lace masterclass learning analytics m&l brussels 2014
DESCRIPTION
This master class covers the latest developments and possibilities of learning analytics and addresses the issue of visualising data for teachers using current examples. This class is organised in the context of the LACE (Learning Analytics Community Exchange) project which brings together existing key European players in the field of learning analytics & Educational Data Mining in order to support development of communities of practice and share emerging best practices.TRANSCRIPT
Learning Analytics and Visualisation of Data
Erwin Bomas
Kennisnet
@ebomas
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Learning Analytics and Visualisation of Data -Overview Masterclass
• What is Learning Analytics (LA)?
– What are current examples of LA?
• What is the role of visualisation?
• Workshop – opportunities for LA:
– What to analyze?
– How to visualise?
• What does LA imply for the role of the teacher?
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EEN PAAR VOORBEELDEN …WHAT IS LEARNING ANALYTICS?
Learning Analytics definitions
• Learning analytics is the measurement, collection, analysis and reporting of data about learners and theircontexts, for purposes of understanding and optimizinglearning and the environments in which it occurs(LAK 2011)
• LA is about collecting traces that learners leave behind and using those traces to improve learning (Duval, LAK 2012)
13http://dougclow.org/2011/02/28/the-learning-analytics-cycle/
Data Generated by LMS Data Generated by Instructor
Number of Times Resource Accessed Grades on Discussion Forum
Date and Time of Access Grades on Assignment
Number of Discussion Posts Generated Grades on Tests
Number of Discussion Posts Read Final Grades
Types of Resource Accessed Number (and Type) of Questions Asked in a Discussion Forum
… Number of Emails Sent to Instructor
…
Source: Dietz-Uhler & Hurn, Journal of Interactive Online Learning (2013)
Examples of data that can be used
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http://edtechreview.in/trends-insights/insights/389-data-mining-and-learning-analytics-improving-education
OPTIMIZING LEARNING BY USING DATA IS APPLICABLE TO THE PRIMARY PROCESS
(LEARNING AND TEACHING) AS WELL AS THE
SECONDARY PROCESS (ORGANIZATION OF
LEARNING)
An
alyt
ics
on
3 le
vels
EEN PAAR VOORBEELDEN …EXAMPLES
Example 1: Math Garden (NL)
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Garden Center - Analytics
Example 2: Khan Academy
Khan student dashboard
Khan teacher dashboard
Example 3:Knewton
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Example 4: PulseOn (NL)
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• 2000 students and 200 teachers, with a vision that each student is unique but with “middle of the road” course materials
• Three-phase approach of implementation of a ”personalizedlearning platform”
• Students, teachers and management are reported to be verypositive and expanding the use of the platform
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Example 5: Learnbeat (NL)
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Example 6: Cum Laude / Magnaview
Summary
• Rapid development – a lot of new initiativesevery year
• Mostly new players – traditional textbookpublishers are not in the forefront
• Using both embedded and extracted analytics
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EEN PAAR VOORBEELDEN …DATA VISUALISATION
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Worldmapper.org
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Worldmapper.org
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Worldmapper.org
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Billion dollar-o-gram (McCandless)
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The River of Myths…
• https://www.youtube.com/watch?v=OwII-dwh-bk
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LA: two approaches (Duval & Verbert, 2012)
• Educational Data Mining
• Information visualization
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Two approaches for Learning Analytics
• Educational Data Mining – big data, business analytics
• Information visualization – Quantified Self
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Two approaches for Learning Analytics
• Educational Data Mining – big data, business analytics
– What data do you have available?
• Information visualization – Quantified Self
– What are the needs of the learner/teacher? How can this be visualized?
– What data is needed?
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EEN PAAR VOORBEELDEN …THE ROLE OF THE TEACHER IN LA
The teacher as a data analist?
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Research issues (Duval & Verbert, 2012)
1. What are meaningful traces?
2. How to visualise? Beware for eye candy
3. Assessing learning impact is hard
4. ‘Become what you measure’ (compare to teaching tothe test)
5. Handling huge data sets
6. Privacy
7. Enslaving instead of empowering
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Work Package 4: Schools – Objectives
• Bring together disparate communities with related interests
• Capture and disseminate the latest thinking on learning analytics in practice
• Analyse significant developments and issues in the domain and produce reports
• Identify, collect and synthesise claims and evidence for the benefits of learning analytics
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• http://www.laceproject.eu/blog/infographic-learning-analytics/• http://dougclow.org/2011/02/28/the-learning-analytics-cycle• http://eleed.campussource.de/archive/8/3336• https://lirias.kuleuven.be/bitstream/123456789/315113/1• http://www.worldmapper.org• http://seealso.org/• http://www.informationisbeautiful.net• http://www.gapminder.org/videos/the-river-of-myths/• http://blog.profitbricks.com/39-data-visualization-tools-for-big-data/• http://www.kennisnet.nl/fileadmin/contentelementen/kennisnet/mbo/Publicaties/Publi
catie_Big_data.pdf (Nederlands)
[email protected]@ebomas
This work was undertaken as part of the LACE Project, supported by the European Commission Seventh Framework Programme, grant 619424.
These slides are provided under the Creative Commons Attribution Licence: http://creativecommons.org/licenses/by/4.0/. Some images used may have different licence terms.
www.laceproject.eu@laceproject
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Main references