Conferencia eMadrid sobre "El uso de herramientas telemáticas en la facilitación de MOOC: primeras impresiones". Manuel León Urrutia. Universidad de Southampton. 17/11/2016.

1. Data visualisation
tools for teaching in
MOOCs
Manuel León Urrutia
@mleonurr
m.leon-
urrutia@soton.ac.uk
Madrid, 17
November

2. Overview
• Who we are
• What we do, why we do it
• How do we do it
• The impact of what we do

3. Our case
University of Southampton & FutureLearn
• 15 MOOCs
• 50+ runs
• ½+ million learners (Futurelearn 5m+!)
• 200 + datasets
• 6 courses running at the moment
• A MOOC Dashboard developed and in use
• Different internal stakeholders starting to
use it

4. Is Learning Analytics the answer?
Massive Open Online Courses (MOOCs)
• have been long recognised for their
potential to provide insights on how
learning takes place online.
MOOC data analytics
• can lead to the identification of weaknesses
in the learning materials
Quantitative educational data analysis alone
• cannot address learners’ needs
[ Koller, 2012 ]
[ Bates, 2012 ]
[ Daniel, 2012 ]

5. Why do we do it? Data
value chain
[Miller and Mork, 2013 ]

6. Why do we do it? Data
value chain
[Miller and Mork, 2013
FutureLearn

7. Why do we do it? Data
value chain
[Miller and Mork, 2013
UoS

8. Why do we do it? Data value chain

9. Terminology
• Courses
• Runs
• Learning Activities (“steps”)
• Learners vs. Fully-participating Learners
• Retention_step(c, r, s)
• Retention_week(c, r, w)

10. Datasets
Text files in Comma Separated
Values (CSV) format:
• enrolment data
• comments data
• step activity data
• quiz data
• peer review data

11. An Integrated toolset and data infrastructure:
the UoS MOOC Dashboard
MOOC Datasets
in .csv
Converted to SQL
Stored &
Hosted in UoS
Queried & Analysed by MOBS

12. Selecting a run in a course
through the MOOC dashboard

13. Case: Origin country
for all CM learners

14. Course comparison, data aggregation
All learners Learners between 18-25 years old

15. Course progress, daily

16. Use cases
RQ1: How can different roles involved in MOOCs
benefit from the MOOC Dashboard
RQ2: What features are most/least useful
RQ3: How can we improve the dashboard

17. Facilitators preliminary
impressions
Comments viewer
• generally useful for filtering comments
• needs link to comments, back to platform
Demographics dashboard
• Satisfies curiosity
• Unlikely to be useful for interventions
Activity measures
• Useful to target particular steps, and dates or
events
• Not useful for interventions

18. Researhers preliminary
impressions
Comments viewer
• Great potential for text mining
Demographics dashboard
• Useful to know MOOC populations better
Activity measures
• Useful to target particular steps, and dates or
events
• Useful to evaluate course

19. Future work
• Mixed methods study: use of the
dashboard by instructors
• Dashboard enhancement
• Aggregate cross-institution
analysis
• Aggregate cross-platform analysis

20. References
https://www.mendeley.com/groups/2754851/mo
oc-observatory/

21. @mleonurr