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Learning analytics, lecture


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Learning analytics, lecture

  2. 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. 3. ANALYSIS  ἀνάλυσις (analusis, "a breaking up‖)  In fact, learning analytics is about ―summing up‖, connecting dots and getting a bigger picture
  4. 4. BIG DATA, ORIGINS  
  5. 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. 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. 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. 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. 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. 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
  11. 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 
  12. 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. 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. 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. 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 . 
  16. 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. 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. 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. 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
  22. 22. BLACKBOARD
  23. 23. ACTIVIT Y STREAMS  Facebook news feed  Moodle news feed  And others
  24. 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. 25. LEARNING ANALY TICS RESOURCES  Several people are included in learning analytics implementation  It‘s not one man only job
  26. 26. ANALY TICS MODEL  
  27. 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. 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 ** five areas of LA/EDM application (.pdf):
  29. 29. IT GIVES US
  30. 30. AND THIS
  31. 31. WHERE-TO MOOCs 1 . Theoretical course by Siemens and the university of Athabasca 33 2. More about methodology, implementation and analysis ―Big Data in Education‖ Teachers College, Columbia university – Brian Baker