An Overview of Big Data and Learning Analytics in Higher Education
What is Big Data?
Manyika et al. (2011) define big data as data “whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze”
What is Learning Analytics?
(Long et al 2011), defines this as "the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs" (p,34)
With the emergence of online learning environments, such as Massive Open Online Courses (MOOCs), large volumes of data are being generated. Similarly, the widespread use of Learning Management Systems (LMS) such as Moodle and Canvas utilized in Higher Education have caused a massive growth of the data that educational institutions are required to manage. Significant amounts of learner activity take place externally, and so records are distributed across a variety of different sites with different standards, owners and levels of access.
(Ferguson, 2012).
Types of Big Data
Big Data is presented in three forms, click on the gallery below to see more:
This type of data is structured and is well-defined.
This type of data is somewhat structured but not completely.
This typenof data is unorganized and difficult to handle, understand and analyze.
This type of data is structured and is well-defined.
Characteristics of Big Data
Big data is the collection of a data from a variety of sources often categorized by the 3V's, this has grown overtime and address as the primary characteristics of Big Data:
VARIABILITY
The different ways Big Data can be used.
VARACITY
The degree to which big data can be trusted
VALUE
The business value of the data collected
VELOCITY
The speed at which big data is generated
VARIETY
The types of data: (structured, unstructured, semi-structured)
VOLUME
The amaount of data from a variety of sources
Learning Analytics & Academic Analysis
Where do we start?
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Source: Mariville University
Source: Mariville University
In higher education, data focus is expressed using learning analytics.
Academic analytics reflects the role of data analysis at an institutional level, whereas learning analytics centers on the learning process (which includes analyzing the relationship between learner, content, institution, and educator). Educational Data Mining (EDM) sifts large volumes of data to reveal insights on students digital footprints.
Source: Siemens and long 2011
