Slide deck from IV Jornadas Innovación Psicología. 15th Dec 2017. Madrid

1. Learning analytics
IV JORNADA DE EXCELENCIA E INNOVACIÓN EN
PSICOLOGÍA
BIG DATA EN PSICOLOGÍA: APLICACIONES EN ENTORNOS CLÍNICOS, EDUCATIVOS Y EN
RECURSOS HUMANOS
PABLO A HAYA

3. Investigación + Desarrollo + innovación en Ingeniería del Conocimiento
Entidad privada
sin ánimo de lucro
creada en 1989
Productos y
servicios únicos y
adaptables
Nos autofinanciamos
con nuestros
productos y servicios
Reinvertimos en
I+D+i
Quiénes somos
N U E S T R O S S O C I O S

4. Definition
Learning Analytics is the measurement,
collection, analysis, and reporting of data
about learners and their contexts, for the
purpose of understanding and optimizing
learning and the environments in which it
occurs.
(LAK’11)

5. Analytics everywhere
Web analytics
Human Resource analytics
Business analytics
Social media analytics
Marketing analytics
Sports analytics
Text analytics
…

6. Definition
XXX Analytics is the measurement,
collection, analysis, and reporting of data
about XXX and their contexts, for the
purpose of understanding and optimizing
XXX and the environments in which it
occurs.
(LAK’11)

7. Nihil sub sole novum

8. Drivers
● Data informing teaching and learning are
increasingly extensive and accessible
● Innovative new analytic approaches to
digesting, visualizing, and acting on these
data emerge every day

9. Growth of MOOCs

10. MOOC Popularity

11. MOOC providers by registered users
● Coursera – 23 million
● edX – 10 million
● XuetangX – 6 million
● FutureLearn – 5.3 million
● Udacity – 4 million

12. LMS/VLE Popularity
https://elearninginfographics.com/top-lms-stats-facts-2015-infographic-need-know/

13. Who benefits?
Siemens, G., & Long, P. (2011)
Micro-level
(students,
Instructors)
Macro-level
(Policy-makers)
Meso-level
(Dpt. leader,
Administrators)

14. Micro-level benefits
● Identify at-risk learners and provide
interventions.
● Provide learners with insight into
their own learning habits and give
recommendations for improvement.
Shum, S. B. (2012)

15. Meso-level benefits
● Improve administrative decision-making and organizational resource
allocation.
● More transparent data and analysis could create a shared
understanding of the institution’s successes and challenges.
● Increase organizational productivity by providing up-to-date
information and allowing rapid response to challenges.
● Help leaders determine the hard (e.g. patents, research) and soft
(e.g. reputation, profile, quality of teaching) value generated by
faculty activity
Shum, S. B. (2012)

16. Macro-level benefits
● Ultimately the above might transform the
college/university system, as well as academic models
and pedagogical approaches.
Shum, S. B. (2012)

17. Who Does What in a Massive Open
Online Course?
6.002x in spring 2012
154,000 registrants
46,000 never accessed the course
(~30%)
76% of participants were browsers
7% of participants earned certificate
230 million interactions
(Seaton, D. T., et al 2014)

18. Percentage use of course components

19. How video production affects student
engagement: an empirical study of
MOOC videos
● 4 edX courses, Fall 2012: CS1, A.I., Stat, Chem
● 862 videos, 128k students, 6.9 million sessions
● Engagement metric: video watching session length
● Interviews with edX video producers & program
managers
(Guo P.J., Kim J., & Rubin, R., 2014)

20. Recording styles
a) a recorded classroom lecture,
b) an instructor’s talking head,
c) a Khan-style digital tablet
drawing (popularized by Khan
Academy),
d) a PowerPoint slideshow.
(Guo P.J., Kim J., & Rubin, R., 2014)

21. Which kind of videos lead to most
engagement at a reasonable cost?
1. Short: Shorter videos much more engaging.
Engagement drops sharply after 6 minutes.
2. Pre-planned: Pre-production improves engagement
3. Talking head: Talking head is more engaging than
showing only slides
4. Personal: Informal shots can beat expensive studios
5. Khan-style: Khan-style tutorials beat slides/code
(Guo P.J., Kim J., & Rubin, R., 2014)

22. Social learning analytics: five
approaches
● social network analytics: interpersonal relationships define social platforms
● content analytics: user- generated content is one of the defining
characteristics of social learning platform
● discourse analytics: language is a primary tool for knowledge negotiation
and construction
● disposition analytics: intrinsic motivation to learn is a defining feature of
online social media and lies at the heart of engaged learning, and
innovation
● context analytics: mobile computing is transforming access to both people
and content
(Ferguson, R., & Shum, S. B. ,2012).

23. Example: video-based learning
http://clipit.es

24. Social network analytics
(Castellanos, J., Haya, P.A., Urquiza-Fuentes, J, 2017)(Haya, P.A., et al., 2015)

25. New career opportunities in psychology
Strengths:
Analytical skills
Know the right questions
Make decisions
Weaknesses:
Big Data: Volume, Velocity, Varity
Machine learning models
Opportunities:
Cloud services
Black-box approaches
R/Python programming languages
Data science process flowchart from "Doing Data Science", Cathy O'Neil and Rachel Schutt, 2013

26. Resources and
references

27. Open University Learning Analytics
dataset
● The dataset contains the information about 22 courses, 32,593 students,
their assessment results, and logs of their interactions with the VLE
represented by daily summaries of student clicks (10,655,280 entries)
● https://analyse.kmi.open.ac.uk/open_dataset
Other datasets:
Stamper, J., Niculescu-Mizil, A., Ritter, S., Gordon, G. J. &
Koedinger, K. R. Algebra I 2008-2009. Challenge data set from
KDD Cup 2010 Educational Data Mining Challenge (2010).
Cao, L. & Zhang, C. KDD Cup 2015—Predicting dropouts in
MOOC (2015)

28. Research Community
● The Society of Learning Analytics Research (SoLAR).
○ LAK conferences
○ Journal of Learning Analytics
○ Learning Analytics Summer Institutes
● International Educational Data Mining Society
○ Journal of Educational Data Mining
○ Educational Data Mining (EDM) Conference
● International Artificial Intelligence in Education Society
○ International Journal of Artificial Intelligence in Education (IJAIED)
○ Artificial Intelligence in Education (AIED) Conference
● Learning at Scale conference
● The International Society of the Learning Sciences
○ Journal of Learning Sciences

29. References
● (LAK11) 1st International Conference on Learning Analytics and Knowledge, Banff, Alberta, February 27–
March 1, 201 https://tekri.athabascau.ca/analytics/
● Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE review,
46(5), 30.
● Shum, S. B. (2012) UNESCO Policy Brief: Learning Analytics, UNESCO,
http://www.iite.unesco.org/publications/3214711/
● Seaton, D. T., Bergner, Y., Chuang, I., Mitros, P., & Pritchard, D. E. (2014). Who does what in a massive open
online course?. Communications of the ACM, 57(4), 58-65.
● Philip J. Guo, Juho Kim, and Rob Rubin. 2014. How video production affects student engagement: an
empirical study of MOOC videos. In Proceedings of the first ACM conference on Learning @ scale conference
(L@S '14). ACM, New York, NY, USA, 41-50. DOI=http://dx.doi.org/10.1145/2556325.2566239
● Ferguson, R., & Shum, S. B. (2012, April). Social learning analytics: five approaches. In Proceedings of the
2nd international conference on learning analytics and knowledge (pp. 23-33). ACM.
● Castellanos, J., Haya, P.A., Urquiza-Fuentes, J. IEEE Transactions on Learning Technologies, 10(3), July-Sept. 1
2017, pp.306-317, DOI: 10.1109/TLT.2016.2582164
● Haya, P. A., Daems, O., Malzahn, N., Castellanos, J., Hoppe, H. U. (2015) Analysing content and patterns of
interaction for improving the learning design of networked learning environments. British Journal of
Educational Technology, 46 (2), 300–316
● Papamitsiou, Z. & Economides, A. A. Learning Analytics and Educational Data Mining in Practice: A
Systematic Literature Review of Empirical Evidence. Educational Technology & Society 17, 49–64 (2014).