Learning analytics aim to enhance student success through real-time data usage by students, instructors, and advisors. It involves developing tools and processes to improve learning and teaching, drawing insights from related fields like business intelligence and web analytics. Currently, learning analytics is an emerging field, and implementation tips include learning from established disciplines and effectively combining various data types.

1. learning analytics

2. what are learning analytics?
related fields of study
processes resources
a model for learning analytics
where are we now?
implementation tips
references literature review

3. learning analytics are:
the ability to “scale the real-time use of learning
analytics by students, instructors and academic
advisors to improve student success”
- Next Generation: Learning Challenges
next page: learning analytics involves
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4. learning analytics involves:
1. the development of new processes and tools
aimed at improving learning and teaching for
individual students and instructors
2. the integration of these tools and processes
into the practice of teaching and learning
next page: related fields of study
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5. related fields of study
business intelligence
web analytics
academic analytics
action analytics
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6. business intelligence:
a well-established process in the business world
whereby decision makers integrate strategic
thinking with information technology to be able
to synthesize “vast amounts of data into
powerful, decision making capabilities”
- Baker, 2007
next page: web analytics
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7. web analytics:
“the collection, analysis and reporting of Web site
usage by visitors and customers of a web site” in
order to “better understand the effectiveness of
online initiatives and other changes to the web site
in an objective, scientific way through
experimentation, testing, and measurement”
- McFadden, 2005
next page: academic analytics
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8. academic analytics:
the application of the principles and tools of
business intelligence to how institutions
gather, analyze, and use data to improve student
success
-Campbell and Oblinger, 2007 &
Goldstein and Katz, 2005
next page: action analytics
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9. action analytics:
involves deploying academic analytics “ to provide
actionable intelligence, service-oriented
architectures, mash-ups of information/content
and services. proven models of course/curriculum
reinvention, and changes in faculty practice that
improve performance and reduce costs
- Norris et al, 2008
next page: learning analytics processes
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10. learning analytics processes
capture
data
gathering
select
refine aggregate
knowledge information
application processing
use predict
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11. data
gathering
select
There are so many metrics that could be
capture tracked, it is essential to define goals and
identify relevant data.
Large store of data already exist What do we want to achieve?
and computer-mediated distance Are we measuring what we should be?
education increasingly creates How can we create innovative metrics?
student data trails.
Most often exists in disjointed and
meaningless forms.
next page: information processing
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12. information
processing predict
aggregate
To be usable, we must be able to Data is useful when it can be used to
aggregate that data into a predict future events.
meaningful form.
To date, however, no guidance it
Dashboards and social network available to educators to indicate which
analysis are two promising tools. captured variables are pedagogically
meaningful.
Outside of education, search engines and recommenders sites are examples of
aggregating information and using it to predict user needs.
next page: information processing
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13. knowledge
application
use
In order to be a knowledge
discovery cycle, data and refine
actions must be re-presented
to users. Otherwise, it is just Analytics are a self-improvement
data mining. project. Monitoring impact must be a
continual effort, the results of which are
used to update the models and improve
predictions.
next page: analytics tools
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14. When institutions work together and
share, duplication is reduced and
improvements are increased.
Sharing data, models and innovations,
therefore, has the potential to improve
learning for everyone.
next page: analytics tools
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15. learning analytics resources
...a single
There are four amalgam of
human and
types tools that machine
must interact processing which
for learning Organizations Computers is instantiated
through an
analytics to be interface that
successful. both drives and is
driven by the
People Theory whole system,
human and
Machine
- Dron and
Anderson, 2009
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16. computers Computers
Organizations
Sophisticated computers already collect People Theory
data.
They also facilitate data processing with
visualization tools because we can process
an incredible amount of information if it is
packaged and presented correctly.
Two promising visualization tools for
learning analytics are dashboards and
social networks maps.
next page: dashboards
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17. dashboards Organizations Computers
People Theory
Meaningful information
can be can be extracted
from CMS/LMS and be
made available to students
and instructors.
next page: social network analysis
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18. social network maps
Organizations Computers
People Theory
Automates the process of
extraction, collation,
evaluation and visualisation
of student network data
into a form quickly usable
by instructors.
next page: theory
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19. theory
Organizations Computers
Computer hardware and Theory
People
software are only useful if they
are based on sound theory.
Social networks maps, for example, are only
useful because of sound research-based theory
that demonstrates we learn better when we
interact with others.
next page: people
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20. people
Organizations Computers
There are still a significant People Theory
aspects of an analytics system
that require human knowledge,
skills and abilities to operate.
Developing effective learning interventions
remains highly dependent on human cognitive
problem-solving and decision-making skills.
next page: organizations
main page more information

21. organizations Organizations Computers
Social networks maps, for People Theory
example, are only useful because
of sound research-based theory
that shows peer networks play an
important role in student
persistence and overall success.
next page: organizations
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22. a model for learning analytics
capture
data
gathering
select
Organizations Computers
People Theory
refine aggregate
knowledge information
application processing
use predict
main page next page: where are we now?

23. where are we now?
Learning analytics is an emerging field.
Analytics is
other fields
is already
well
established.
Tools and lessons learned from other fields can be used to support the
introduction of learning analytics to the majority.
next page: tips for analytics
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24. implementation tips
1. Learn from others disciplines in which analytics
is an established field
2. Find out what you are already measuring
3. Combine web-based data with traditional
evaluation, assessment and demographic data
4. Good communication skills are essential
5. Change is hard for everyone and rarely
welcome - tread lightly and offer support
next page: references
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25. references
Arnold, K. E. (2010). Signals: Applying Academic Analytics, EDUCAUSE Quarterly 33(1).
Retrieved October 1, 2010 from
http://www.educause.edu/EDUCAUSE+Quarterly/EDUCAUSEQuarterlyMagazineVolum/Si
gnalsApplyingAcademicAnalyti/199385
Astin, A. (1993). What Matters in College? Four Critical Years Revisited. San Francisco:
Jossey-Bass.
Baker, B. (2007). A conceptual framework for making knowledge actionable through
capital formation. D.Mgt. dissertation, University of Maryland University College, United
States -- Maryland. Retrieved October 19, 2010, from ABI/INFORM Global.(Publication No.
AAT 3254328).
Dron, J. and Anderson, T. (2009). On the design of collective applications, Proceedings of
the 2009 International Conference on Computational Science and Engineering , Volume
04, pp. 368-374.
Goldstein, P. J. and Katz, R. N. (2005). Academic Analytics: The Uses of Management
Information and Technology in Higher Education, ECAR Research Study Volume 8.
Retrieved October 1, 2010 from http://www.educause.edu/ers0508
next page: references (cont’d)

26. references (continued)
McFadden, C. (2005). Optimizing the Online Business Channel with Web Analytics [blog
post]. Retrieved October 5, 2010 from
http://www.webanalyticsassociation.org/members/blog_view.asp?id=533997&post=8932
8&hhSearchTerms=definition+and+of+and+web+and+analytics
NextGeneration: Learning Challenges (n.d.). Learning Analytics [website]. Retrieved
October 12, 2010 from http://nextgenlearning.com/the-challenges/learning-analytics
Norris, D., Baer, L., Leonard, J., Pugliese, L. and Lefrere, P. (2008). Action Analytics:
Measuring and Improving Performance That Matters in Higher Education, EDUCAUSE
Review 43(1). Retrieved October 1, 2010
from http://www.educause.edu/EDUCAUSE+Review/EDUCAUSEReviewMagazineVolume4
3/ActionAnalyticsMeasuringandImp/162422
Zhang, H. and Almeroth, K. (2010). Moodog: Tracking Student Activity in Online Course
Management Systems. Journal of Interactive Learning Research, 21(3), 407-429.
Chesapeake, VA: AACE. Retrieved October 5, 2010 from http://0-
www.editlib.org.aupac.lib.athabascau.ca/p/32307.