Learning Analytics

Learning is a product of interaction. Depending on the epistemology underlying the learning design, learners might interact with instructors and tutors, with content and/or with other people.

Many educators expend enormous amounts of effort to designing their learning to maximize the value of those interactions. Regardless of the approach taken, a series of questions consistently arises: How effective is the course? Is it meeting the needs of the students? How can the needs of learners be better supported? What interactions are effective? How can they be further improved?

Traditional approaches to answering these questions have involved student evaluation, the analysis of grades and attrition rates, and instructor perceptions most often gathered at the end of a course. Consequently the evaluation and analysis of learning has suffered from: a limited quantity of data busy students and instructors are willing to share at the end of a course; the limited quality of this self-reported, retrospective data; and a significant delay (normally at least one semester) between the events being reported and the implementation of an intervention. As an increasingly large number of educational resources move online, however, an unprecedented amount of data surrounding these interactions is becoming available. This is particularly true with respect to distance education in which a much higher proportion of interactions are computer-mediated. For example, the amount of time reading content online can be easily captured by an LMS/CMS. When, why and with whom learners are connecting is also logged in discussion forums and social networking sites.

Recently, interest in how this data can be used to improve teaching and learning has also seen unprecedented growth and the emergence of the field of learning analytics. In other fields, analytics tools already enable the statistical evaluation of rich data sources and the identification of patterns within the data.

Learning analytics is an emerging field in which sophisticated analytic tools are used to improve learning and education. It draws from, and is closely tied to, a series of other fields of study including business intelligence, web analytics, academic analytics, educational data mining, and action analytics.

Moreover, learning analytics is focused on building systems able to adjust content, levels of support and other personalized services by capturing, reporting, processing and acting on data on an ongoing basis in a way that minimizes the time delay between the capture and use of data. Thus, in contrast to current evaluation processes which use the results from one semester to inform improvements in the next, learning analytics seeks to combine historical and current user data to predict what services specific users may find useful now.

Thus, the study and advancement of learning analytics involves:

(1) the development of new processes and tools aimed at improving learning and teaching for individual students and instructors, and (2) the integration of these tools and processes into the practice of teaching and learning.

(By-Tanya Elias in Learning Analytics: The Definitions, the Processes, and the Potential )