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@glynnmark
Learning Analytics
- Using data to make informed
decisions
@glynnmark #heie
Outline
• Introduction
• Examples
• Challenges
• Conclusions & Call to
action
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Introduction
16000 Students
Est 1989
1500+ staff
Multiple campuses
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Teaching Enhancement Unit
TEACHINGENHANCEMENT
UNIT
Onlineand Blended Learning
Support
Awards and Grants
Credit Earning Modules
Professional Development
Workshops
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S.A.M.R.* & Learning Analytics
• Assignment planning
• Quicker action
• Change resources
• Text analysis
*SAMR - http://bit.ly/2oo9maG-
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Course data
Substitution
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Personalised contact
Augmentation
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Modifying course content
Comparing students who watched versus not watched video
one
Comparing Means [ t-test assuming unequal variances (heteroscedastic) ]
Descriptive Statistics
VAR Sample size Mean Variance
Didn't watch 84 51.86905 691.58505
Watched 102 63.15686 576.74743
Two-tailed distribution
p-level 0.00284t Critical Value
(5%) 1.97402
One-tailed distribution
p-level 0.00142t Critical Value
(5%) 1.65387
Modification
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Redefining assessment
Re-define
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Conclusions & Call to action
• Data can be extremely powerful and can help
make informed decisions by all levels across
the institution
• Introduce / expand the use of learning
analytics within your institution
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Questions
• [email protected] for
further information
• Images within the
presentation are from
www.presentermedia.com
and DCU VLE
