2. Data Changes the way
of learning
If you have three pet dogs, give them names.
If you have 10,000 head of cattle, dont
bother.
Quantity changes the methods and
approaches that we use to interact with and
make sense of data.
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3. Data is defined by three
elements
1. SpeedThe increasing availability of data in
real time, making it possible to process and act
on it instantaneously
2. ScaleIncrease in computing power: Moores
law (stating that the number of transistors on a
circuit board will double roughly every two years)
continues to hold true.
3. SensorsNew types of data: Social data is
set to be surpassed in the data economy,
though, by data published by physical, real-world
objects like sensors, smart grids and connected
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4. Big Data
datasets whose size is beyond the ability of
typical database software tools to capture,
store, manage and analyze.
a new breed of technologies (e.g., Hadoop),
databases, and techniques (e.g., data-mining
or knowledge discovery in databases) has
been developed.
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5. Learning Analytics
Still a young concept in education, learning analytics
already suffers from term sprawl
learning analytics is the measurement, collection,
analysis and reporting of data about learners and their
contexts, for purposes of understanding and optimizing
learning and the environments in which it occurs. - 1st
International Conference on Learning Analytics and
Knowledge
Analytics marries large data sets, statistical
techniques, and predictive modeling. It could be thought
of as the practice of mining institutional data to produce
actionable intelligence.
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8. Three kinds of learning
analytics
Small scale learning analytics that is just
another name for "awareness tools", "learning
cockpits" and so forth and that have a long
tradition in educational technology
Mid scale analytics who's primary function is to
help improve courses, detect and assist students
with dif?culties
Large scale analytics (also called academic
analytics) whose function is to improve
institutions or even whole educational systems.
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9. Moving beyond the
LMS
LMSs have been adopted as learning analytics tools because the
data captured is structured and reflects the learners interaction within
a system.
distributed networks and physical world interactions present additional
challenges for analytics. For example, most LMS analytics models do
not capture activity by online learners outside of an LMS (i.e., in
Facebook, Twitter, or blogs).
Similarly, most analytics models do not capture or utilize physical-
world data, such as library use, access to learning support, or
academic advising.
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