1. LEARNING ANALYTICS
TALLINN UNIVERSITY
ELEARNING DAY
10.12.2013
Maka Eradze
PhD student

2. ABOUT ME
 PhD student, 2 nd year
 Thesis: Monitoring and Analysis of Learning Interactions in
Digital Learning Ecosystems
 From Georgia
 Background in humanities, MA/BA in Modern Greek and
Georgian language and literature
 Practical experience in eLearning development, training and
capacity building (Georgia)

3. ANALYSIS
 ἀνάλυσις (analusis, "a breaking up‖)
 In fact, learning analytics is about ―summing up‖, connecting
dots and getting a bigger picture

4. BIG DATA, ORIGINS

 https://www.youtube.com/watch?v=C5VB0E1bWiI

5. BIG DATA - DEFINITION
―datasets whose size is beyond the ability of typical database
software tools to capture, store, manage and analyze.‖
The McKinsey Global Institute

6. INITIAL CONCEPTS
 Online learning without embedded analytics is like a car
without wheels. Embedded analytics turns online learning into
an engine for both scaling access and improving
retention, persistence, and completion
Donald Norris,
president and founder of
Strategic Initiatives, Inc

7. WHERE DOES THE DATA COME FROM
 Digital footprints of interactions mostly within the LMS
 According to Marissa Mayer (CEO, Yahoo, former google
executive) data is today defined by three elements:
 Speed—The increasing availability of data in real time, making
it possible to process and act on it instantaneously
 Scale—Increase in computing power: Moore‘s law (stating that
the number of transistors on a circuit board will double
roughly every two years) continues to hold true.
 Sensors—New types of data: ―Social data is set to be
surpassed in the data economy, though, by data published by
physical, real-world objects like sensors, smart grids and
connected devices‖—that is, the ―Internet of Things. ‖

8. BIG DATA FOR EDUCATION
 A byproduct of the Internet, computers, mobile devices, and enterprise
learning management systems (LMSs) is the transition from ephemeral
to captured, explicit data. Listening to a classroom lecture or reading a
book leaves limited trails. A hallway conver sation essentially vaporizes
as soon as it is concluded. However, ever y click , ever y Tweet or
Facebook status update, ever y social interaction, and ever y page read
online can leave a digital footprint. Additionally, online learning, digital
student records, student cards, sensor s, and mobile devices now
capture rich data trails and activity streams .
 New computer-suppor ted interactive learning methods and tools —
intelligent tutoring systems, simulations, games —have opened up
oppor tunities to collect and analyze student data, to discover patterns
and trends in those data, and to make new discoveries and test
hypotheses about how students learn. Data collected from online
learning systems can be aggregated over large numbers of students
and can contain many variables that data mining algorithms can
explore for model building .*
 * E n h a n c i n g Te a c h i n g a n d L e a r n i n g T h r o u g h E d u c a t i o n a l D a t a M i n i n g a n d L e a r n i n g A n a l y t i c s :
A n I s s u e B r i e f U . S . D e p a r t m e n t o f E d u c a t i o n O f f i c e o f E d u c a t i o n a l Te c h n o l o g y

9. WEB ANALY TICS
 Business intelligence, eCommerce
Examples:
 Amazon
 Netflix
 Basically everybody
Web analytics early examples:
 Web page visits
 countries
 domains where the visit was from
 links that were clicked through.

10. BIG DATA IN OTHER FIELDS
 The move toward using data and evidence to make decisions is
transforming other fields.
 The shif t from clinical practice to evidence -based medicine in health care.
 Reliance on individual physicians basing their treatment decisions on their personal
experience with earlier patient cases .
 Which is about carefully designed data collection that builds up evidence
on which clinical decisions are based.
 Medicine is looking even fur ther toward computati onal modeling by using
analytics to answer the simple question ―who will get sick?‖
 And acting on those predictions to assist individuals in making lifestyle or
health changes.
 Insurance companies also are turning to predictive modeling to determine
high-risk customer s. Ef fective data analysis can produce insight into how
lifestyle choices and per sonal health habits af fect long -term
risks. 4 Business and governments too are jumping on the analytics and
data-driven decision -making trends, in the form of ―business
intelligence. ‖*
*Penetrating the Fog: Analytics in Learning and Education
Phillip D. Long and George Siemens
http://www.educause.edu/ero/article/penetrating-fog-analytics-learning-and-education

11. WHY DO WE NEED LEARNING ANALY TICS
 improve administrative decision -making and organizational resource allocation.
 i d e n t i f y a t - r i s k l e a r n e r s a n d p r ov i d e i n te r v e n t i o n to a s s i s t l e a r n e r s i n a c h i e v i n g s u c c e s s .
B y a n a l y z i n g d i s c u s s i o n m e s s a g e s p o s te d , a s s i g n m e n t s c o m p l e t e d , a n d m e s s a g e s r e a d i n
LMSs, educators can identify students who are at risk of dropping out .
 c r e a te , t h r o u g h t r a n s p a r e n t d a t a a n d a n a l y s i s , a s h a r e d u n d e r s t a n d i n g o f t h e
institution‘s successes and challenges.
 i n n o v a t e a n d t r a n s f o r m t h e c o l l e g e / u n i v e r s i t y s y s te m , a s we l l a s a c a d e m i c m o d e l s a n d
pedagogical approaches.
 a s s i s t i n m a k i n g s e n s e o f c o m p l e x to p i c s t h r o u g h t h e c o m b i n a t i o n o f s o c i a l n e t wo r k s a n d
te c h n i c a l a n d i n fo r m a t i o n n e t wo r k s : t h a t i s , a l g o r i t h m s c a n r e c o g n i z e a n d p r o v i d e i n s i g h t
i n to d a t a a n d a t - r i s k c h a l l e n g e s .
 h e l p l e a d e r s t r a n s i t i o n to h o l i s t i c d e c i s i o n - m a k i n g t h r o u g h a n a l y s e s o f w h a t - i f s c e n a r i o s
a n d e x p e r i m e n t a t i o n to e x p l o r e h ow v a r i o u s e l e m e n t s w i t h i n a c o m p l e x d i s c i p l i n e
( e . g . , r e t a i n i n g s t u d e n t s , r e d u c i n g c o s t s ) c o n n e c t a n d to e x p l o r e t h e i m p a c t o f c h a n g i n g
core elements.
 i n c r e a s e o r g a n i z a t i o n a l p r o d u c t i v i t y a n d e f fe c t i v e n e s s by p r ov i d i n g u p - to - d a t e
i n f o r m a t i o n a n d a l l ow i n g r a p i d r e s p o n s e to c h a l l e n g e s .
 h e l p i n s t i t u t i o n a l l e a d e r s d e te r m i n e t h e h a r d ( e . g . , p a te n t s , r e s e a r c h ) a n d s o f t
( e . g . , r e p u t a t i o n , p r o fi l e , q u a l i t y o f te a c h i n g ) v a l u e g e n e r a t e d by f a c u l t y a c t i v i t y .
 p r o v i d e l e a r n e r s w i t h i n s i g h t i n to t h e i r ow n l e a r n i n g h a b i t s a n d c a n g i v e
r e c o m m e n d a t i o n s fo r i m p r ov e m e n t . A l s o , c o m p a r e ow n s t a n d i n g i n t h e c l a s s

http://www.educause.edu/ero/article/penetrating-fog-analytics-learning-and-education

12. DIMENSIONS OF LEARNING ANALY TICS

G r e l l e r, W. , & D r a c h s l e r, H . ( 2 0 1 2 ) . Tr a n s l a t i n g L e a r n i n g i n t o N u m b e r s : A G e n e r i c F r a m e w o r k
f o r L e a r n i n g A n a l y t i c s . E d u c a t i o n a l Te c h n o l o g y & S o c i e t y, 1 5 ( 3 ) , 4 2 – 5 7.

13. DEFINITIONS
 ―learning analytics is 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. ‖
1 st International Conference on Learning Analytics and
Knowledge

14. EDM AND LA
EDM
 develops methods and applies techniques from
statistics, machine learning, and data mining to analyze data
collected during teaching and learning.
 tests learning theories and informs educational practice.
Learning analytics:
 applies techniques from information
science, sociology, psychology, statistics, machine learning, and
data mining to analyze data collected during education
administration and services, teaching, and learning.
 creates applications that directly influence educational practice.
S o u r c e : U . S . D e p a r t m e n t o f E d u c a t i o n , O f f i c e o f E d u c a t i o n a l Te c h n o l o g y, E n h a n c i n g Te a c h i n g a n d
Learning Through Educational Data Mining and Learning Analytics: An Issue
B r i e f , Wa s h i n g t o n , D . C . , 2 0 1 2 . R e t r i e v e d
f r o m h t t p : / / w w w . e d . g o v / e d b l o g s / t e c h n o l o g y / f i l e s / 2 01 2 / 0 3 / e d m - l a - b r i e f . p d f

15. ACADEMIC ANALY TICS
Academic analytics, in
contrast, is the application of
business
i n te l l ig e n c e i n
education
and emphasizes analytics at
i n s t i t ut i on a l ,
regional, and
i n te r n a t i o n a l l ev e l s .

http://www.educause.edu/ero/article/penetrating-fog-analytics-learning-and-education

16. LEVELS OF LEARNING ANALY TICS
levels of
Learning Analytics
 Macro
-cross-institutional
 Meso
institutional
 micro
Learners, educators
 C o nv e r g e n c e o f L A
Learning Analytics. UNESCO Policy Brief (Buckingham Shum, S., 2012)

17. LIMITATIONS OF LA
 Mainly LMS based, while much of the learning happens
outside of LMS
 It captures only online activities
 Solutions
 We are working on them 
 One part of a solution is Experience API

18. CRITIC AND MEANING
 According to Buckingam Shum, compared to many other
sectors, educational institutions are currently ‗driving blind‘.
And there are two reasons why they should invest in analytics:
 to optimise student success
 enable their own researchers to ask foundational questions about
learning and teaching in the 21st century.
 Wider stand:
 To research learning
 She compares an institution without analytics infrastructure
to a theoretical physicist with no access to CERN, or a
geneticist without genome databases .

19. DASHBOARDS
 Fist kind of analytics are dashboards present in almost every
LMS
 They can be presented in form of
 Graphs
 Tables
 Other forms of visualizations
 Meant for:




Educators
Learners
Administrators
Data analysts

20. GOOGLE ANALY TICS

21. SOCIAL NETWORK ANALYSIS
 http://www.wolframalpha.com/facebook/

22. BLACKBOARD
http://www.blackboard.com/Platforms/Analytics/Products/Blackboard-Analytics-for-Learn.aspx

23. ACTIVIT Y STREAMS
 Facebook news feed
 Moodle news feed
 And others

24. ETHICAL CONSIDERATIONS
 Data is there
Who has access?
 Educators
 Institutions
 Learners if its reporting back to learners via dashboards
 one way of overcoming the ethical implications of learning
analytics is to involve students in the process, make it
transparent and make it a student analytics.
 1 . Anonymization of data sets
 3. Consent forms

25. LEARNING ANALY TICS RESOURCES
 Several people are included in learning analytics
implementation
 It‘s not one man only job

26. ANALY TICS MODEL

 https://www.youtube.com/watch?v=KqETXdq68vY

27. METHODS AND APPLICATIONS
 Course-level: learning trails, social network analysis, discourse
analysis
 Educational data-mining: predictive modeling, clustering, pattern
mining
 Intelligent curriculum: the development of semantically defined
curricular resources
 Adaptive content: adaptive sequence of content based on learner
behavior, recommender systems
 Adaptive learning: the adaptive learning process (social
interactions, learning activity, learner support, not only content )
* S i e m e n s h t t p : / / w w w. e d u c a u s e . e d u / e r o / a r t i c l e / p e n e t r a t i n g - f o g - a n a l y t i c s - l e a r n i n g - a n d education

28. METHODS AND APPLICATION
EDM
Baker and Yasef *





Prediction
Clustering
Relationship mining
Distillation of data for human judgment
Discovery with models
LA
According to Bienkowski, Feng, and Means**





Modeling user knowledge, behavior, and experience
Creating profiles of users
Modeling knowledge domains
Trend analysis
Personalization and adaptation
http://www.educationaldatamining.org/JEDM/images/articles/vol1/issue1/JEDMVol1Issue1_BakerYacef.pdf
** five areas of LA/EDM application (.pdf):

29. IT GIVES US
https://learn.canvas.net/courses/33/wiki/week-3-tools-and-methods

30. AND THIS

31. WHERE-TO
MOOCs
1 . Theoretical course
by Siemens and the university of Athabasca
https://learn.canvas.net/courses/ 33
2. More about methodology, implementation and analysis
―Big Data in Education‖
Teachers College, Columbia university – Brian Baker
https://class.coursera.org/bigdata-edu-001/