Learning analytics, lecture
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Maka Eradze presents on learning analytics at Tallinn University. She is a PhD student from Georgia studying the monitoring and analysis of learning interactions in digital learning ecosystems. Her presentation covers the origins and definitions of big data and learning analytics. It discusses where data comes from, including digital footprints within learning management systems. The presentation also outlines some of the key reasons for using learning analytics, including improving student success and enabling research into learning and teaching. Finally, it discusses some of the methods and applications of learning analytics, including educational data mining and adaptive learning.
Learning analytics, lecture
- 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
- 21. SOCIAL NETWORK ANALYSIS http://www.wolframalpha.com/facebook/
- 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):
- 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/
Editor's Notes
- #11: 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.”*