This document discusses the use of data and analytics in education. It outlines different types of educational data that can be captured, such as data from learning management systems about time spent on resources and social connections. It states that learning analytics can provide insight into factors influencing learner success and serve as a tool for informed change in education. An example given is using analytics to create personalized, adaptive courses tailored to each individual learner's needs. Major obstacles to the use of data and analytics are privacy concerns and ensuring the accuracy of analytic models.

1. -submitted by
Marilyn 1837039
USE OF DATA & ANALYTICS IN
THE FIELD OF EDUCATION

2. INTRODUCTION
 Schools have always had long amassed data:
tracking grades, attendance, textbook purchases, test
scores, cafeteria meals, and the like. But very little
has actually been done with this information —
whether due to privacy issues or technical capacities
— to enhance students’ learning.
 With the adoption of technology in more schools and
with a push for more open government data, there
clearly have been a lot of opportunities for better data
gathering and analysis in education. But what will that
look like? It’s indeed a question, no doubt, as some
states are turning to things like standardized test
score data in order to gauge teacher effectiveness
and, in turn, retention and promotion.

3. TYPES OF EDUCATIONAL DATA
THAT CAN BE CAPTURED AND
MINDED
 A learning management system like Moodle or
Desire2Learn captures a significant amount of
data, including time spent on a resource,
frequency of posting, number of logins, etc. This
data is fairly similar to what Google Analytics or
Piwik collects regarding website traffic. A new
generation of tools, such as SNAPP, uses this
data to analyze social networks, degrees of
connectivity, and peripheral learners.
 Social networks can provide valuable insight into
how connected learners are to each other and to
the university. Potential models are already being
developed on the web that would translate well to

4. ANALYTICS CAN CHANGE
EDUCATION
 Learning analytics are a foundational tool for
informed change in education.
 Learning analytics will serve this role. Once we
better understand the learning process — the
inputs, the outputs, the factors that contribute to
learner success — then we can start to make
informed decisions that are supported by
evidence.
 Analytics can provide valuable insight into the
factors that influence learners’ success (time on
task, attendance, frequency of logins, position
within a social network, frequency of contact with
faculty members or teachers).

5. EXAMPLE :-
 Learning analytics can provide dramatic, structural
change in education. For example, today, our learning
content is created in advance of the learners taking a
course in the form of curriculum like textbooks. This
process is terribly inefficient. Each learner has
differing levels of knowledge when they start a
course. An intelligent curriculum should adjust and
adapt to the needs of each learner. We don’t need
one course for 30 learners; each learner should have
her own course based on her life experiences,
learning pace, and familiarity with the topic. The
content in the courses that we take should be as
adaptive, flexible, and continually updated. The black
box of education needs to be opened and adapted to
the requirements of each individual learner.

6. MAJOR OBSTACLES FACING DATA
AND ANALYTICS
 Privacy for learners and teachers is a critical
issue.
 Other concerns relate to error-correction in
analytics. If educators rely heavily on analytics,
effort should be devoted to evaluating the
analytics models and understanding in which
contexts those analytics are not valid.
 The complex challenges that schools and
universities face can, at least partially, be
illuminated through analytics applications.