Maka EradzePhD student

LEARNING ANALYTICSTALLINN UNIVERSITY

ELEARNING DAY10.12.2013

PhD student, 2nd 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)

ABOUT ME

 ἀνάλυσις (analusis, "a breaking up”)

In fact, learning analytics is about “summing up”, connecting dots and getting a bigger picture

ANALYSIS

https://www.youtube.com/watch?v=C5VB0E1bWiI

BIG DATA, ORIGINS

“datasets whose size is beyond the ability of typical database software tools to capture, store, manage and analyze.”

The McKinsey Global Institute

BIG DATA - DEFINITION

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

INITIAL CONCEPTS

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.”

WHERE DOES THE DATA COME FROM

A byproduct of the Internet, computers, mobile devices, and enterprise learning management systems (LMSs) is the transit ion from ephemeral to captured, explicit data. Listening to a classroom lecture or reading a book leaves l imited trai ls. A hallway conversation essential ly vaporizes as soon as it is concluded. However, every cl ick, every Tweet or Facebook status update, every social interaction, and every page read online can leave a digital footprint. Additionally, online learning, digital student records, student cards, sensors, and mobile devices now capture rich data trai ls and activity streams.

New computer-supported interactive learning methods and tools—intel l igent tutoring systems, simulations, games—have opened up opportunit ies to col lect 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 col lected 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.*

*Enhanc ing Teach ing and Learn ing Th rough Educat i ona l Data M in ing and Learn ing Ana ly t i c s : An I ssue Br i e f U .S . Depar tment o f Educat i on  Offi ce o f Educat i ona l Techno logy

BIG DATA FOR EDUCATION

Business intelligence, eCommerce

Examples:AmazonNetfl ix Basically everybody

Web analytics early examples: Web page visitscountries domains where the visit was from links that were clicked through. 

WEB ANALYTICS

The move toward using data and evidence to make decisions is transforming other fi elds.

The shift from cl inical 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 careful ly designed data col lect ion that bui lds up evidence

on which cl inical decisions are based. Medicine is looking even further toward computational model ing by using

analyt ics to answer the simple question “who wi l l get sick?” And acting on those predict ions to assist individuals in making l i festyle

or health changes. Insurance companies also are turning to predict ive model ing to determine

high-r isk customers. Eff ective data analysis can produce insight into how l i festyle choices and personal health habits aff ect long-term risks. 4  Business and governments too are jumping on the analyt ics and data-driven decision-making trends, in the form of “business intel l igence.”*

* Pe n e t r a t i n g t h e Fo g : A n a l y t i c s i n Le a rn i n g a n d E d u c a t i o n P h i l l i p D . Lo n g  a n d   G e o rg e S i e m e n sh t t p : / / w w w. e d u c a u s e . e d u / e ro / 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 rn i n g - a n d - e d u c a t i o n

BIG DATA IN OTHER FIELDS

improve adminis t rat ive dec is ion-making and organizat ional resource a l locat ion. ident i fy at - r i sk learners and prov ide intervent ion to ass is t learners in achiev ing

success . By analyz ing d iscuss ion messages posted, ass ignments completed, and messages read in LMSs , educators can ident i fy s tudents who are at r i sk of dropp ing out .

create , through t ransparent data and analys is , a shared understand ing of the inst i tut ion’s successes and chal lenges .

innovate and t rans form the co l lege/univers i ty system, as wel l as academic models and pedagogica l approaches .

ass is t in making sense of complex top ics through the combinat ion of soc ia l networks and technica l and in format ion networks : that i s , a lgor i thms can recognize and provide ins ight into data and at - r i sk chal lenges .

help leaders t rans i t ion to ho l i s t ic dec is ion-making through analyses of what - i f scenar ios and exper imentat ion to exp lore how var ious e lements wi th in a complex d isc ip l ine (e .g . , re ta in ing s tudents , reducing costs ) connect and to exp lore the impact of chang ing core e lements .

increase organizat ional product iv i ty and eff ect iveness by prov id ing up- to -date in format ion and a l lowing rap id response to chal lenges .

help ins t i tut ional leaders determine the hard (e .g . , patents , research) and soft (e .g . , reputat ion, profi le , qual i ty of teaching) va lue generated by facul ty act iv i ty.

provide learners wi th ins ight into the i r own learn ing hab i ts and can g ive recommendat ions for improvement . A lso, compare own s tand ing in the c lass

h t t p : / / w w w.e d u c a u s e . e d u / e ro / 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 rn i n g - a n d - e d u c a t i o n

WHY DO WE NEED LEARNING ANALYTICS

DIMENSIONS OF LEARNING ANALYTICS

Gre l ler , W. , & Drachsler , H. (2012) . Translat ing Learning into Numbers: A Gener ic Framework for Learning Analyt ics . Educat ional Technology & Society, 15 (3) , 42–57.

“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.”

1st International Conference on Learning Analytics and Knowledge

DEFINITIONS

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 infl uence educational practice.

Source : U .S . Depar tment o f Educa t ion , Offi ce o f Educa t iona l Techno logy , Enhanc ing Teach ing and Lea rn ing Th rough Educa t iona l Da ta M in ing and Learn ing Ana ly t i cs : An I s sue B r ie f , Wash ing ton , D .C . , 2012 . Re t r i eved f rom ht tp: / /www.ed .gov /edb logs / techno logy /fi les /2012 /03 /edm- la -b r ie f.pd f

EDM AND LA

Academic analyt ics , in contrast , is the appl icat ion of

business intel l igence in

educat ion and emphasizes analyt ics at

inst i tut ional , regional , and

internat ional levels .  

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 - e d u c a t i o n

ACADEMIC ANALYTICS

leve ls ofLearning Analyt ics

Macro -cross-institutional Mesoinstitutional micro Learners, educators

Convergence of LA

L e a r n i n g A n a l y t i c s . U N E S C O P o l i c y B r i e f   ( B u c k i n g h a m S h u m , S . , 2 0 1 2 )

LEVELS OF LEARNING ANALYTICS

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

LIMITATIONS OF LA

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.

CRITIC AND MEANING

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

DASHBOARDS

GOOGLE ANALYTICS

http://www.wolframalpha.com/facebook/

SOCIAL NETWORK ANALYSIS

BLACKBOARD

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

Facebook news feedMoodle news feedAnd others

ACTIVITY STREAMS

Data is there

Who has access?Educators InstitutionsLearners 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 sets3. Consent forms

ETHICAL CONSIDERATIONS

Several people are included in learning analytics implementation

It’s not one man only job

LEARNING ANALYTICS RESOURCES

https://www.youtube.com/watch?v=KqETXdq68vY

ANALYTICS MODEL

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)

*Siemens http:/ /www.educause.edu/ero/art ic le/penetrat ing-fog-analyt ics-learning-and-educat ion

METHODS AND APPLICATIONS

EDMBaker and Yase f *

Pred ic t ion Clus te r ing Re la t ionsh ip m in ing Dis t i l l a t i on o f da ta fo r human judgment Discovery w i th mode l s

L AAccord ing to B ienkowsk i , Feng , and Means**

Mode l ing user know ledge , behav io r , and exper ience Crea t ing profi les o f use rs Mode l ing know ledge doma ins Trend ana lys i s Persona l i za t i on and adapta t ion

h t t p : / / w w w. e d u c a t i o n a l d a t a m i n i n g . o rg / J E D M / i m a g e s / a r t i c l e s / v o l 1 / i s s u e 1 / J E D M Vo l 1 I s s u e 1 _ B a ke r Ya c e f. p d f

* *  fi v e a re a s o f L A / E D M a p p l i c a t i o n  ( . p d f ) :

METHODS AND APPLICATION

IT GIVES US

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

AND THIS

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/

WHERE-TO