This hands-on workshop will work with learning design tools and with massive open online courses (MOOCs) on the FutureLearn platform to explore how learning design can be used to influence the choice and design of learning analytics. This workshop will be of interest to people who are involved in the design or presentation of online courses, and to those who want to find out more about learning design, learning analytics or MOOCs. Participants will find it helpful to have registered for FutureLearn and explored the platform for a short time in advance of the workshop.
This presentation was given during the EMMA Summer School, that took place in Ischia (Italy) on 4-11 July 2015.
More info on the website: http://project.europeanmoocs.eu/project/get-involved/summer-school/
Follow our MOOCs: http://platform.europeanmoocs.eu/MOOCs
Design and deliver your MOOC with EMMA: http://project.europeanmoocs.eu/project/get-involved/become-an-emma-mooc-provider/
On National Teacher Day, meet the 2024-25 Kenan Fellows
EMMA Summer School - Rebecca Ferguson - Learning design and learning analytics: building the links
1. Learning design and learning
analytics: building the links
EMMA Summer School, Ischia, July 2015
2. Rebecca Ferguson
• The Open University (200,000 learners)
• Informal learning:
• YouTube (28.9 million views)
FutureLearn (1.8 million learners)
OpenLearn (37.9 million visits)
• Making use of big data for
45 years
• Learning analytics research / events
• LACE project – learning analytics
community exchange
2
Lead on MOOC evaluation at The Open University, UK
http://www.laceproject.eu/
3. Workshop overview
09.45 Introduction
Linking learning analytics, learning design and MOOCs
10.15 Questions and answers
10.25 Move to workshop area
10.30 Group work
11.15 Workshop end
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You can view and download the original version of these slides at
http://www.slideshare.net/R3beccaF
5. What are learning analytics?
High-level figures
Brief overviews for internal and external
reports
Academic analytics
Figures on retention and success, for the
institution to assess performance
Learning analytics
Use of big data to provide actionable
intelligence for learners and educators
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6. Educators use analytics to
• Monitor the learning process
• Explore student data
• Identify problems
• Discover patterns
• Find early indicators for success
• Find early indicators for poor marks or drop-out
• Assess usefulness of learning materials
• Increase awareness, reflect and self reflect
• Increase understanding of learning environments
• Intervene, supervise, advise and assist
• Improve teaching, resources and the environment
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Dyckhoff, A L, Lukarov, V, Muslim, A, Chatti, M A, & Schroeder, U. (2013).
Supporting Action Research with Learning Analytics. Paper presented at LAK13.
7. Learners use analytics to
• Monitor their own activities and interactions
• Monitor the learning process
• Compare their activity with that of others
• Increase awareness, reflect and self reflect
• Improve discussion participation
• Improve learning behaviour
• Improve performance
• Become better learners
• Learn!
7
Dyckhoff, A L, Lukarov, V, Muslim, A, Chatti, M A, & Schroeder, U. (2013).
Supporting Action Research with Learning Analytics. Paper presented at LAK13.
8. Analytics example: UK schools
8
• Aligned with
clear aims
• Huge and
sustained effort
• Agreed proxies
for learning
• Clear and
standardised
visualisation
• Driving
behaviour at
every level
BUT
• Stressed, unhappy learners
• Analytics with little value for learners or educators
• Omission of key areas, such as collaboration
9. Analytics example: Course Signals
Developed at Purdue University
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Arnold, K E, & Pistilli, M (2012). Course Signals at Purdue: Using Learning Analytics
To Increase Student Success. Paper presented at LAK12, Vancouver, Canada.
13. Learning design in MOOCs
● Puts the learning journey at the heart of the design process
● Provides a set of tools and information to support a learner-
activity based approach
● Helps to show the costs and performance outcomes of
design decisions
● Enables the sharing of best practice
● Helps MOOC designers to choose and integrate a coherent
range of media, technologies and pedagogies
● Enables a consistent and structured approach
to review and analytics
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Mor, Y, Ferguson, R, & Wasson, B. (2015). Editorial: learning design, teacher inquiry into student learning
and learning analytics: a call for action. British Journal of Educational Technology, 46(2), 221-229.
15. Design template analytics
15
Learning outcome How this is assessed
1. Be able to define an ecosystem.
2. Have joined the iSpot community
and obtained identifications for
animals, plants or fungi.
1. Multiple choice. Week 1, question
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2. Self report.
Analytics
1. How many attempted that question? How many got it right
1st / 2nd / 3rd time? How many followed the link back to resources?
2. Access to iSpot data. Use of MOOC hashtag. Persistence over time.
Ethical implications of tracking off-site.
Short description of course and learning outcomes
16. MOOC planner
• Delivered
• Reflection
• Collaboration
• Conversation
• Networking
• Browsing
• Assessment
16
Blocking out types of learning activity
Conole, Gráinne. (2010). Learning design – making practice explicit.
Paper presented at ConnectEd, Sydney, Australia. http://cloudworks.ac.uk/cloud/view/4001
17. MOOC planner analytics
Delivered Content (reading, watching, listening and observing)
Analytics: amount of content viewed, dwell time
Reflection (thinking, considering and reflecting)
Analytics: returns to the same material, reflection exercises completed,
quality of reflection
Collaboration (constructing, collaborating, defining and engaging)
Analytics: collaboration exercises completed, quality of collaboration
Conversation (debating, arguing, questioning, discussing…)
Analytics: number and length of contributions, quality of discussion
Browsing (exploring, searching, finding and discovering)
Analytics: Number of click-throughs to external links, number of visits,
number of resources
Assessment (answering, presenting, demonstrating, critiquing…)
Analytics: Assessments completed, scores, dwell time on hints,
persistence in answering questions
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18. MOOC map analytics
● How long did you expect learners
to spend on these key elements?
● How long did learners actually
spend on the key elements
● How many missed out these
elements?
● How many jumped ahead to
these elements?
● Which types of element are
consistently (un)popular?
● How many left the MOOC at
these points?
18
The MOOC map identifies key elements of the course
0
100
200
300
400
500
600
Assimilative
InformationHandling
Productive
Experiential
Adaptive
Communicative
Assessment
Organisation
Minutes
19. MOOC journey planner analytics
19
Relationships between tools, resources, activities & narrative
A framework for data collection
20. Analytics to solve problems
Analytics could filter discussions or group learners
20
You have been actively engaged in the
discussions, which is excellent, thank you,
but with more than 23,000 participants it
means that our responses and comments
risk getting lost.
This will be primary school material for
some of you and exactly the opposite for
others. It is just not possible to tailor the
material to each of you […]
Introduction to
Forensic Science:
University of Strathclyde
21. Start with the pedagogy
• How do people learn?
• How can we use data to facilitate that process in our MOOC?
• Which elements are learners struggling with?
• Which sections engage them the most?
• What prompts them to ask questions?
• Are they finding assessment challenging?
• What misconceptions have learners shown?
• Are there any accessibility issues?
• How can analytics be used to obtain desired learning outcomes?
21
22. Learning analytics and design
Learning design – helping to identify useful analytics
● What do learners need to know in order to –
network, collaborate, browse or reflect?
● What do educators need to know to support them?
Learning design – helping to identify gaps in the data
● What data do we need to collect?
Learning design – helping to identify gaps in our toolkit
● Which design elements can we look at easily?
● Which ones still pose problems?
Learning design – helping to frame & focus analytics questions
● What did they learn?… in relation to learning outcomes
● Were they social?... when they were collaborating
● Did they share links?... when encouraged to browse
● Did they return to steps?... when encouraged to reflect
22
Making the links
23. Q & A
Next: if you would like to exlore the relationship
between learning design and learning analytics
further, join us downstairs
in the workshop area
23
24. Learning outcomes
24
Make a note of the learning outcomes
Promotional video for Lancaster University’s FutureLearn MOOC: Soils: Introducing the World Beneath Our Feet
You can access this without registering at futurelearn.com
25. Learning outcomes
By the end of the course, learners should know:
●What soils are made of
●How soils are formed
●The variety of soils around the world
●Why soil is so important
●What life exists within soils
●Threats to soil
●Why we need to protect soil
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FutureLearn MOOC from Lancaster University
26. Exploring, searching, finding
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Screenshot from Lancaster University’s FutureLearn MOOC: Soils: Introducing the World Beneath Our Feet
You can currently register for this course free of charge at futurelearn.com
27. Exploring, searching, finding
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Screenshot from Lancaster University’s FutureLearn MOOC: Soils: Introducing the World Beneath Our Feet
You can currently register for this course free of charge at futurelearn.com
28. What are soils made of?
Delivered Content (reading, watching, listening and observing)
Ask learners to watch a video lecture on soils
Reflection (thinking, considering and reflecting)
Ask ‘What are the key constituents of soils and where do they come
from’, and show learners a video
Collaboration (constructing, collaborating, defining and engaging)
Ask learners to put together what they already know, and come up with a
list of the key constituents of soil
Conversation (debating, arguing, questioning, discussing…)
Ask learners to discuss an audio recording about soil
Browsing (exploring, searching, finding and discovering)
Point learners to some key resources and ask them to develop an initial
list of the constituents of soil
Assessment (answering, presenting, demonstrating, critiquing…)
Ask learner to post a list of five constituents of soil, and then comment on
another learner’s list 28
29. Types of learning behaviour
Delivered Content (reading, watching, listening and observing)
Reflection (thinking, considering and reflecting)
Collaboration (constructing, collaborating, defining and engaging)
Conversation (debating, arguing, questioning, discussing…)
Browsing (exploring, searching, finding and discovering)
Assessment (answering, presenting, demonstrating, critiquing…)
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30. Group activity
15 minutes
https://www.futurelearn.com/
Take each learning outcome of the soil science
MOOC and briefly outline a different type of
learning activity for each one
(delivered, reflection, conversation,
collaboration, browsing or assessment).
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31. Example analytics questions
Watch a video lecture on soils
Is the video engaging learners? How long did they watch? How many
skipped it?
Reflect on key constituents of soils
Are learners engaging in behaviours that indicate they are reflecting? Did
they return to the video? Did they pause the video?
Discuss an audio recording about soil
Does this work as a discusssion activity? How many engaged? How
detailed were the comments? Were they key words in the discussion the
same as key words in the recording?
Develop an initial list of the constituents of soil
What mistakes do I need to correct? What elements did they identify?
Which elements dd they miss out?
Post a list of five constituents of soil, and then comment
What type of general feedback can I provide as a model for this activity in
future? What were common mistakes? What did they generally get right?
31
32. Group activity
15 minutes
https://www.futurelearn.com/
For each of your learning activities,
identify questions you would like answered in order
to improve learning and teaching on the MOOC
What types of data would help you
to answer these questions?
32