simply, that the privacy of learner data is only rarely at issue when the learners are employees of the organization. It is almost universally understood that any data created by employees, whether intentionally or incidentally, is owned by the organization and not by the employees themselves. As such, this challenge is far less of an obstacle where corporate learning analytics is concerned.

The challenge of finding appropriate technical skills to implement learning analytics has perhaps been downplayed too much by Siemens. As he points out, "A systemic approach to analytics requires a combination of skills and knowledge that are likely not in the possession of a single individual." This is a very salient point, and in fact the complete set of skills required to implement a full systemic solution is exceedingly rare, not to mention highly sought after.

Conclusion
This paper provides a good summary of the current state of learning analytics research and development, as well as the issues currently facing this burgeoning field. Siemens has also done a good job of describing the entire learning analytics model, which is remarkably complex with many variables and inputs. For individuals who are trying to get a grasp on what learning analytics entails and the current state of the field, this paper is a good starting point.