The move toward using data and evidence to make decisions is transforming other fields. Notable is the shift from clinical practice to evidence-based medicine in health care. The former relies on individual physicians basing their treatment decisions on their personal experience with earlier patient cases.2 The latter is about carefully designed data collection that builds up evidence on which clinical decisions are based. Medicine is looking even further toward computational modeling by using analytics to answer the simple question “who will get sick?” and then acting on those predictions to assist individuals in making lifestyle or health changes.3Insurance companies also are turning to predictive modeling to determine high-risk customers. Effective data analysis can produce insight into how lifestyle choices and personal health habits affect 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.”*
Learning analytics, lecture
PhD student, 2 nd year
Thesis: Monitoring and Analysis of Learning Interactions in
Digital Learning Ecosystems
Background in humanities, MA/BA in Modern Greek and
Georgian language and literature
Practical experience in eLearning development, training and
capacity building (Georgia)
ἀνάλυσις (analusis, "a breaking up‖)
In fact, learning analytics is about ―summing up‖, connecting
dots and getting a bigger picture
BIG DATA, ORIGINS
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
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
president and founder of
Strategic Initiatives, Inc
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. ‖
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
WEB ANALY TICS
Business intelligence, eCommerce
Web analytics early examples:
Web page visits
domains where the visit was from
links that were clicked through.
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
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
*Penetrating the Fog: Analytics in Learning and Education
Phillip D. Long and George Siemens
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
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
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
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.
―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
EDM AND LA
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.
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
ACADEMIC ANALY TICS
Academic analytics, in
contrast, is the application of
i n te l l ig e n c e i n
and emphasizes analytics at
i n s t i t ut i on a l ,
i n te r n a t i o n a l l ev e l s .
LEVELS OF LEARNING ANALY TICS
C o nv e r g e n c e o f L A
Learning Analytics. UNESCO Policy Brief (Buckingham Shum, S., 2012)
LIMITATIONS OF LA
Mainly LMS based, while much of the learning happens
outside of LMS
It captures only online activities
We are working on them
One part of a solution is Experience API
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.
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 .
Fist kind of analytics are dashboards present in almost every
They can be presented in form of
Other forms of visualizations
ACTIVIT Y STREAMS
Facebook news feed
Moodle news feed
Data is there
Who has access?
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
LEARNING ANALY TICS RESOURCES
Several people are included in learning analytics
It‘s not one man only job
ANALY TICS MODEL
METHODS AND APPLICATIONS
Course-level: learning trails, social network analysis, discourse
Educational data-mining: predictive modeling, clustering, pattern
Intelligent curriculum: the development of semantically defined
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
METHODS AND APPLICATION
Baker and Yasef *
Distillation of data for human judgment
Discovery with models
According to Bienkowski, Feng, and Means**
Modeling user knowledge, behavior, and experience
Creating profiles of users
Modeling knowledge domains
Personalization and adaptation
** five areas of LA/EDM application (.pdf):
IT GIVES US
1 . Theoretical course
by Siemens and the university of Athabasca
2. More about methodology, implementation and analysis
―Big Data in Education‖
Teachers College, Columbia university – Brian Baker