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 )
