This document discusses data-centric education and learning. It begins by outlining past and present technologies used in education. It then discusses how data-centric learning is enabled by devices that connect to the cloud and collect real-time student data. This data can provide adaptive instruction, feedback, and insights into learning processes. Examples are given of social network analysis and predictive analytics projects using large educational datasets. Finally, frameworks for designing data-driven learning environments and strategies to improve performance are presented. The conclusion emphasizes using data and analytics responsibly and strategically to improve education.

1. DATA CENTRIC EDUCATION & LEARNING
Seung Won Yoon, Ph.D. hrdswon@gmail.com
Instructional Design & Technology, Western Illinois University

2. Project & Case Examples
Outline
Technology in Education: Past
Now and Future
Design Frameworks
1
2
3
4

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. 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. 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. 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. 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. Social Network Analysis

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. 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. Examples cont’d
Predictive analytics at American Public Universities
 1 mil students, 5 mil courses, 16 schools

12. Examples cont’d
Graduate Committee Network
Shirley, K., & Bradley, K.D., (2009). Examining graduate faculty committee compositions.

13. Examples cont’d
Citation Analysis
Cho, Y. J. and colleagues. (2012). Landscape of educational technology. BJET.

14. Examples cont’d
Leadership and programs
Co-authorship & collaboration
Interdisciplinary research

15. In the Workplace
IBM’s Knowledge creation & sharing
Encouraging strategic collaboration, before & after

16. Design Frameworks
 Learning must take place in the context of work or performing
tasks.

17. Learning & Performance Architecture
Rosenberg, M. (2006). Beyond E-Learning: Approaches and technologies to enhance
organizational knowledge, learning, and performance. New York: Pfeiffer.

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. 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. 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. Interactions & Others
 PBL, PJL, AL – Activity Theory
 4C/ID, Learning environment
 Online interactions
Figure. Activity Theory (Engeström, 2000)
Figure. Online interactions (Hirumi, 2002)

22. Innovation & Adoption (TAM/UTAUT)

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?