Presentation to the first EDSA Advisory Board meeting in October 2015.

1. MOOCs & Learning Analytics
John Domingue & Alexander Mikroyannidis
Knowledge Media Institute
The Open University, UK

2. Part I: MOOCs

3. A revolution in education

4. Characteristics of MOOCs
• Focus is on social media and peer
support / assessment
• Heavy use of multimedia and online
educational tools
• Large numbers of registered
learners, especially in MOOCs
offered by renowned universities
• Low completion rates
• Certificates are awarded on
completion of a MOOC based on
certain criteria, some of which are
free of charge and others that incur
a fee and may require identity
verification.

5. PAGE ‹#›
Over
1,800,000
FutureLearn sign-ups
Over
2,200,000
course sign-ups
PROGRESS SINCE LAUNCH
In just over a year, FutureLearn has built a large userbase

6. FutureLearn student types

7. Open Educational Resources
Open Educational Resources can be described as:
“teaching, learning and research resources that reside in
the public domain or have been released under an
intellectual property license that permits their free use or
repurposing by others depending on which Creative
Commons license is used” (Atkins et al., 2007)

8. iTunes U

9. OU iTunes U Stats
• Open University on iTunes U was launched on
3rd June 2008
• Now 58 iTunes U Courses
• 68,138,000 downloads
• Over 9,015,700 visitors downloaded files
• Currently averaging 87,500 downloads a week
• 449 collections containing 3,485 tracks (1,638
audio, 1,847 video)
• 423 OpenLearn study units as eBooks (ePub),
representing over 5,000 hours of study
• Currently delivering an average of 0.3 TB of
data a week

10. EDSA MOOC: Process Mining
• The EDSA MOOC “Process Mining: Data science in Action”
explains the key analysis techniques in process mining. The
course provides easy-to-use software, real-life data sets,
and practical skills to directly apply the theory in a variety
of application domains.
• Available on Coursera:
https://www.coursera.org/course/procmin
• Over 42,000 registered students on its first run

11. Planned EDSA courses
the Data Science community.
Table 3: EDSA Core Curriculum Schedule
Topic Schedule
Foundations of Data Science M6
Foundations of Big Data M6
Big Data Architecture M6
Distributed Computing M6
Machine Learning, Data Mining and Basic Analytics M6
Process Mining M6
Statistical / Mathematical Foundations M18
D2.1 Data Science Curricula 1 Page 11 of 37
Data Management and Curation M18
Big Data Analytics M18
Data Visualisation M18
Finding Stories in Open Data M18
Programming / Computational Thinking (R and Python) M30
Stream Processing M30
Visual Analytics M30
Data Exploitation including data markets and licensing M30

12. Part II: Learning Analytics

13. What is Learning Analytics?
“The measurement, collection, analysis and reporting of
data about learners and their contexts, for purposes of
understanding and optimizing learning and the
environments in which it occurs” (1st International
Conference on Learning Analytics and Knowledge – LAK
2011 https://tekri.athabascau.ca/analytics/).
A “bricolage field, incorporating
methods and techniques from a
broad range of feeder fields:
social network analysis (SNA),
machine learning, statistics,
intelligent tutors, learning
sciences, and others”
(Siemens 2014).

14. Methods
Content analysis – particularly of resources which
students create, such as essays.
Discourse Analytics – aims to capture meaningful data on
student interactions, e.g. by exploring the properties of the
language used.
Social Learning Analytics (Buckingham Shum et al.
2012b) – aimed at exploring the role of social interactions
in learning, the importance of learning networks, discourse
used for sensemaking, etc.
Disposition Analytics (Brown 2012; Buckingham Shum et
al. 2012a) – seeks to capture data regarding student's
dispositions to their own learning, and the relationship of
these to their learning. For example, “curious” learners may
be more inclined to ask questions.

15. Applications (Powell & MacNeill 2012)
• Individual learners using analytics to reflect on their
achievements and patterns of behavior in relation to their
peers.
• Identification of students who may require extra support
and attention.
• Helping teachers and support staff to plan supporting
interventions with individuals and groups.
• Enabling functional groups, such as course teams, to
improve current courses or develop new curriculum
offerings.
• Providing information to help institutional administrators
to take decisions on matters such as marketing and
recruitment or efficiency and effectiveness measures.

16. EDSA approach
• With Learning Analytics it will be possible to obtain
valuable information about how the students interact with
the EDSA courseware, in addition to their own judgments
provided via questionnaires.
• Our approach is based on tracking learner activities,
which consist of interactions between a subject
(learner), an object (learning activity) and is bounded
with a verb (action performed).
• We use the Tin Can API (xAPI)
for expressing learner activities
and the Learning Locker
for storing and visualising them.

17. OU Analyse
• Using machine-learning based methods for early
identification of students at risk of failing.
• The overall objective is to significantly improve the
retention of OU students.
• Approach:
– Demographic and VLE (Moodle) data
– Four predictive models

18. OU Analyse demo http://analyse.kmi.open.ac.uk

19. Example: video lecture micro
activity

20. References
• Atkins, D. E., Brown, J. S. & Hammond, A. L. (2007) A Review of the Open Educational
Resources (OER) Movement: Achievements, Challenges, and New Opportunities. The
William and Flora Hewlett Foundation.
• Brown, M., (2012). Learning Analytics: Moving from Concept to Practice. EDUCAUSE
Learning Initiative Briefing. http://www.educause.edu/library/resources/learning-analytics-
moving-concept-practice
• Buckingham Shum, S. and Deakin Crick, R. (2012a). Learning Dispositions and
Transferable Competencies: Pedagogy, Modelling and Learning Analytics. Proceedings of
the 2nd International Conference on Learning Analytics & Knowledge, Vancouver, 29 Apr-2
May 2012. ACM: New York. pp.92-101. Retrieved from: http://oro.open.ac.uk/32823
• Buckingham Shum, S. and Ferguson, R. (2012b). Social Learning Analytics. Educational
Technology & Society Special Issue on Learning & Knowledge Analytics, Eds. G. Siemens
& D. Gašević, 15, 3,, 3-26. Retrieved from: http://oro.open.ac.uk/34092
• Powell, S., & MacNeill, S. (2012). Institutional readiness for analytics. Cetis Analytics
Series, 1(8). Retrieved from http://publications.cetis.ac.uk/2012/527
• Siemens, G. (2014). Supporting and Promoting Learning Analytics Research. Inaugural
Issue of the Journal of Learning Analytics. Journal of Learning Analytics 1(1), 1–2.
• Swan, M. (2013). The quantified self: Fundamental disruption in big data science and
biological discovery. Big Data, 1(2), 85-99.