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Moodle & Analytics – a matter of
perspective
Mark Drechsler
What do we mean by ‘analytics’?
How can we classify different kinds of analytics?
What are the challenges we face in the LMS?
What is happening in the Moodle project (and
beyond) in relation to analytics?
This session
“…exploring the unique types of data that come from
educational settings, and using those methods to better
understand students.”
A broad definition
Source: Wikipedia entry on Educational Data Mining
However – many different
perspectives
Type Level/object Who benefits?
Learning
Analytics
Course level Learners, Academics
Faculty level Learners, Academics
Academic
Analytics
Institutional Administrators, Funders,
Marketing
Regional Funders, Administrators
National and
international
National governments,
Educational authorities
Multiple perspectives
Source: http://www.educause.edu/ero/article/penetrating-fog-analytics-learning-and-education
Penetrating the Fog: Analytics in Learning and Education – Phillip Long & George Siemens
Type Level/object Who benefits?
Learning
Analytics
Course level Learners, Academics
Faculty level Learners, Academics
Academic
Analytics
Institutional Administrators, Funders,
Marketing
Regional Funders, Administrators
National and
international
National governments,
Educational authorities
Multiple perspectives
Target Consumer
Source: http://www.educause.edu/ero/article/penetrating-fog-analytics-learning-and-education
Penetrating the Fog: Analytics in Learning and Education – Phillip Long & George Siemens
Target
Students?
Subjects?
Faculties?
Universities?
Two dimensional model
Consumer
Students?
Teachers?
Decision makers?
Two dimensional model
Target
Consumer
Scope
Automation
Slightly modified model
One extreme: LMS only data analysis
Simple to implement
Losing ‘market share’ of data over time leading to lower
reliability of analysis
Other extreme: Learning ecosystem
Complex – needs open standards model and external
repository
The holy grail of educational analytics?
Analytics Scope
One extreme: Manual analysis
Run reports, analyse data, take actions
Other extreme: Automated analysis
Automated analysis against known metrics
Alerts & notifications to consumers
Analytics automation
Goal:
Finding out you have a problem
Before you have a problem.
This gives us a framework for
considering perspectives on
analytics
Couple of examples
1. Engagement Analytics
http://docs.moodle.org/22/en/Engagement_Analytics_Plugin
2. Custom reporting
http://docs.moodle.org/23/en/Configurable_reports
Where to from here?
Only one part of the story
Lecture capture? Library systems?
Virtual classroom? Social learning?
Logs in Moodle are good, but not
comprehensive
‘LMS only’ analysis problems
Data gets big(gish)
‘Typical’ Moodle uni generates
between 50,000,000 and
100,000,000 log records in a year
LMS only analysis problems
Moodle analytics roadmap:
‘Foundation building’
Solution part 1: abstraction
Log
store
Adapted from http://docs.moodle.org/dev/Logging_2
Log data
What they are saying:
SOLAR: “Development of a common language for
data exchange*” – first item on their roadmap
IMS: “The … ‘holy grail’ of data interoperability
is an agreed upon “learning/progress map” that
all tools and assessments could populate.**”
Solution part 2 - standards
* source: http://solaresearch.org/OpenLearningAnalytics.pdf
** source: http://www.imsglobal.org/blog/?p=258
Increase amount of data gathered in Moodle’s logs to
support more in-depth analytics.
Solution part 3 – more data!
Analytics roadmap:
http://docs.moodle.org/dev/Road
map
(Look for Logging 2)
Once ‘foundation’ is
stronger, more plugins can be
built more easily for more
purposes
And then…
Plug-in analytics
Log
store
Adapted from http://docs.moodle.org/dev/Logging_2
Log data
In-LMS analytics
Externalanalytics
Earlier effort:
Monash Uni/NetSpot project
implementing ‘engagement
analytics’ plugin in 2012:
http://bit.ly/MoodleAnalytics
This year’s project:
Macquarie Uni, UNSW and
NetSpot initiating a project to
develop student centric analytics
in Moodle
Thanks!
www.netspot.com.au

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