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2013 한국데이터사이언스 창립기념 심포지움 발표 - Western Illinois University, 윤승원박사

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  1. 1. DATA CENTRIC EDUCATION & LEARNING Seung Won Yoon, Ph.D. Instructional Design & Technology, Western Illinois University
  2. 2. Project & Case Examples Outline Technology in Education: Past Now and Future Design Frameworks 1 2 3 4
  3. 3. Assumptions  Strategies first, then technologies.  If great people/tool vs. bad culture/system, latter wins.  Most technology integration has failed. Innovation adoption has been tough. Workshop or training to (big) data/analytics will always have minimal impacts.  Learning and performance must be integrated.
  4. 4. Technologies in the Past  Technologies in education  Process: Instructional design, learning strategy, 4C/ID (for complex contents), learning environments, design based research, etc.  Media: radio, TV, CBT, video, PC, multimedia, Web, virtual worlds, … now, analytics & network analysis (paradigm change? Anything new?)  Education: Industry lowest in adopting technologies  People will always focus more on new media  Media vs. methods debate  New technology has never replaced ‘old’
  5. 5. Present & Future  NMC Horizon report  Near: MOOC and Tablet computing  Middle: Learning analytics & gaming  Far: 3D printing & wearable technology  A day made of glass by Amber Case, from Flick’r
  6. 6. What’s New about Data-centric Learning?  Tablet, phone, devices connected to the cloud  Real-time feedback (pacing)  Adaptive contents (individualization)  Instructional precision/effectiveness (previous knowledge)  Analyzed and used to augment teacher/student capabilities Watters. A. (2011, July). How data and analytics will change education.
  7. 7. Data-centric Education & Learning  LMS: grade, log, forum postings  Student/institutional records  Web 2.0/3.0 – digital trails  Top 100 learning tools – more data  Data sources (in addition to social media):  PSLC’s learning datasets  Carnegie Melon University  Stanford’s multimodal learning analytics  Stanford’s large network data collection  Society for Learning Analytics Research datasets
  8. 8. Social Network Analysis
  9. 9. Building Blocks  Nodes, edges, density, centrality, community, motif  Data: Affiliation matrix, edge list  Sources:  Social media  Email/listserv/forum  RDBM  Open communities  Tools  NodeXL, Gephi…
  10. 10. Project & Case Examples Use of SNAPP (Moodle, BlackBoard, D2L, Angel)  At risk students/non-participants  Identify communities  Identify brokers  Before and after new strategy (e.g., each post vs. responding to instructor prompt)  LATF at U of Michigan: Cross-course analysis - Time on tasks, frequency of contacts, network position, resource use, instructor reuse of contents, learning-reflecting assessments, contextual resources (privacy, security, governance) Bakharia, A., & Dawson, S. (2011). SNAPP: A bird’s eye view of temporal participant interaction
  11. 11. Examples cont’d Predictive analytics at American Public Universities  1 mil students, 5 mil courses, 16 schools
  12. 12. Examples cont’d Graduate Committee Network Shirley, K., & Bradley, K.D., (2009). Examining graduate faculty committee compositions.
  13. 13. Examples cont’d Citation Analysis Cho, Y. J. and colleagues. (2012). Landscape of educational technology. BJET.
  14. 14. Examples cont’d Leadership and programs Co-authorship & collaboration Interdisciplinary research
  15. 15. In the Workplace IBM’s Knowledge creation & sharing Encouraging strategic collaboration, before & after
  16. 16. Design Frameworks  Learning must take place in the context of work or performing tasks.
  17. 17. Learning & Performance Architecture Rosenberg, M. (2006). Beyond E-Learning: Approaches and technologies to enhance organizational knowledge, learning, and performance. New York: Pfeiffer.
  18. 18. Strategic Blending Yoon, S. W., & Lim, D. H. (2007). Strategic blending: A conceptual framework to improve learning and performance. International Journal on E-Le Yoon, S. W., & Lim, D. H. (2007). Strategic blending: A conceptual framework to improve learning and performance. International Journal on E-Learning, 6(3), 475-489. Yoon, S. W., & Lim, D. H. (2010). Virtual learning and technologies for managing organizational competency and talents. Advances in Developing Human Resources, 12(6), 715-728.
  19. 19. HPT Model Van Tiem, D. M., Moseley, J. L. & Dessinger, J. C. (2012). Fundamentals of Performance Improvement: A guide to optimizing results through people, process, and organizations
  20. 20. Learning & Knowledge at Macro Level Figure 1. Esterby-Smith’s (2003) Key Topics of Learning in Organizations Song J.H., Uhm, D., Yoon, S. W. (2011). Organizational knowledge creation practice., LODJ.
  21. 21. Interactions & Others  PBL, PJL, AL – Activity Theory  4C/ID, Learning environment  Online interactions Figure. Activity Theory (Engeström, 2000) Figure. Online interactions (Hirumi, 2002)
  22. 22. Innovation & Adoption (TAM/UTAUT)
  23. 23. Conclusions  It’s not about if (big) data/analytics is different; it’s about doing the right thing & doing things right  Must be treated as the same as business intelligence  Tools & frameworks are here, are you ready?